bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bilinear sparse domination for oscillatory integral operators
Authors: Mattsson Tobias 
Published: April 2024

AbstractIn this paper, we prove bilinear sparse domination bounds for a wide class of Fourier integral operators of general rank, as well as oscillatory integral operators associated to Hörmander symbol classes $$S^m_{\rho ,\delta }$$

S

ρ
,
δ

m

for all $$0\le \rho \le 1$$

0
≤
ρ
≤
1

and $$0\le \delta < 1$$

0
≤
δ
<
1

, a notable example is the Schrödinger operator. As a consequence, one obtains weak (1, 1) estimates, vector-valued estimates, and a wide range of weighted norm inequalities for these classes of operators.
A Computational Investigation of the Influence of Seafloor Conditions on the Turbulent Flow Characteristics of an Autonomous Underwater Vehicle
Authors: Tabatabaei Malazi Mahdi , Tumse Sergen , Ozgoren Muammer , Sahin Besir 
Published: April 2024

AbstractThe effect of the seabed on the hydrodynamics of three-dimensional autonomous underwater vehicles (AUVs) varies according to the physical conditions of the place where AUVs interact with the environmental conditions. This study examines the hydrodynamics of an AUV resembling a torpedo model while taking the influence of the seabed surface as a function of the dimensionless distances (G/D) between the torpedo and the seabed. Reynolds numbers, varying from 1 × 104 to 8 × 104, were considered. These Reynolds numbers were associated with various seabed distances falling within 0.25 ≤ G/D ≤ 1.5. To perform the simulations, governing equations were utilized and incorporated with the k–ω SST turbulence model. It has been observed that when AUVs or torpedo models operate in close proximity to the seabed surface, several key hydrodynamic parameters and flow characteristics are affected. These include the pressure coefficient (Cp), drag coefficient (CD), overall flow structures, maneuverability, and performance of the torpedo model. As the AUV or torpedo model approaches the seabed surface, the symmetrical flow pattern deteriorates. This deterioration is associated with changes in vortical flow structures under the influence of seabed surfaces. Additionally, the intensity of the shear stress (τ) near the seabed surface gradually increases as the AUV or torpedo model gets closer to it. In summary, the proximity of AUVs or torpedo models to the seabed surface causes disruptions in the flow patterns, increased shear stress, and alterations in key hydrodynamic parameters, ultimately affecting the system's performance and behavior.
Event Ethnography to study the global negotiations on the treaty to end plastic pollution: dataset from the first session of negotiations (INC-1)
Authors: Cowan Emily 
Published: April 2024

AbstractPlastics and their associated chemical pollution have emerged as a global threat. This recognition materialized in March 2022 with the adoption of a groundbreaking mandate among United Nations (UN) Member States, signaling the commencement of negotiations for a comprehensive treaty aimed at ending plastic pollution, encompassing the entire lifecycle. The first session of negotiations convened at the International Negotiating Committee (INC-1) in Punta del Este, Uruguay from November 28th–December 2nd, 2022, from there, four other negotiation sessions have either convened or will, with the expected conclusion in December 2024. The current study introduces a methodology for systematically observing and documenting global agreement-making. It utilizes a dataset from the first session of negotiations to explore the event ethnography (EE) methodology for following Multilateral Environmental Agreements (MEA). This paper aims to inspire and guide researchers in employing EE, recognizing its efficacy in navigating the complexities of the extensive Global Plastics Treaty (GPT) negotiations and promoting collaborative efforts for a comprehensive understanding of the process. EE is a method increasingly recognised to study multilateral international negotiations within the science-policy nexus to examine emerging obstacles, trends, power dynamics, and actors (both state and non-state) in action within the negotiations. Finally, a dataset is presented from INC-1 through a practical Excel document; the article then demonstrates one example of how the dataset can be employed to enhance comprehension of the negotiators and observers influencing the GPT negotiations.
Sex differences in Parkinson’s disease-related non motor symptoms: a focus on sleep problems
Authors: Bovenzi Roberta , Conti Matteo , De Franco Valentino , Pierantozzi Mariangela , Schirinzi Tommaso , Cerroni Rocco , Stefani Alessandro , Mercuri Nicola Biagio , Liguori Claudio 
Published: April 2024

AbstractParkinson’s disease (PD) symptomatology differs between females and males, yet the contribution of sex on sleep problems needs further analysis. Here, we aimed to investigate sex-specific patterns in the relationship between sleep problems, assessed using the Parkinson’s disease sleep scale (PDSS-2), non motor symptoms (NMS), measured by the NMS scale (NMSS), and health-related quality of life (HR-QoL), evaluated by the Parkinson’s disease questionnaire (PDQ-39), in a large cohort of PD patients. One-hundred-fifty-four PD patients were included in the study. Female PD patients (n = 62) exhibited a higher prevalence of sleep problems than males (n = 92), with nocturnal motor-related sleep issues being the most frequent. Sleep disturbances differently correlated with a range of NMS between the two sexes. In females, sleep problems mostly correlated with pain; on the other hand, sleep disturbances were linked to a frailer phenotype characterized by global dysautonomia, perception disturbances, and impaired cognitive function in males. Whether female PD patients experienced a lower HR-QoL than males, sleep disturbances were associated with a worse HR-QoL in both sexes. In conclusion, sleep problems in PD differently burden the two sexes, suggesting possible different etiopathogenesis, diagnostic investigations, and possibly tailored approaches.
Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function
Authors: Heyat Md Belal Bin , Akhtar Faijan , Munir Farwa , Sultana Arshiya , Muaad Abdullah Y. , Gul Ijaz , Sawan Mohamad , Asghar Waseem , Iqbal Sheikh Muhammad Asher , Baig Atif Amin , de la Torre Díez Isabel , Wu Kaishun 
Published: April 2024

AbstractDepression is a multifactorial disease with unknown etiology affecting globally. It’s the second most significant reason for infirmity in 2020, affecting about 50 million people worldwide, with 80% living in developing nations. Recently, a surge in depression research has been witnessed, resulting in a multitude of emerging techniques developed for prediction, evaluation, detection, classification, localization, and treatment. The main purpose of this study is to determine the volume of depression research conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, and associations with other mental disorders like anxiety and stress using traditional and medical intelligence (medical with AI). In addition, it also designs a comprehensive survey on detection, treatment planning, and genetic predisposition, along with future recommendations. This work is designed through different methods, including a systematic mapping process, literature review, and network visualization. In addition, we also used VOSviewer software and some authentic databases such as Google Scholar, Scopus, PubMed, and Web of Science for data collection, analysis, and designing comprehensive picture of the study. We analyzed 60 articles related to medical intelligence, including 47 from machine learning with 513,767 subjects (mean ± SD = 10,931.212 ± 35,624.372) and 13 from deep learning with 37,917 subjects (mean ± SD = 3159.75 ± 6285.57). Additionally, we also found that stressors impact the brain's cognitive and autonomic functioning, resulting in increased production of catecholamine, decreased cholinergic and glucocorticoid activity, with increased cortisol. These factors lead to chronic inflammation and hinder the brain's normal functioning, leading to depression, anxiety, and cardiovascular disorders. In the brain, reactive oxygen species (ROS) production is increased by IL-6 stimulation and mitochondrial cytochrome c oxidase is inhibited by nitric oxide, a potent inhibitor. Proteins, lipids, oxidative phosphorylation enzymes, and mtDNA are further disposed to oxidative impairment in the mitochondria. Consequently, mitochondrial dysfunction exacerbates oxidative stress, impairs mitochondrial DNA (mtDNA) or deletions of mtDNA, increases intracellular Ca2+ levels, changes in fission/fusion and mitochondrial morphology, and lastly leads to neuronal death. This study highlights the multidisciplinary approaches to depression with different aspects using traditional and medical intelligence. It will open a new way for depression research through new emerging technologies.
Development of a Tube-Based Elastocaloric Regenerator Loaded in Compression: A Review
Authors: Dall’Olio Stefano , Ahčin Žiga , Žerovnik Andrej , Kabirifar Parham , Brojan Miha , Tušek Jaka 
Published: April 2024

AbstractIn recent years, devices based on the elastocaloric effect (eCE) have emerged as one of the most promising alternatives to vaporcompression cooling and heating systems. After a brief overview of elastocaloric materials and elastocaloric devices developed to date, this paper reviews our recent activities in the development of tube-based elastocaloric regenerators loaded in compression. These include the evaluation of novel elastocaloric thermodynamic cycles, the characterization of the elastocaloric and fatigue behavior of Ni–Ti tubes, the thermo-hydraulic evaluation of a tube-based geometry to be applied as an elastocaloric regenerator, the buckling analysis of Ni–Ti tube in compression, and finally the development, numerical modeling, and testing of a tube-based elastocaloric regenerator in both cooling and heat-pumping modes. The developed regenerator shows a durable operation with more than 300,000 cycles, a maximum temperature span of more than 31 K at zero thermal load, and a heating power of more than 60 W at a temperature span of 10 K using only 13.7 g of elastocaloric material. In addition, further improvements of the tube-based elastocaloric regenerators related to a lower thermal mass of the regenerator’s housing are shown and discussed.
Asymmetric effects of macro policies on women’s and men’s incomes. An empirical investigation of the eurozone crisis in a gender perspective
Authors: Cirillo Valeria , Corsi Marcella , D’Ippoliti Carlo , Gobbi Lucio 
Published: April 2024

AbstractWe study how macro policies affected women’s and men’s incomes during the financial crisis in Europe. We consider the monetary stance, proxied by benchmark interest rates, and the fiscal stance, measured by the variation in public expenditures and public revenues, and investigate how they are associated to women’s and men’s labor and capital incomes, using microdata for 27 European countries between 2008 and 2016. We individualize household-level data by considering four scenarios of intra-household sharing of resources. We also explore how and to what extent macro-policies affect the distribution of labour incomes for men and women by applying a conditional quantile regression approach. Results highlight that the ECB’s expansionary policies had a positive effect on both labor and capital incomes for both men and women, while austerity policies had a mixed impact. Reductions in public expenditure had the effect of reducing labor incomes for both men and women, particularly at the median of the wage and labor distributions. In contrast, increases in public revenues benefited capital incomes, for all income quantiles.
Perovskite Oxides Toward Oxygen Evolution Reaction: Intellectual Design Strategies, Properties and Perspectives
Authors: Liu Lin-Bo , Yi Chenxing , Mi Hong-Cheng , Zhang Song Lin , Fu Xian-Zhu , Luo Jing-Li , Liu Subiao 
Published: April 2024

AbstractDeveloping electrochemical energy storage and conversion devices (e.g., water splitting, regenerative fuel cells and rechargeable metal-air batteries) driven by intermittent renewable energy sources holds a great potential to facilitate global energy transition and alleviate the associated environmental issues. However, the involved kinetically sluggish oxygen evolution reaction (OER) severely limits the entire reaction efficiency, thus designing high-performance materials toward efficient OER is of prime significance to remove this obstacle. Among various materials, cost-effective perovskite oxides have drawn particular attention due to their desirable catalytic activity, excellent stability and large reserves. To date, substantial efforts have been dedicated with varying degrees of success to promoting OER on perovskite oxides, which have generated multiple reviews from various perspectives, e.g., electronic structure modulation and heteroatom doping and various applications. Nonetheless, the reviews that comprehensively and systematically focus on the latest intellectual design strategies of perovskite oxides toward efficient OER are quite limited. To bridge the gap, this review thus emphatically concentrates on this very topic with broader coverages, more comparative discussions and deeper insights into the synthetic modulation, doping, surface engineering, structure mutation and hybrids. More specifically, this review elucidates, in details, the underlying causality between the being-tuned physiochemical properties [e.g., electronic structure, metal–oxygen (M–O) bonding configuration, adsorption capacity of oxygenated species and electrical conductivity] of the intellectually designed perovskite oxides and the resulting OER performances, coupled with perspectives and potential challenges on future research. It is our sincere hope for this review to provide the scientific community with more insights for developing advanced perovskite oxides with high OER catalytic efficiency and further stimulate more exciting applications.
Graphical Abstract
Encouraging Positive Bystander Responses to Bias-Based Bullying in Primary Schools Through a Serious Game Approach: A Non-Randomized Controlled Evaluation of the ‘GATE-BULL’ Program
Authors: Willems Roy A. , Sapouna Maria , De Amicis Leyla , Völlink Trijntje , Dehue Francine , Dimakos Ioannis , Priovolou Konstantina , Nikolaou Georgios , Rosinský Rastislav 
Published: April 2024

AbstractBias-based bullying is a serious public health issue, negatively impacting on the well-being of children with socially devalued identities or attributes. The aim of the GATE-BULL project was to develop and evaluate a serious game supplemented by a series of classroom-based lesson plans to encourage positive bystander responses in weight-, ethnicity-, and religion-based bullying situations. The program was a 4-week school-based intervention targeted at 9–13-year-olds and tested using a quasi-experimental design. In total, 578 children from the Netherlands, Scotland and Greece were included in the analyses. The evaluation suggested that the GATE-BULL program improved the intention to intervene in weight-based bullying situations in Scotland and marginally contributes to a reduction in intergroup anxiety among majority group children with respect to Muslim children in the Netherlands. No positive effects were found in Greece. While the program showed limited evidence of effectiveness, several important lessons have been learned for future development of interventions targeting bias-based bullying.
Cn2a-capsnet: a capsule network and CNN-attention based method for COVID-19 chest X-ray image diagnosis
Authors: Zhang Hui , Lv Ziwei , Liu Shengdong , Sang Zhenlong , Zhang Zehua 
Published: April 2024

AbstractDue to its high infectivity, COVID-19 has rapidly spread worldwide, emerging as one of the most severe and urgent diseases faced by the global community in recent years. Currently, deep learning-based diagnostic methods can automatically detect COVID-19 cases from chest X-ray images. However, these methods often rely on large-scale labeled datasets. To address this limitation, we propose a novel neural network model called CN2A-CapsNet, aiming to enhance the automatic diagnosis of COVID-19 in chest X-ray images through efficient feature extraction techniques. Specifically, we combine CNN with an attention mechanism to form the CN2A model, which efficiently mines relevant information from chest X-ray images. Additionally, we incorporate capsule networks to leverage their ability to understand spatial information, ultimately achieving efficient feature extraction. Through validation on a publicly available chest X-ray image dataset, our model achieved a 98.54% accuracy and a 99.01% recall rate in the binary classification task (COVID-19/Normal) on a six-fold cross-validation dataset. In the three-class classification task (COVID-19/Pneumonia/Normal), it attained a 96.71% accuracy and a 98.34% recall rate. Compared to the previous state-of-the-art models, CN2A-CapsNet exhibits notable advantages in diagnosing COVID-19 cases, specifically achieving a high recall rate even with small-scale datasets.
How Does It Feel to Leave Your Protest?
Authors: Ratkos Thom 
Published: April 2024

AbstractSocial justice movements, protests, marches, and direct action are collective events including many people. Although organizers should carefully plan the goals and strategies of these actions broadly, and behavior analysts have made significant progress to systems level analyses of social and cultural practices, it is important to continue to include analyses of the individuals involved and how the contingencies of protest may encourage or discourage continued involvement. One critical aspect of these contingencies, I argue, is how the action ends. Two protest actions are analyzed as a participant with a focus on how the protest ended and if the operant “attending a protest” was reinforced or punished.
Fabrication and Physico-mechanical Characterization of Short Natural/Synthetic Fiber–Reinforced Hybrid Composites: Effects of Biodegradation and Chemical Aging
Authors: Rahman S. , Das Subrata C. , Saha J. , Khan Mubarak A. 
Published: April 2024

AbstractThe main goal of this study was to develop eco-friendly and low-cost multiple short natural fiber-reinforced hybrid composites with the hybridization of comparatively high-strength glass fibers. The hybrid composites were fabricated via hand lay-up by using short jute, silk, water hyacinth, and glass fibers for the reinforcements and unsaturated polyester resin for the thermoset polymer matrix. The reinforcing fibers were randomly oriented, and five types of hybrid composites were fabricated with different types of fiber content (wt.%). The performance of the manufactured hybrid composites was assessed by tensile, flexural, and impact testing, as well as water uptake (%). It was revealed that composites with high glass fiber content (wt.%) exhibited optimum mechanical performance in most cases, while poor moisture resistance performance was exhibited for the hybrid composites containing higher natural fibers (wt.%). The hybrid composite samples were also aged in soil medium (biodegradation) for 25 days and different chemical solutions (alkaline, acidic, and salt) for 10 days. After biodegradation, the drop of tensile strength (TS) and tensile modulus (TM) was revealed to be approximately 38–61 and 58–72%, respectively. On the other hand, after chemical aging, the drop of TS and TM was exhibited to be approximately 49–76% and 51–65%, respectively, for alkali solution aging; 42–75% and 29–76%, respectively, for acid solution aging; and 43–59% and 51–65%, respectively, for salt solution aging.
Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption
Authors: Al-Rajab Murad , Loucif Samia 
Published: April 2024

AbstractIn a world where electricity is often taken for granted, the surge in consumption poses significant challenges, including elevated CO2 emissions and rising prices. These issues not only impact consumers but also have broader implications for the global environment. This paper endeavors to propose a smart application dedicated to optimizing the electricity consumption of household appliances. It employs Augmented Reality (AR) technology along with YOLO to detect electrical appliances and provide detailed electricity consumption insights, such as displaying the appliance consumption rate and computing the total electricity consumption based on the number of hours the appliance was used. The application utilizes Linear Regression as a machine learning (ML) algorithm to develop the electricity consumption forecasting model for the next months, based on past utility bills. Linear regression is often considered one of the most computationally lightweight ML algorithms, making it suitable for smartphones. The application also offers users practical tips for optimizing their electricity consumption habits.
An Algorithm for the Recognition of Motion-Blurred QR Codes Based on Generative Adversarial Networks and Attention Mechanisms
Authors: Dong Hao , Liu Haibin , Li Mingfei , Ren Fujie , Xie Feng 
Published: April 2024

AbstractMotion blur can easily affect the quality of QR code image, making it difficult to recognize QR codes on moving objects. This paper proposes an algorithm for the recognition of motion-blurred QR codes based on generative adversarial network and attention mechanism. Firstly, a multi-scale feature extraction framework for motion defuzzification is designed using deep convolutional neural networks, and enhanced multi-scale residual blocks and multi-scale feature extraction modules are utilized to capture rich local and global features. Secondly, the efficient channel attention module is added to strengthen the weights of effective features and suppress invalid features by modeling the correlations between channels. In addition, training stability is achieved through the use of the WGAN-div loss function, leading to the generation of higher quality samples. Finally, the proposed algorithm is evaluated through qualitative and quantitative comparisons with several recent methods on both the GOPRO public dataset and a self-constructed QR code dataset, respectively. The experimental results demonstrate that, compared to the other methods, the proposed algorithm has shown significant improvements in both processing time and recognition accuracy when dealing with the task of severe motion-blurred QR code recognition.