bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is noxious stimulus-evoked electroencephalography response a reliable, valid, and interpretable outcome measure to assess analgesic efficacy in neonates? A systematic review and individual participant data (IPD) meta-analysis protocol
Authors: Baxter Luke , Vaart Marianne van der , Cobo Maria M , Gunawan Patricia , Allegaert Karel , Davis Jonathan M , Turner Mark , Ward Robert , Darsey Edress , Sheppard James P , Bhatt Aomesh , Anker John van den , Massaro An N , Walls Ramona L , Song Laura S , Singh Kanwaljit , N/A INC Pain working party authorship group , Slater Rebeccah 
Published: June 2024

Abstract

Background: There are several major challenges limiting our ability to test analgesic efficacy for treatment of neonatal pain, and progress in analgesic drug studies in neonates has stalled. One significant issue is the reliance of clinical pain assessments on traditional behavioural and vital signs-based measures and the exclusion of novel brain-based biomarkers. In this review protocol, we outline our strategy to assess the reliability, validity, and interpretability of an electroencephalography (EEG)-based response biomarker for assessment of acute somatic nociceptive pain in neonates.
Methods: To standardise EEG analysis and generate the outcome of interest, we will perform an individual participant data (IPD) meta-analysis using data from neonates aged 34-44 weeks postmenstrual age that have had EEG recorded during acute somatic nociceptive skin-breaking procedures. Relevant data from both published and grey literature will be identified by searching six databases (MEDLINE, Embase, CINAHL, Web of Science, Scopus, Google Scholar), two clinical trial registry platforms (ClinicalTrials.gov, WHO ICTRP), and by consulting expert opinion. We will assess availability bias, data accuracy, and data quality by cross-referencing provided data with data descriptions in the literature, identifying duplicates and non-sensical values, and extracting quality control metrics. Data will be synthesised via a two-stage IPD meta-analysis using a random effects modelling approach grouped by site. Reliability (inter- and intra-rater) outcomes will be measured as Gwet's AC1 coefficient. Validity (known-groups and known-stimuli) outcomes will be measured as EEG response magnitude differences between clinically meaningfully different stimuli. Interpretability will be addressed by providing normative values, in both original and standardised units.
Discussion: The purpose of this study is to establish the reliability, validity, and interpretability of a specific EEG-based response biomarker for assessing acute somatic nociceptive pain in neonates. It will provide an overview of available data and how EEG is being used globally to assess acute neonatal pain. If sufficient IPD are made available and the outcome is reliable, valid, and interpretable, this work will support the use of EEG-based outcome measures as primary endpoints in clinical trials assessing analgesic efficacy in neonates.
Systematic review registration: The protocol was registered with PROSPERO on 14-July-2023: CRD42023444809.
A Coevolutionary Algorithm with Diffusive Growing Neural Gas Strategy for Many-Objective Optimization Problems with Irregular Pareto Front
Authors: WANG PENG , XUE MING , TONG XIANGRONG , LI CHENGZHEN 
Published: June 2024

Abstract

Due to the difficulty in balancing convergence, diversity and irregular Pareto fronts (PFs) handling simultaneously, most existing evolutionary algorithms face significant challenges on many-objective optimization problem with irregular PFs. To address these challenges, this article proposes a coevolutionary algorithm with diffusive growing neural gas (GNG) strategy for many-objective optimization problems with irregular Pareto front, called CoA-DGNG. Specifically, a coevolutionary mechanism is designed to collaborative facilitate both objective optimization and irregular PFs handling. Meanwhile, by utilizing the topology information of PFs, a diffusive growing neural gas node update strategy, named DGNG, is developed to guide the update of adaptive reference vectors. Moreover, a two-fold environmental selection strategy is presented to enhance selection pressure towards promising direction. The proposed algorithm is compared with four state-of-the-art optimizers on 45 test problems with irregular PFs. The experimental results demonstrate the superiority of the proposed CoA-DGNG in comparison to the chosen algorithms.
Ride-Hailing Pick-Up Area Recommendation in a Vehicle-Cloud Collaborative Environment: A Feature-Aware Personalized Clustering Federated Learning Approach
Authors: Zhou Xinyu , Liao ZhuHua , Zhao Yijiang , Liu Yizhi , Yi Aiping 
Published: June 2024

Abstract

Ride-hailing pick-up area recommendations based on GPS data can significantly enhance driver efficiency, improve passenger experience, and optimize urban traffic management. However, direct access to sensitive driver and passenger data, lack of data sharing between ride-hailing platforms, and other barriers severely undermine the reliability of current models and increase the difficulty of training and building these models. Additionally, the centralized mining of driver pick-up data can lead to spatial imbalances in ride-hailing platform order supplies, resulting in the recommendation of the same pick-up areas to different drivers in cases with similar spatiotemporal characteristics. This scenario not only exacerbates competition in certain areas but also impacts the overall service efficiency of the platform.To address these issues, this paper proposes a feature-aware personalized clustering federated learning model based on vehicle-cloud collaboration. This model achieves data sharing while protecting privacy, providing personalized pick-up area recommendations for drivers, and incorporating a feature-aware mechanism to prevent uneven order distribution. Specifically, the study introduces a clustering analysis algorithm based on Dynamic Time Warping (DTW) to accurately align and measure the similarity of drivers' order time series, classifying driver order-taking behavior patterns for more comprehensive behavior pattern analysis and improving model accuracy and reliability. Furthermore, we propose a deep matrix factorization algorithm, AFFM, that integrates driver features and geographical information. This algorithm deeply learns the interaction relationships between drivers and geographical features, modulates the attention weights of driver features, and constructs personalized models tailored to different driver pick-up characteristics, thus enhancing the precision and effectiveness of recommendations.Additionally, we present a federated learning algorithm, CFL\_AFFM, based on vehicle-cloud collaboration. This algorithm leverages cloud computing resources to compensate for the limited computational resources of vehicles, allowing the AFFM algorithm to effectively utilize DTW clustering results. By sharing global models and clustering parameters through the cloud platform, broader collaborative effects are achieved, enhancing the model's generalization ability and stability while protecting driver and passenger privacy.Finally, we design a reachability matrix that incorporates the driver's current location and features. Based on the predictions of the CFL\_AFFM algorithm, reliable pick-up areas are recommended, ensuring personalized and efficient ride-hailing services.
Investigation of factors associated with pediatric intention to vaccinate and vaccine hesitancy and refusal in pregnant women in Turkey
Authors: Yörük Selda , Güler Döndü 
Published: June 2024

Abstract

Background
Pregnant women are an ideal target population for interventions intended to immunize infants after birth. Lack of confidence in vaccines among pregnant women increases vaccine hesitancy or refusal.
Aim
This study aims to determine the frequency, causes, and risk factors of pediatric intention to vaccinate and vaccine hesitancy and refusal in pregnant women.
Methods
Data were collected using a questionnaire designed by the researchers and included items about descriptive characteristics of women and their vaccine hesitancy and refusal. The study was used with the STROBE Statement checklist.
Results
Pediatric vaccine acceptance in pregnant women was 72.2%, the prevalence of vaccine refusal was 3.4%, and the prevalence of vaccine hesitancy was 24.4%. The vaccine that women were hesitant to get administered to their infants in the first place after birth was the pneumococcal vaccine (17.2%), and Tdab was the vaccine that they intended to have administered to their infants the most(76.3%). According to the multivariate logistic regression model, which was adjusted for age, education, income, and gestational week, vaccine hesitancy was significantly higher in pregnant women (OR = 15.14) who stated that they would not have screening tests such as hearing and heel blood done after birth (p < 0.0001).Vaccine hesitancy was also high in pregnant women who did not use any folic acid during pregnancy (OR = 2.59) and those who had not received two doses of tetanus vaccine during pregnancy or within the last 10 years (OR = 1.75). It was statistically significantly 2.14 times higher in pregnant women who used social media as a source of information about vaccines (OR = 2.14) and those who stated that they did not trust the content of vaccines (OR = 22.08) (p < 0.05).
Conclusion
The risk of pediatric vaccine hesitancy was higher in pregnant women who did not use preventive interventions during the antenatal period.
Study on flow and heat transfer characteristics of microchannel with double-layer staggered trapezoidal convex rib cavity
Authors: Feng Yuxiang , Zhang Haoyu 
Published: June 2024

Abstract

This paper focuses on the numerical study of fluid flow and heat transfer characteristics in double-layer staggered trapezoidal convex rib and concave microchannel (SMCCR). The effects of base thickness ratio (α) and height ratio (β) on thermal performance were studied by numerical simulation. Comparison was made between SMCCR and rectangular straight microchannel (MC). The comprehensive performance of SMCCR was evaluated. The heat transfer enhancement mechanism of SMCCR is analyzed by using flow field, temperature field and pressure field. The results show that the heat transfer performance of SMCCR-2 microchannel is better than that of MC microchannel and MCCR-2 microchannel. When Re=279, among the seven designed channels, the PEC of SMCCR-2 channel with α=2 and β=0.5 structure reaches 2.35, and the comprehensive heat transfer effect is the best. When the thermal resistance Rth is the same, the SMCCR-2 channel with β=0.5 needs less pumping power and less energy consumption. By comparing this special structure microchannel with the traditional straight microchannel, the advantages and characteristics of double-layer staggered trapezoidal ribbed groove microchannel in heat transfer enhancement are analyzed, thus providing useful reference for practical engineering application.
Enhancing AutoML Performance for Imbalanced Tabular Data Classification: A Self-Balancing Pipeline
Authors: Aragão Marcelo Vinícius Cysneiros , Carvalho Mateus de Freitas , Pereira Tiago de Morais , de Figueiredo Felipe Augusto Pereira , Mafra Samuel Baraldi 
Published: June 2024

Abstract

In data science and machine learning, imbalanced data poses a significant challenge. This study presents a self-balancing strategy integrating traditional (randomly duplicating data from the minority class) and generative (creating novel samples from the minority class' features space oversampling, undersampling techniques, and hyperparameter optimization to enhance automated machine-learning pipelines. Through a systematic grid search methodology and by taking multiple datasets into account, the research validates the effectiveness of integrated sampling techniques in consistently improving classification performance in diverse scenarios. The proposed approach contributes to a refined understanding of addressing class imbalance, emphasizing the importance of a unified strategy. Therefore, this research presents a comprehensive proposal for effectively handling imbalanced datasets and advancing the development of more reliable machine learning applications. The experiments' results highlighted the effectiveness of sampling methods and showed improvements of up to almost six percentage points compared to the baseline. We made the code, experiment reproduction instructions, and outcomes publicly available on GitHub.
Estimations of biological motion walking direction are affected by observer and walker genders
Authors: Sun Qi , Dong Liang-Liang , You Fan-Huan , Chang Dorita H. F. 
Published: June 2024

Abstract

A facing-towards bias is commonly reported when observers are asked to judge the motion directions of others. However, it remains unclear just how accurately observers are able to estimate the motion direction of others. Here, we asked male and female participants to estimate the direction of a point-light walker (PLW) in three experiments. The gender of PLWs was neutral (Experiment 1, 96 participants), clearly male or female (Experiment 2, 72 participants), or more subtlety male or female (Experiment 3, 98 participants). We found that female PLWs showed a stronger reference repulsion bias (RRB) than male PLWs. That is, for female PLWs, the estimates of facing directions were biased away from the boundaries of facing-towards, facing-away, and lateral (left/right)-motion directions. Interestingly, RRBs differed depending on whether the observer was male or female. When the PLW gender difference was clear, the RRB was stronger for female participants than male participants; when the PLW gender difference was reduced, the trend disappeared or was reversed. Finally, the perceived PLW direction was biased towards the previously seen PLW direction, showing serial dependence that was not affected by the PLW and observer genders. In conclusion, the current study shows that observers can accurately estimate PLW directions, but that judgments are curiously affected by both the observers’ and PLWs’ genders.
Energy Efficiency Assessment in Advanced Driver Assistance Systems with Real-Time Image Processing on Custom Xilinx DPUs
Authors: TATAR Güner , BAYAR Salih 
Published: June 2024

Abstract

The rapid advancement in embedded AI, driven by integrating deep neural networks (DNNs) into embedded systems for real-time image and video processing, has been notably pushed by AI-specific platforms like the AMD Xilinx Vitis AI on the MPSoC-FPGA platform. This platform utilizes a configurable Deep Processing Unit (DPU) for scalable resource utilization and operating frequencies. Our study employed a detailed methodology to assess the impact of various DPU configurations and frequencies on resource utilization and energy consumption. The findings reveal that increasing the DPU frequency enhances resource utilization efficiency and improves performance. Conversely, lower frequencies significantly reduce resource utilization, with only a borderline decrease in performance. These trade-offs are influenced not only by frequency but also by variations in DPU parameters. These findings are critical for developing energy-efficient AI-driven systems in Advanced Driver Assistance Systems (ADAS) based on real-time video processing. By leveraging the capabilities of Xilinx Vitis AI deployed on the Kria KV260 MPSoC platform, we explore the intricacies of optimizing energy efficiency through multi-task learning in real-time ADAS applications.
Nitrogen and water availability effects dynamics of soil nitrogen mineralization in a maize system
Authors: Donovan Tyler C. , Comas Louise H. , Schneekloth Joel , Schipanski Meagan 
Published: June 2024

Abstract

Nitrogen (N) fertilizer and water availability can independently stimulate or limit soil N dynamics through direct and indirect processes. Importantly, soil N mineralization (Nmin) is a major N source for maize but affected by N fertilization and water availability. We examined in-situ net Nmin, soil enzyme activity, and maize N uptake in a semiarid region of North America in response to two levels of water availability (100% and 70% crop evapotranspiration, ET) and three levels of N fertilization (22–275 kg ha− 1 capturing low, optimal, and excess N fertilization. Nitrogen mineralization rates peaked relatively early in the growing season leading to asynchrony between soil N supply and plant demand. Later in the season when plant N uptake was highest, Nmin rates were high under low N with full water supply, and high under high N with limited water supply, resulting in an N fertilizer and water interaction. Soil L-leucine amino peptidase (LAP) and β-1,4-N-acetyl-glucosaminidase (NAG), which can be indicators of gross Nmin, increased with N fertilizer additions but were not affected by water supply. Further research is needed to understand the mechanisms underlying this interaction as well as exploring if gross Nmin has a similar response. Maize N uptake increased with N fertilizer additions under both levels of water availability but was higher in the full water supply. In the limited water availability, increased plant N uptake with increased N fertilization did not translate to large grain yield increases highlighting the impact of water stress, especially during grain fill.
Operando Neutron Imaging-guided Gradient Design of Li-ion Solid Conductor for Extremely High Mass-loading Cathodes
Authors: Zhu Hongli , Ji Tongtai , Zhang Yuxuan , Torres James , Bilheux Jean , Wang Jiwei 
Published: June 2024

Abstract

High mass-loading cathodes are crucial for achieving high energy density in all-solid-state batteries from lab scale to industry. However, as mass-loading increases, electrochemical performance is significantly compromised due to sluggish kinetics. Operando neutron imaging of a high mass-loading NMC 811 cathode of 33 mg/cm2 (5.0 mAh/cm2, 180 µm thick) reveals the lithiation prioritization of the cathode active material (CAM) from the solid electrolyte layer to the current collector side. In addition to the tortuosity, another key limitation to ion transfer in the cathode arises from the mismatch between the uniform distribution of the solid electrolyte (catholyte) in the conventional composite cathode and the non-uniform Li+ flux generated by the Faraday reaction of CAMs. Therefore, a novel design with a gradient in the catholyte concentration is engineered to match the Li+ flux distribution, aiming to eliminate the ion transfer obstacle. This innovative approach demonstrates enhanced rate performance, even with ultra-high mass-loading cathodes. A LiCoO2 composite cathode with 100 mg/cm2 ultra-high mass-loading exhibited an areal capacity of 10.4 mAh/cm2 at a current density of 2.25 mA/cm2. This work demonstrated an effective gradient design to optimize ion transport in high mass-loading cathodes to overcome the kinetic barrier and achieve high battery performance.
Adamaoua Under the Prism of Cross-border Rural Banditry
Authors: Alim Bakari Yaya 
Published: June 2024

Abstract

The Adamaoua region has been the scene of intensive subversive activities by armed groups from conflict-prone areas of the Central African Republic and Nigeria in recent years. Instigators of insecurity have transformed this region into an ungovernable space, a stronghold where they deploy innovative strategies to dispossess populations. Rural banditry prevails in rural areas, under the configuration of kidnappings accompanied by ransom demands, ambushes of rural roads, and cattle theft, illustrating the worsening security situation. The question is how do we understand this security threat and how have the authorities reacted ? The hypothesis put forward aims to demonstrate that rural banditry in the Adamaoua region is influenced by regional geography, socio-economic factors, and the crisis of governance security at the borders. The situation has led to the emergence of sovereignty actions in Cameroon and security cooperation with Central African Republic and Nigeria.
Contribution of GIS and remote sensing in evaluating groundwater potential zoning in Mbandjock
Authors: Bah Ntutuwouo Gael Didier , Rosine Eve Njogi Bella Anne , Djousse Kenouo Boris Merlain , Motchemien Rigobert , Fonteh Mathias Fru 
Published: June 2024

ABSTRACT
Geographical information systems present today an undisputed advantage for the management of natural resources to facilitate their management. As a matter of fact, this study aims to evaluate the water potential in the water tables of Mbandjock using the multivariable analytical hierarchy process method applied on GIS. To do so, we first determined the different contributing variables: in this study, we use drainage density, rainfall distribution, soil geology, variation in slopes, soil type, land use, and lineament density. Once the different factors were mapped and reclassified, which led us to the second part of this work, where it was a question of overlaying these different variables by associating each variable with a weight value proportional to its importance in relation to other variables. This led to the production of a distribution map of groundwater potential.
Metagenomic analysis reveals differential effects of sewage treatment on the microbiome and antibiotic resistome in Bengaluru, India
Authors: Nataraj Annamalai , Mondhe Divya , Srikantaiah Vishwananth , Ishtiaq Farah 
Published: June 2024

ABSTRACT

Climate change and health are closely linked to urban wastewater used for irrigation. Sewage treatment plants (STPs) provide ideal environments and niche availability for the transmission of antibiotic resistance genes (ARGs) among pathogenic and non-pathogenic bacteria. In this study, we examined the differential effect of sewage processing methods from the inlet to the outlet on the microbial diversity and antibiotic resistomes of 26 STPs in the urban sewage network of Bengaluru, India. We screened 478 ARGs and found 273 in wastewater, including clinically relevant genes such as CTX-M, qnr, sul-1, and NDM-1, which confer resistance to six major classes of antibiotics. The richness of ARGs was higher in sewage inlets compared with outlets. We observed a downward shift in drug classes from inlet to outlet samples, except for aminoglycosides, beta-lactams, MLSB, and tetracycline. Inlet samples exhibited more complex correlations between ARGs and bacteria compared with outlet samples. Our findings serve as a baseline study that could aid in the quantification of genes from both culturable and non-culturable taxa and will assist in the development of policies and strategies to address water quality issues associated with the use of recycled water.