bluuebunny/crossref_metadata_2025 huggingface dataset:
向印尼汉语学习者的中国酒文化教学: 跨文化教学策略
Authors: Markali Alpin Gadman , Kurniawan Budi , Halim Steffi Thanissa
Published: October 2024

This paper explores cross-cultural teaching strategies for teaching Chinese wine culture to Indonesian Chinese language learners. It begins by providing necessary background information by summarizes the historical origins and rich meanings of Chinese wine culture, as well as its differences from Indonesian wine culture. Subsequently, the paper proposes various cross-cultural teaching strategies, including using authentic materials and situational teaching, combining language teaching with cultural teaching, promoting cross-cultural understanding and communication, and enhancing learners' cultural sensitivity. These strategies aim to help students better understand and appreciate Chinese wine culture and promote cultural exchange and understanding between China and Indonesia. Finally, the paper emphasizes the importance of cross-cultural teaching strategies in teaching Chinese wine culture to Indonesian Chinese language learners and proposes some directions for future research, such as exploring more deeply the relationship between drinking habits and cultural concepts under different cultural backgrounds, and developing more targeted teaching resources and course designs. These explorations will help further improve the effectiveness of cross-cultural teaching and promote cultural exchange and cooperation between China and Indonesia.
Feminicidio y violencia sistémica en 2666 de Roberto Bolaño
Authors: Cabrera García Elisa
Published: October 2024

“La parte de los crímenes” compone la cuarta parte de la novela póstuma de Roberto Bolaño. En ella se describen las diversas formas de vio- lencia ejercida contra el cuerpo de 109 mujeres asesinadas en una ciudad cticia de la fronte- ra entre México y Estados Unidos.
Hybrid Convolutional and Gated Recurrent Unit Network with Attention for Drilling Kick Prediction
Authors: Qiao Ying , Tu Xiaoyue , Zhou Liangzhi , Guo Xiao
Published: October 2024

Drilling safety is a primary issue in the oil drilling process. Kick is one of the most serious accidents in abnormal drilling accidents. If it is not discovered and addressed in time, it may cause a blowout or even a bigger safety accident. Therefore, predicting the occurrence of kicks in advance is very important to avoid more serious accidents. This research introduces a prediction method for kicks using a combination of convolutional neural networks (CNNs) and gated recurrent units (GRUs), along with an attention mechanism, to assess the likelihood of a kick happening downhole in advance. The method uses CNN layers to extract features from drilling data and reduce the dimensionality of these features. It models drilling time series data using GRUs. The output vector from the GRU is weighted by an attention mechanism to focus on more significant features. Finally, the predictions of kicks are derived through data analysis. The results demonstrate that the method can predict the kick 20 minutes in advance with an accuracy of 98.64%. These results will prove to be significant for improving the prediction level of drilling kicks.
Leaky seams of paper seed-, food-, and feedbags invite attack by stored product pests
Authors: Adler Cornel
Published: October 2024

Larger quantities of plant seeds, food and animal feed products are often packed into multi-layer paper-bags. When the bags are sewn at the top, the seams pose a threat concerning stored product protection: Where the needle punctured the paper, the thread often does not fill out the hole but leaves an opening. This attracts female stored product moths and beetles for oviposition and is sufficient for minute neonate larvae to enter into the stored good following the cue of attractive volatiles. Industry complaints in Germany seem to have increased during recent hot summer periods. Heavily infested paper bags and an increase in humidity allowed additional species their development such as dust lice. In a laboratory test with sewn seed bags, eggs and neonate larvae of the Indianmeal moth Plodia interpunctella (Lepidoptera, Pyralidae) were found next to the seam. Holes were approximately 1 mm in diameter while the thread only covered 0.2–0.3 mm. This is sufficient for larvae to enter. As infestation pressure is prone to rise due to global warming, preventive action is required. Pest-proof packaging could help to avoid losses and guarantee product quality on the way from producer to consumer. Instead of a seam, the opening of a paper-bag could be glued shut. The glue should cover all bag circumference in order to provide a complete barrier against larval movement, as glue points would still leave openings for insect larvae to enter. For bags that require the seam or gas exchange, additional options are discussed in this paper.
Association between Job burnout and Productivity among Iranian operating room Nurses: A cross-sectional study in northwest Iran
Authors: Rahmani Vahid , Alizadeh Amirali , Arabsorkhi Mohammad , Gheibi Mohaddeseh , Aghazadeh Nasrin
Published: October 2024

Background
Job burnout is a widespread issue among medical staff and operating room nurses due to the complex and stressful conditions they face in their work environment. This phenomenon can have several negative effects, such as reducing job satisfaction, increasing absenteeism, and lowering the quality of patient care. Therefore, this study aims to investigate the association between job burnout and productivity among Iranian operating room nurses.
Methods
This cross-sectional study was conducted on 294 operating room nurses in Iran. The study made use of a demographic information questionnaire, Maslach Burnout Inventory (MBI), and Hersey and Goldsmith Productivity Questionnaire for data collection. The collected data were analyzed using descriptive and analytical tests with SPSS V.27.
Results
The study's findings indicate that operating room nurses experience a moderate level of job burnout (62.18 ± 14.17) and productivity (55.61 ± 12.37). Furthermore, the study discovered a significant negative correlation between burnout and productivity in these nurses (P < 0.001, r=-0.278).
Conclusion
The study shows a negative association between job burnout and productivity, suggesting actions like workload reduction, enhanced social support, better work-life balance, process improvements, and increased appreciation could mitigate burnout and boost productivity, thereby improving patient care quality.
Activating Wnt1/β-Catenin signaling pathway to restore Otx2 expression in the dopaminergic neurons of ventral midbrain
Authors: Lin Xian , Li Zhao , Duan Jin , Cao An , Gong Zhuo , Liu Hao , Shen Dan , Ye Tong , Zhu Shun , Cen Qi , He Shuai , He Yong , Zheng Can
Published: October 2024

Parkinson’s disease (PD) is the world’s second most prevalent neurodegenerative disease. Currently, aside from levodopa, there are no other effective drugs clinically available to slow its progression. Otx2 plays a critical role in the differentiation of midbrain dopaminergic neurons (mDANs) during midbrain development. However, in adulthood, Otx2 is primarily expressed in the ventral tegmental area (VTA)-ventral part, and mDANs in the dorsal part of the VTA and the substantia nigra pars compacta (SNc) show no Otx2 expression. Research indicates that Otx2 is essential not only for the development of mDANs but also for their protection against the toxicity of MPTP and rotenone. Consequently, Otx2 is a potential clinical target for mDANs protection. Identifying the upstream mechanism that regulates Otx2 expression is crucial to restoring its expression in the SNc and enhancing its levels in the entire ventral midbrain mDANs. In this study, we have demonstrated the safety of Otx2 overexpression in vitro using adeno-associate virus (AAV) and explored the feasibility of promoting Otx2 expression through the Wnt/β-Catenin signaling pathway using various drugs, a miR-34 mimic, and an inhibitor. Our results showed that Otx2 overexpression via AAV in the SNc is relatively safe, and CHIR99021 can induce Otx2 expression in mouse mDANs, thereby, alleviating PD-liked motor symptoms induced by MPTP. These findings suggest that modulating Otx2 expression through the Wnt/β-Catenin signaling pathway holds a therapeutic approach for Parkinson’s disease.
The BrainHealth Project Study Protocol: A Longitudinal Study in Generally Healthy Adults
Authors: Cook Lori G. , Spence Jeffrey S. , Venza Erin E. , Tate Aaron , Robertson Ian H. , D'Esposito Mark , Ling Geoffrey S.F. , Wigginton Jane , Chapman Sandra Bond
Published: October 2024

Background: Optimization of brain health is a focal point in medical science, yet data regarding measuring, preserving, and improving lifelong brain health are lacking. This void demands an objective, change-sensitive measure of brain health and proven strategies to strengthen brain performance. The BrainHealth Project addresses these key issues, drawing upon neuroplasticity evidence of persistent modifiability of brain function across the lifespan. This landmark study aims to definitively (i) evaluate and refine holistic change indices of brain health, (ii) evaluate the impact of evidenced-based cognitive strategies and lifestyle interventions on improving/maintaining brain health, and (iii) elucidate the mechanisms associated with brain health gains/losses.
Methods: This prospective, longitudinal, interventional, open-label, single-arm clinical trial aims to recruit 100,000 generally healthy adults over 10 years. Assessments, coaching, and training are conducted online through the secure BrainHealth Platform, allowing utilization tracking. The BrainHealth Index (BHI)—a multidimensional assessment—is offered at baseline and every six months. Participants have access to coaching every three months and continual access to self-paced trainings and resources.
The primary outcome is the further validation of a novel composite BHI score and its sub-scores: Clarity (cognitive health), Connectedness (social health), and Emotional Balance (well-being). The BHI includes measures sensitive to change, including gains from behavior change and integration of cognitive strategies into daily life. These contrast with traditional assessments focused on detecting cognitive decline or diagnosing pathological conditions.
The primary online training, SMART (Strategic Memory Advanced Reasoning Tactics), is a strategy-based program validated with 25+ years of research. SMART promotes improvements in neural health, cognition, well-being, connectedness, and real-life function, previously reported in randomized controlled trials (RCTs). Statistical approaches focus on individual prediction using nonlinear models trained with large samples and on assessing mechanisms influencing gains or losses on brain health metrics.
Discussion: This research extends RCT evidence to a longitudinal, epidemiologic approach, leveraging digital health and machine learning tools to deliver a generalizable measure of longitudinal brain health and to achieve precision brain health. By integrating advanced statistical methods and large-scale data, the BrainHealth Project should provide medicine and society with accurate and actionable ways to optimize brain health practices across the lifespan.
ClinicalTrials.gov Identifier: NCT04869111 (registered April 27, 2021)
Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis
Authors: Zhang Yunfan , Yuan Fan , Liu Zheng , Huang Xiaoxi , Hong Junsheng , Chang Feifan , Wu Dawei
Published: October 2024

Background
Dysbiosis of the nasal microbiome is considered to be related to the acute exacerbation of chronic rhinosinusitis (AECRS). The microbiota in the nasal cavity of AECRS patients and its association with disease severity has rarely been studied. This study aimed to characterize nasal dysbiosis in a prospective cohort of patients with AECRS.
Methods
We performed a cross-sectional study of 28 patients with AECRS, 20 patients with chronic rhinosinusitis (CRS) without acute exacerbation (AE), and 29 healthy controls using 16S rRNA gene sequencing. Subjective and objective assessments of CRS disease severity during AE were also collected.
Results
Compared to healthy controls and patients with CRS without AE, AECRS presented with a substantial decrease of the Corynebacterium_1 and a significant increase of Ralstonia and Acinetobacter at the genus level (LDA score > 2.0 [P < 0.05]). Furthermore, 29 genera with a substantial alteration in AECRS were rare constituents of the microbiome, of which 18 rare genera were highly associated with subjective and objective disease severity. Moreover, a combination of 15 genera could differentiate patients with AECRS with an area under the curve of 0.870 (95% CI = 0.784–0.955). Prediction of microbial functional pathways involved significantly enhanced lipopolysaccharide biosynthesis pathways and significantly decreased folate biosynthesis, sulfur relay system, and cysteine and methionine metabolism pathways in patients with AECRS.
Conclusions
The rare nasal microbiota correlated with disease status and disease severity in patients with AECRS. The knowledge about the pattern of the nasal microbiome and its metabolomic pathway may contribute to the fundamental understanding of AECRS pathophysiology.
Updating the impact of mRNA COVID-19 vaccine exposure during pregnancy on obstetric and neonatal outcomes.
Authors: Adusei-Mensah Frank , Olubamwo Olubunmi , Olaleye Sunday , Akter Laboni , Balogun Oluwafemi Samson , Moshoeshoe Rethabile Joyce , Awoniyi Luqman , Olawuni Adedayo , Kauhanen Jussi
Published: October 2024

Background
The safety of mRNA COVID-19 vaccines in pregnant women remains a critical concern. This systematic review and meta-analysis evaluate the maternal and neonatal outcomes associated with mRNA COVID-19 vaccination during pregnancy.
Methods
We conducted a systematic search of PubMed, Embase, Cochrane Library, and clinical trial registries for studies published between December 2020 and July 2024. Studies were included if they assessed obstetric and neonatal outcomes following mRNA COVID-19 vaccination in pregnant women. Data were extracted and analyzed using a random-effects model to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs).
Results
Twelve studies met the inclusion criteria, encompassing 42,944 vaccinated and 183,733 unvaccinated pregnant women. mRNA vaccination was associated with a significant reduction in preterm delivery (OR 0.743, 95% CI 0.607–0.911), fetal distress (OR 0.699, 95% CI 0.546–0.893), neonatal congenital abnormalities (OR 0.712, 95% CI 0.570–0.889), and NICU admissions (OR 0.718, 95% CI 0.617–0.836). However, a slight increase in gestational diabetes risk was observed (OR 1.107, 95% CI 1.054–1.162).
Conclusion
mRNA COVID-19 vaccines are safe during pregnancy and associated with reduced risks of adverse obstetric and neonatal outcomes. The observed increase in gestational diabetes risk underscores the need for vigilant monitoring. These findings support the inclusion of pregnant women in vaccination campaigns and inform public health policies and clinical practices to improve maternal and neonatal health outcomes.
Identification of hub genes in osteoarthritis via integrating bioinformatics analysis and machine learning
Authors: Wang Yanqing , Xu Yizhou , Deng Gang , Xu Shuyi , Li Xican , Zhou Xianxi , Guo Jiasong , Chen Dongfeng
Published: October 2024

Osteoarthritis (OA) represents a significant burden on global healthcare systems that causes pain and functional impairment by affecting joint tissue. Ubiquitin-specific peptidase 53 (USP53), a member of the ubiquitin-specific protease (USP) family, is involved in the progression of various disease states, but its role in OA has not been investigated. This study employed bioinformatics analysis to identify 92 common genes were identified in the positivity related modules of OA. Further studies demonstrated that the DEGs enriched in 20 pathways, mainly including the Focal adhesion, PI3K-Akt signaling pathway, ECM-receptor interaction, Human papillomavirus infection and Pyrimidine metabolism in KEGG enrichment analysis. Furthermore, machine learning results show that USP53, CA12 and PON could be effective biomarkers for OA diagnosis. Preliminary laboratory investigations showed that compared with the control group, the expression of USP53 in the OA group showed significant changes in vivo. The clinical specimen test results showed the protein expression of USP53 in cartilage tissue of OA patients. All in all, this study indicated the hub genes USP53 could be a potential biomarker for OA, thus providing a novel insight into the modulation of ubiquitin in OA clinical diagnosis and treatment.
Cockayne syndrome mice reflect human kidney disease and are defective in de novo NAD biosynthesis
Authors: Bohr Vilhelm , Pekhale Komal , Tiwari Vinod , Kulikowicz Thomas , Bridges Christy , Croteau Deborah , Levy Moshe , Wang Xiaoxin , Lee Jong-Hyuk
Published: October 2024

Cockayne Syndrome (CS) is a premature aging disorder caused by mutations in the CSA and CSB genes involved in DNA metabolism and other cellular processes. CS patients display many features including premature aging, neurodegeneration, and kidney abnormalities. Nicotinamide dinucleotide (NAD+) deprivation has been oberserved in CS patient-derived cells. NAD+ has essential roles in regulating cellular health, stress responses, and renal homeostasis. While kidney dysfunction is a common featurein CS patients, its molecular pathogenesis is not understood. Here, we report that severe kidney pathology is present in CS A and B mice. We find that the NAD+ biosynthetic pathways are impaired in kidneys from these mice. Using human renal tubular epithelial cells, we show that CSA/B downregulation causes persistent activation of the ATF3 transcription factor on the quinolinate phosphoribosyl transferase gene locus, a rate-limiting enzyme in de novo NAD+ biosynthesis in the kidney, causing impaired transcription and deficient NAD+ homeostasis.
Autodisplay made easy: a plasmid toolbox for the surface display of recombinant proteins and its optimization.
Authors: Jose Joachim , Röhe Philip , Furtmann Christoph
Published: October 2024

A plasmid library is provided for the surface display of proteins, to be used by experts and non experts, including a simple option for optimization. Based on a classical autotransporter protein, variants were constructed with different signal peptides, linkers and adjacent regions, set under control of a rhaBAD promoter, resulting in a library of 24 plasmids named Autodisplay ToolBox (ATB). For proof of concept, a β-glucosidase as model passenger was subjected to combinatorial permutation using the plasmids of the ATB (pATB). The best combination based on enzymatic activity was pATB_45-β-Gluc with an OprF signal peptide, a C terminal spacer (GGGDDNAAPA), and a short linker (ΔepitopeΔβ1). Activity was 4.9-fold increased compared to the initial construct.
ATB can be extended to 72 variants by using two additional promoters (PRox306, ParaBAD) and to 81 variants by using an inverse autotransporter protein (YeeJ). Besides using the pATB library, individual plasmids can be picked for custom-made surface display.
ML-test: a machine learning-based test for integer ambiguity validation
Authors: Hou Yanqing , Shi Yining , Duan Xiaojun , Chen Xuan
Published: October 2024

The accurate resolution of integer ambiguity is crucial for Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), with ambiguity validation serving as a pivotal stage. The ratio test based on a fixed threshold (RT) is currently the most widely used test for ambiguity validation. However, due to its inflexible characteristics of remaining a fixed threshold, it is challenging to guarantee availability and reliability simultaneously. The fixed failure rate ratio test (FFRT) addresses this by adjusting the threshold dynamically according to model strength. However, its reliability is compromised due to the detachment from real scenarios in Monte Carlo simulations and theoretical limitations. This paper contributes by proposing the ML-test, a machine learning (ML)-based test for ambiguity validation. ML-test aims to discover patterns influencing the correctness of ambiguity validation by utilizing a substantial amount of real data for ML. To verify the performance of ML-test, comparative experiments between ML-test, RT, and FFRT are conducted over multiple days and baselines. The results indicate that the total average fix rate of RT (with a fixed threshold of 3) is between 74% and 78%, FFRT (with a fixed failure rate of 0.001) is between 98% and 99%, and ML-test is between 89% and 96%. The total average conditional success rate of RT is nearly 100%, FFRT is between 96% and 98%, and ML-test is between 98% and 99%, generally higher than FFRT. It is evident that ML-test demonstrates advantages in balancing availability and reliability, with stable performance that ensures a high fix rate while maintaining a sufficiently high conditional success rate.
Towards Understanding the Firm Specific Determinants of Corporate Financial Flexibility
Authors: Al-ATHAMNEH MUSTAFA , OBEIDAT MOHAMMED , ALMOMANI MOHAMMAD , ALMOMANI TAREQ , Darkel Nadeen
Published: October 2024

The study objects for determining the most important firm specific factors affecting the corporate financial flexibility of the listed manufacturing firms at Amman Stock Exchange. Firm specific factors including, profitability, assets tangibility, cash holdings, and retained earnings, are taken into consideration, as possible internal determinants of corporate financial flexibility. To achieve the objectives of the study, secondary data covering the period 2013-2021, of 40 listed manufacturing listed firms at Amman Stock Exchange, had been collected and used in the analysis and hypotheses testing. Employing both of the simple and linear regression methods in hypotheses testing, and at the individual level of independent variables, the result reveals a significant impact of profitability, assets tangibility, cash holdings, and capital structure, and insignificant impact of retained earnings, on corporate financial flexibility. Moreover, a combined grouping significant impact, the result shows for the different firm specific factors, as a single group, on financial flexibility. The study recommends more investigations regarding financial flexibility and its internal and external macroeconomic determinants.
A Unified Multi-Objective Adaptive Task Scheduling for VM Optimization in Heterogeneous Cloud Data Center
Authors: MP Abdul Razaak , Masood Mohamed
Published: October 2024

In light of the swift progress of cloud technology, substantial cloud computing centers have surfaced to cater to the escalating demand in the business realm. These centers are designed to cater to the increasing demands of data processing and machine learning, with their vast number of computing resources, efficient resource and energy management becomes crucial in order to reduce costs and minimize environmental impact. Energy efficiency in cloud computing is a critical concern as the demand for cloud services continues to grow. Energy efficiency in cloud computing is not only a technological imperative but also a pivotal step towards a more sustainable and environmentally responsible future. With the increasing environmental awareness and the need to reduce operational costs, optimizing energy consumption in data centres and cloud infrastructure is essential. The Virtual Machine Consolidation (VMMC) is a strategy to optimize power utilization in the Data Center of Cloud by optimizing hardware and software, consolidating virtual machines, scheduling workloads, and integrating renewable energy. In this paper, we proposed the ARMS (Adaptive Resource Management Systems) framework to a mixture of the Autoregressive Integrated Moving Average (ARIMA) model with Random Forest Algorithm (RFA). The experiment results indicate that the benchmark methods are outperformed in terms of Energy Efficiency, Cost, Makespan, SLAV, and Number of Iteration etc.
The role of modelling in stakeholder discussions for nature-based solutions
Authors: Bogatinoska Borjana , Lansu Angelique , Hugé Jean , Dekker Stefan C. , Stoorvogel Jetse
Published: October 2024

Nature-based solutions have gained recognition for their potential in addressing multiple challenges such as flooding, droughts but also carbon sequestration. NbS often require interventions and changes of land management in rural areas that require the involvement of multiple stakeholders. This study explores the role of environmental modelling to support stakeholder discussions aimed at planning nature-based solutions. The research focuses on the Aa of Weerijs catchment, a transboundary area in Belgium and the Netherlands facing challenges like droughts and flooding. Four potential model contributions are identified to support stakeholder discussions around interventions including: sensitivity analysis, impact assessment, scenario evaluation – catchment level and scenario evaluation – local level. The study makes use of a stakeholder-oriented approach, emphasizing an iterative process for aligning the decision space of stakeholders to the opportunity space. Opportunity space refers to options that work and help reach the goals and decision space includes the options that are considered by the stakeholders. Results demonstrate the sensitivity of the catchment to extrememe land use land cover changes, identify impactful locations for nature-based solutions’ implementation, evaluate diverse nature-based scenarios, and assess location-specific feasibility. The findings underscore the significance of integrating advanced visualization tools together for a model understanding specifically of water-carbon interaction and iterative stakeholder engagement for effective nature-based solutions’ design, offering a pathway for informed decision-making.
Dragonfly Visual Attention-Merged Evolutionary Neural Network: Biomimetic Optimizer Solving LSGO problems
Authors: Zhang Zhuhong , Wang Heng
Published: October 2024

The biological visual system includes a variety of motion-sensitive neurons that intrinsically perceive changes in visual motion. Although some of their neurophysiological findings have been successfully used to construct computational models for engineering applications targeting specific problems, it remains unclear how the relevant response mechanisms can be used to optimize the subject. Hereby, related to grayscale intensity difference, the dragonfly visual information-processing and attention mechanisms are borrowed to develop a feedforward dragonfly visual attention-merged neural network with presynaptic and postsynaptic neural subnetworks, which can characterize changes of visual motion in terms of the on-line output activities named learning rates. Afterwards, integrated such learning rates into a new-type and metaheuristics-inspired state transition strategy, a dragonfly attention-merged visual evolutionary neural network with the unique parameter of input resolution, which enables current states to discover the global optimum quickly, is developed to solve large-scale global optimization (LSGO) and even hyperdimensional global optimization (HDGO) problems. The theoretical analysis implicates that the evolutionary neural network’s complexity is mainly decided by the optimization problem itself. Comparative experiments have validated that it, as a highly competitive optimizer, can significantly outperform the compared approaches and successfully solve the design optimization problem of a complex sixth-order analog active filter.
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

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

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

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

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

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

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

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

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

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

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

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.