bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multifunctional 3D-printed composites based on biopolymeric matrices and tomato plant (Solanum lycopersicum) waste for contextual fertilizer release and Cu(II) ions removal
Authors: Scaffaro Roberto , Gulino Emmanuel Fortunato , Citarrella Maria Clara 
Published: May 2024

AbstractThe production of tomatoes faces significant challenges, including the high amount of waste generated during the harvest stage and copper-contaminated soil due to pesticide use. To address these issues and to promote a more sustainable agriculture, innovative biodegradable green composites for contextual controlled soil fertilization and Cu removal were produced by 3D-printing technology. These composites were made by incorporating NPK fertilizer flour and tomato plant waste particles (SLP) into three different biodegradable polymeric matrices: polylactic acid (PLA); a commercial blend of biodegradable co-polyesters (Mater-Bi®, MB) and their blend (MB/PLA, 50:50). Rheological characterization suggested the potential processability of all of the composites by FDM. Morphological analysis of printed samples confirmed the good dispersion of both filler and fertilizer, which also acted as reinforcement for MB and MB/PLA composites. SLP and NPK moduli were evaluated by powder nanoindentation and, for almost composites, the theoretical Halpin-Tsai model satisfactorily fitted the actual tensile moduli. The decrease in NPK fertilizer release rate and the increase in Cu(II) removal efficiency were achieved using whole 3D-printed composites. By selecting the appropriate matrix and incorporating SLP particles, it was possible to tune the NPK release rate and achieve copper absorption efficiency. Notably, MB samples containing SLP particles displayed the fastest release and the highest Cu(II) removal efficiency.
Graphical abstract
A Deep Learning Framework for Monitoring Audience Engagement in Online Video Events
Authors: Vrochidis Alexandros , Dimitriou Nikolaos , Krinidis Stelios , Panagiotidis Savvas , Parcharidis Stathis , Tzovaras Dimitrios 
Published: May 2024

AbstractThis paper introduces a deep learning methodology for analyzing audience engagement in online video events. The proposed deep learning framework consists of six layers and starts with keyframe extraction from the video stream and the participants’ face detection. Subsequently, the head pose and emotion per participant are estimated using the HopeNet and JAA-Net deep architectures. Complementary to video analysis, the audio signal is also processed using a neural network that follows the DenseNet-121 architecture. Its purpose is to detect events related to audience engagement, including speech, pauses, and applause. With the combined analysis of video and audio streams, the interest and attention of each participant are inferred more accurately. An experimental evaluation is performed on a newly generated dataset consisting of recordings from online video events, where the proposed framework achieves promising results. Concretely, the F1 scores were 79.21% for interest estimation according to pose, 65.38% for emotion estimation, and 80% for sound event detection. The proposed framework has applications in online educational events, where it can help tutors assess audience engagement and comprehension while hinting at points in their lectures that may require further clarification. It is effective for video streaming platforms that want to provide video recommendations to online users according to audience engagement.
Using the Fuzzy Method and Multi-Criteria Decision Making to Analyze the Impact of Digital Economy on Urban Tourism
Authors: Wang Ning 
Published: May 2024

AbstractUrban tourism promotes the economic growth of a nation around the year through direct and indirect incomes. In recent years, the digital economy has impacted the growth of urban tourism through hassle-free money transactions and expenditures. This article, therefore, introduces a Multi-Criteria Fuzzy-based Decision-Making Method (MCFDMM) for validating the impact of the digital economy impact over tourism. The study introduces a new framework, DLFDSS-RRM, that uses deep learning and fuzzy decision support systems for residence right management, enhancing resource allocation, security, and resident satisfaction in urban residential communities. The criteria such as expenses, positive response, and repeated payments are validated by the tourists across their travel plan. These conditions satisfying the tourist’s expectations are estimated based on their reviews of economic conditions are validated. The validation is performed against the growth of the country from urban tourism. The fuzzy process validates the growth of the country between two successive financial quarters based on the above conditions. In the condition analysis, the fuzzy process identifies the least derivatives contributing to minimal economic growth. This is reversed using the hiking condition that occurs in any quarter and hinders economic growth. Therefore, the process is validated using the metrics growth rate, condition satisfaction, analysis rate, analysis time, and unrelated assessment. The comparative analysis across various models reveals growth rates ranging from 0.263 to 0.4055, condition satisfaction percentages from 53.747 to 74.351, and analysis rates from 0.275 to 0.4662.
Analysis of Jet Blast Distance of a Refined Engine Nozzle Model for Departing Aircraft
Authors: Gao Haoran , Guo Dongxin , Li Zejun , Niu Tiantian , He Xin , Chen Yaqing 
Published: May 2024

AbstractTo evaluate the influence distance and evolution law of the jet blast under the full thrust state of the aircraft engine, we constructed a refined single-engine nozzle model in this study. Using a specific B737-800 engine as an illustrative example, this model takes into account both the internal combustion of the engine and the shearing effect of the tail cone on the airflow; based on a structured grid, the grid and far-field independence of the numerical solution were verified. The numerical simulation results obtained via the Fluent software were compared with the data outlined in the Boeing aircraft characteristics manual for a thorough analysis. Our results indicated that the calculated jet influence distance from the refined nozzle model aligns more closely with the data in the aircraft characteristics manual, albeit with superior accuracy compared to the simplified nozzle model employed in previous studies. The findings of this study can serve as a valuable reference for computing jet influence distances across various aircraft types and engine models, providing data support for the study of safety intervals for aircraft crossing the runway behind the takeoff point.
STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks
Authors: Zhang Jijie , Xu Xiaolong , Xiao Fu 
Published: May 2024

AbstractWith the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%,$${R}^{2}$$R2index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.
Breaking down (and moving beyond) novelty as a trigger of curiosity
Authors: Liquin Emily G. , Lombrozo Tania 
Published: May 2024

Abstract
The Novelty Seeking Model (NSM) places “novelty” at center stage in characterizing the mechanisms behind curiosity. We argue that the NSM's conception of novelty is too broad, obscuring distinct constructs. More critically, the NSM underemphasizes triggers of curiosity that better unify these constructs and that have received stronger empirical support: those that signal the potential for useful learning.
National epidemiology of culture-confirmed brucellosis in Israel, 2004–2022
Authors: Weinberger Miriam , Moran-Gilad Jacob , Perry Markovich Michal , Bardenstein Svetlana 
Published: May 2024

Abstract
Brucellosis, a global zoonosis, is endemic in Israel. We used a national database of culture-confirmed cases (2004–2022) to analyse the trends of brucellosis. Of 2,489 unique cases, 99.8% were bacteraemic, 64% involved males, and the mean age was 30.5 years. Brucella melitensis was the dominant species (99.6%). Most cases occurred among the Arab sector (84.9%) followed by the Jewish (8.5%) and Druze (5.5%) sectors. The average annual incidence rates overall and for the Arab, Druze, and Jewish sectors were 1.6/100,000, 6.6/100,000, 5.5/100,000, and 0.18/100,000, respectively. The annual incidence rates among the Arab (incidence rate ratio (IRR) = 36.4) and the Druze (IRR = 29.6) sectors were significantly higher than among the Jewish sector (p < 0.001). The highest incidence rates among the Arab sector occurred in the South District, peaking at 41.0/100,000 in 2012. The frequencies of B. melitensis isolated biotypes (biotype 1 – 69.1%, biotype 2 – 26.0%, and biotype 3 – 4.3%) differed from most Middle Eastern and European countries. A significant switch between the dominant biotypes was noted in the second half of the study period. Efforts for control and prevention should be sustained and guided by a One Health approach mindful of the differential trends and changing epidemiology.
Profiles of Dementia Caregivers according to Psychosocial and Resource Variables. Importance of Kinship
Authors: Huertas-Domingo Cristina , Losada-Baltar Andrés , Gallego-Alberto Laura , García-Batalloso Inés , García-García Laura , Márquez-González María 
Published: May 2024

Abstract
The present study aims to analyze the existence of different profiles in family caregivers of people with dementia according to psychosocial and resource variables. In addition, it aims to study whether there is a greater representation of each kinship group in each of the profiles and if there are differences in emotional distress among such profiles considering the kinship with the care-recipient. Participants were 288 family dementia caregivers, divided into four kinship groups (wives, husbands, sons and daughters). Psychosocial (familism, dysfunctional thoughts and experiential avoidance), resource (leisure activities and social support) and outcomes (depressive, anxious and guilt symptomatology) variables were collected. A hierarchical cluster analysis using Ward’s method, an exploratory factor analysis of two fixed factors and contingency tables were performed. Five clusters were obtained: Low psychosocial vulnerability-High resources, Low psychosocial vulnerability-Low resources, Mixed, High psychosocial vulnerability-High resources, and High psychosocial vulnerability-Low resources. Results suggested that clusters associated with lower distress were the Low psychosocial vulnerability-High resources and the High psychosocial vulnerability-High resources. Clusters associated with higher distress were the Low psychosocial vulnerability-Low resources and Mixed. High levels of dysfunctional thoughts, familism and experiential avoidance do not always have a maladaptive function. This could depend on sociocultural and resource variables such as the kinship with the caregiver or perceived social support. The identification of profiles of family caregivers potentially needing protection and vulnerable to psychological distress could help to increase the effectiveness of interventions aimed at this population.
The Significance of Ancient Buried Landscapes as Natural Geomorphic Experiments
Authors: Conway‐Jones B. W. , White N. J. 
Published: May 2024

AbstractThere is considerable interest in developing quantitative methods for analyzing present‐day fluvial landscapes with a view to extracting information about tectonic forcing and drainage evolution, together with the influence of lithologic substrates and of paleoclimatic variations. In view of the multifactorial nature of this complex problem, it has previously been proposed that natural geomorphic experiments could play a significant role in developing a quantitative understanding of landscape growth and decay. Here, we describe and analyze a stacked sequence of five buried transient landscapes that punctuate marine strata along the fringes of the North Atlantic Ocean. We propose that these landscapes constitute a suite of natural experiments, which illuminate significant aspects of quantitative fluvial geomorphology. Our preliminary analysis of four of these buried landscapes suggests that the amplitude of external tectonic forcing plays a significant role in fluvial landscape evolution. In future, we hope that this suite of natural experiments will be further exploited by the fluvial community with a view to identifying the most appropriate analytical techniques.
Blood volume reduction due to rapid plasma loss after birth in preterm piglets
Authors: Nguyen Tam M. T. , Morwood Holly , Bakrania Bhavisha A. , Miller Stephanie M. , Bjorkman Stella T. , Lingwood Barbara E. , Wright Ian M. R. , Eiby Yvonne A. 
Published: May 2024

Abstract

Background
Understanding changes in blood volume after preterm birth is critical to preventing cardiovascular deterioration in preterm infants. The aims were to determine if blood volume is higher in preterm than term piglets and if blood volume changes in the hours after birth.

Methods
Paired blood volume measurements were conducted in preterm piglets (98/115d gestation, ~28wk gestation infant) at 0.5–5 h (n = 12), 0.5-9 h (n = 44) and 5–11 h (n = 7) after birth, and in a term cohort at 0.5–9 h (n = 40) while under intensive care.

Results
At 30 min after birth, blood volume was significantly lower in preterm piglets compared to term piglets. By 9 h after birth, blood volume had reduced by 18% in preterm piglets and 13% in term piglets. By 5–9 h after birth, preterm piglets had significantly lower blood volumes than at term (61 ± 10 vs. 76 ± 11 mL/kg).

Conclusions
In contrast to clinical resources, preterm piglets have a lower blood volume than at term. Substantial reductions in blood volume after birth leave some preterm piglets hypovolemic. If this also occurs in preterm infants, this may have important clinical consequences. Modern studies of blood volume changes after birth are essential for improving preterm outcomes.

Impact

Preterm piglets do not have a higher blood volume than their term counterparts, in contrast to current clinical estimates.

Rapid reduction in blood volume after birth leads to hypovolemia in some preterm piglets.

There is a critical need to understand blood volume changes after birth in preterm infants in order to improve clinical management of blood volume.
Anti-T-lymphocyte globulin improves GvHD-free and relapse-free survival in myelofibrosis after matched related or unrelated donor transplantation
Authors: Rathje Kristin , Gagelmann Nico , Salit Rachel B. , Schroeder Thomas , Gurnari Carmelo , Pagliuca Simona , Panagiota Victoria , Rautenberg Christina , Cassinat Bruno , Thol Felicitas , Robin Marie , Oechsler Sofia , Heuser Michael , Rubio Marie-Thérèse , Maciejewski Jaroslaw P. , Reinhardt Hans Christian , Scott Bart L. , Kröger Nicolaus 
Published: May 2024

AbstractAcute and chronic graft-versus-host disease (GvHD) are major complications of allogeneic hematopoietic cell transplantation (alloHCT). In vivo T-cell depletion with anti-T-lymphocyte globulin (ATLG) as part of the conditioning regimen prior to alloHCT is frequently used as GvHD prophylaxis, but data on its role in myelofibrosis is scarce. We took advantage of an international collaborative network to investigate the impact of ATLG in myelofibrosis undergoing first alloHCT. We included 707 patients (n = 469 ATLG and n = 238 non-ATLG prophylaxis). The cumulative incidence of acute GvHD grade II-IV was 30% for the ATLG group vs. 56% for the non-ATLG group (P < 0.001). Acute GvHD grade III-IV occurred in 20% vs. 25%, respectively (P = 0.01). Incidence of mild-to-severe chronic GvHD was 49% vs. 50% (P = 0.52), while ATLG showed significantly lower rates of severe chronic GvHD (7% vs. 18%; P = 0.04). GvHD-free and relapse-free survival (GRFS) at 6 years was 45% for the ATLG group vs. 37% for the non-ATLG group (P = 0.02), driven by significantly improved GRFS of ATLG in matched related and matched unrelated donors. No significant differences in risk for relapse, non-relapse mortality, and overall survival were observed. Multivariable modeling for GRFS showed a 48% reduced risk of GvHD, relapse, or death when using ATLG.
Multimodal fusion of brain signals for robust prediction of psychosis transition
Authors: Reinen Jenna M. , Polosecki Pablo , Castro Eduardo , Corcoran Cheryl M. , Cecchi Guillermo A. , Colibazzi Tiziano 
Published: May 2024

AbstractThe prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.
Mendelian randomization analysis reveals causal associations of serum metabolites with sepsis and 28-day mortality
Authors: Jing Guoqing , Zuo Jing , Liu Zhi , Liu Huifan , Cheng Miao , Yuan Min , Gong Hailong , Wu Xiaojing , Song Xuemin 
Published: May 2024

AbstractMetabolic disorder has been found to be an important factor in the pathogenesis and progression of sepsis. However, the causation of such an association between serum metabolites and sepsis has not been established. We conducted a two-sample Mendelian randomization (MR) study. A genome-wide association study of 486 human serum metabolites was used as the exposure, whereas sepsis and sepsis mortality within 28 days were set as the outcomes. In MR analysis, 6 serum metabolites were identified to be associated with an increased risk of sepsis, and 6 serum metabolites were found to be related to a reduced risk of sepsis. Furthermore, there were 9 metabolites positively associated with sepsis-related mortality, and 8 metabolites were negatively correlated with sepsis mortality. In addition, “glycolysis/gluconeogenesis” (p = 0.001), and “pyruvate metabolism” (p = 0.042) two metabolic pathways were associated with the incidence of sepsis. This MR study suggested that serum metabolites played significant roles in the pathogenesis of sepsis, which may provide helpful biomarkers for early disease diagnosis, therapeutic interventions, and prognostic assessments for sepsis.
Supercapacitors and triboelectric nanogenerators based on electrodes of greener iron nanoparticles/carbon nanotubes composites
Authors: dos Reis Glaydson Simoes , de Oliveira Helinando Pequeno , Candido Iuri Custodio Montes , Freire Andre Luiz , Molaiyan Palanivel , Dotto Guilherme Luiz , Grimm Alejandro , Mikkola Jyri-Pekka 
Published: May 2024

AbstractThe development of supporting materials based on carbon nanotubes (CNTs) impregnated with iron nanoparticles via a sustainable and green synthesis employing plant extract of Punica granatum L. leaves was carried out for the iron nanoparticle modification and the following impregnation into the carbon nanotubes composites (CNT-Fe) that were also coated with polypyrrole (CNT-Fe + PPy) for use as electrode for supercapacitor and triboelectric nanogenerators. The electrochemical characterization of the materials by cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) assays revealed that the CNT-Fe + PPy gave rise to better performance due to the association of double-layer capacitance behavior of carbon derivative in association with the pseudocapacitance contribution of PPy resulting in an areal capacitance value 202 mF/ cm2 for the overall composite. In terms of the application of electrodes in triboelectric nanogenerators, the best performance for the composite of CNT-Fe + PPy was 60 V for output voltage and power density of 6 μW/cm2. The integrated system showed that the supercapacitors can be charged directly by the nanogenerator from 0 to 42 mV in 300 s. The successful green synthesis of iron nanoparticles on CNT and further PPy coating provides a feasible method for the design and synthesis of high-performance SCs and TENGs electrode materials. This work provides a systematic approach that moves the research front forward by generating data that underpins further research in self-powered electronic devices.
Ganoderma tsuage promotes pain sensitivity in aging mice
Authors: Yang Kai-Ning , Lin Chia-Ying , Li Wei-Nong , Tang Chao-Ming , Pradhan Jyotirmayee , Chao Ming-Wei , Tseng Chia-Yi 
Published: May 2024

AbstractAdvances in modern medicine have extended human life expectancy, leading to a world with a gradually aging society. Aging refers to a natural decline in the physiological functions of a species over time, such as reduced pain sensitivity and reaction speed. Healthy-level physiological pain serves as a warning signal to the body, helping to avoid noxious stimuli. Physiological pain sensitivity gradually decreases in the elderly, increasing the risk of injury. Therefore, geriatric health care receives growing attention, potentially improving the health status and life quality of the elderly, further reducing medical burden. Health food is a geriatric healthcare choice for the elderly with Ganoderma tsuage (GT), a Reishi type, as the main product in the market. GT contains polysaccharides, triterpenoids, adenosine, immunoregulatory proteins, and other components, including anticancer, blood sugar regulating, antioxidation, antibacterial, antivirus, and liver and stomach damage protective agents. However, its pain perception-related effects remain elusive. This study thus aimed at addressing whether GT could prevent pain sensitivity reduction in the elderly. We used a galactose-induced animal model for aging to evaluate whether GT could maintain pain sensitivity in aging mice undergoing formalin pain test, hot water test, and tail flexes. Our results demonstrated that GT significantly improved the sensitivity and reaction speed to pain in the hot water, hot plate, and formalin tests compared with the control. Therefore, our animal study positions GT as a promising compound for pain sensitivity maintenance during aging.