bluuebunny/crossref_metadata_2025_split huggingface dataset:
Moderation effects of health behaviors on stress and suicidal ideation in adolescents: a cross-sectional survey during COVID-19
Authors: O’Rourke Teresa , Humer Elke , Plener Paul L. , Pieh Christoph , Probst Thomas 
Published: December 2023

AbstractThis cross-sectional online survey study investigated whether certain health behaviors moderated the relationship between perceived stress and suicidal ideation in Austrian adolescents during the COVID-19 pandemic. A total of 1505 14–20-year-old (median age = 16) high school students (77.9% female) filled out an online survey from September to November 2021. Perceived stress was measured with the PSS10, suicidal ideation with item 9 of the PHQ-9. The following health behaviors were assessed: Physical activity (days/week), phone use (hours/day), problematic drinking behavior (CAGE). All three health behaviors significantly moderated the relationship between perceived stress and suicidal ideation (all p < .05), but effects were small. The moderation analyses revealed that higher physical activity and less time spent on the phone were associated with less suicidal ideation at higher stress levels. Showing signs of problematic drinking behavior was associated with higher suicidal ideation at higher stress levels. In conclusion, these results suggest that some health behaviors may be able to act as a buffer between perceived stress and suicidal ideation. However, more research is needed to confirm these potentially buffering effects.
Organ-specific characteristics govern the relationship between histone code dynamics and transcriptional reprogramming during nitrogen response in tomato
Authors: Julian Russell , Patrick Ryan M. , Li Ying 
Published: December 2023

AbstractEnvironmental stimuli trigger rapid transcriptional reprogramming of gene networks. These responses occur in the context of the local chromatin landscape, but the contribution of organ-specific dynamic chromatin modifications in responses to external signals remains largely unexplored. We treated tomato seedlings with a supply of nitrate and measured the genome-wide changes of four histone marks, the permissive marks H3K27ac, H3K4me3, and H3K36me3 and repressive mark H3K27me3, in shoots and roots separately, as well as H3K9me2 in shoots. Dynamic and organ-specific histone acetylation and methylation were observed at functionally relevant gene loci. Integration of transcriptomic and epigenomic datasets generated from the same organ revealed largely syngenetic relations between changes in transcript levels and histone modifications, with the exception of H3K27me3 in shoots, where an increased level of this repressive mark is observed at genes activated by nitrate. Application of a machine learning approach revealed organ-specific rules regarding the importance of individual histone marks, as H3K36me3 is the most successful mark in predicting gene regulation events in shoots, while H3K4me3 is the strongest individual predictor in roots. Our integrated study substantiates a view that during plant environmental responses, the relationships between histone code dynamics and gene regulation are highly dependent on organ-specific contexts.
Peer-Mediated Intervention for Socially Isolated Preschoolers: An Early-Stage Feasibility Study
Authors: Foster Tiffany J. , Xiao Nan , Pelfrey G. Logan , Villasanti Hugo Gonzalez , Brock Matthew , Justice Laura 
Published: December 2023

Purpose:
A common goal of peer-mediated interventions is to train peers to successfully initiate and maintain social and linguistic interactions with a target child in the classroom. Ample evidence indicates that peer-mediated interventions improve social and linguistic outcomes for students in the primary and later grades with developmental disabilities; however, relatively little work has focused on applying these approaches to socially isolated preschoolers who are vulnerable to academic and social challenges. In this early-stage feasibility study, we examined the potential of Promoting Early Engagement, Relationships, and Socialization (PEERS), a peer-mediated intervention designed to support socially isolated preschoolers. The main goal was to consider whether the intervention showed promise for improving the social and linguistic experiences of young, isolated children and was appropriate for implementation in a preschool setting.

Method:
In one preschool classroom, two peers who were well connected to their classmates were trained via storybook-based activities to provide social support to an isolated classmate. In-person and sensing technology observations were used to examine the target student's incoming peer interactions, outgoing peer interactions, and physical proximity to peers.

Results:
In-person observations showed a trend toward increased interactions; sensing technology data, which provided longer snapshots of classroom experiences, were more variable. In addition, teachers perceived PEERS to be appropriate and effective.

Conclusion:
The findings of this study support further investigation of storybook-based peer-mediated interventions in preschool settings to improve the social and linguistic experiences of children who are socially isolated.
A dynamic alignment and illumination‐aware convolution for shadow removal
Authors: Wang Xingqi , Dai Jialai , Chen Bin , Wei Dan , Shao Yanli 
Published: December 2023

AbstractShadow removal is a challenging task because the variety of shadows is influenced by surface texture and lighting. This paper proposes a dynamic alignment and illumination‐aware convolution (DAIC), which consists of a Feature Alignment Module (FAM) and a Dynamic Weight Module (DWM). FAM aligns the downsampled deep features with the original features and helps to extract the optimal local information to ensure that the object texture features are not corrupted. DWM generates weights according to different lighting variations for a better shadow removal result. The shadow removal approach is based on an image decomposition algorithm using a multi‐exposure image fusion model. Here, the shadow removal network and refinement network use U‐Net framework, and the transposed convolution operations are replaced with DAIC in the decoder part of U‐Net to improve the performance of the network. The experiments are conducted on two large shadow removal datasets, ISTD+ and SRD. Compared to state‐of‐the‐art methods, this model achieves optimal performance in terms of Root Mean Square Error (RMSE) for the non‐shadow region. It also achieves performance comparable to the state‐of‐the‐art method in terms of RMSE for the shadow region and structural similarity index measurement for the entire image.
Intelligent design of network multimedia using big data and virtual Artificial Intelligence technology
Authors: Zhang Xin 
Published: December 2023

AbstractIn recent years, with the rapid development of the Internet of Things and communication technology, the application of network multimedia has become increasingly popular. At present, network multimedia has become a new type of mass media. Due to its interchangeability and extensive dissemination, it creates favourable conditions for information dissemination and acceptance in terms of vision, hearing, touch etc. However, at the same time, there are also some problems. The network multimedia does not have enough original information and the information retrieval is not intelligent, so this has caused great trouble to users. In order to solve these problems, the authors attempted to use the combination of Big Data (BD) and virtual Artificial Intelligence (AI) technology to realise the intelligent design of network multimedia. Therefore, based on the theoretical analysis, the authors also verified the effect of BD and virtual AI technology on the intelligent design of network multimedia. It was found that the intelligent design method proposed by the authors had good application effect in network multimedia, and could effectively improve the information retrieval speed of network multimedia. The query time of this method was 1.87 s less than that of the traditional method when the amount of information was 1000 and 18.16 s less than that of the traditional method when the amount of information was 40,000. At the same time, this method could also improve the accuracy of network multimedia information recommendation, and make network multimedia have better sharing, so as to provide more services for users. In addition, the discussion of BD and virtual AI technology in network multimedia intelligent design could also broaden the application scope of the Internet of Things and promote its better development.
Joint transceive beamforming for multistatic radar system by semi‐definite relaxation method
Authors: Li Haoran , Geng Jun , Xie Junhao 
Published: December 2023

AbstractThe joint transmit and receive beamforming algorithm has been envisioned to optimise the target's signal‐to‐interference‐plus‐noise ratio by co‐designing the transmit and receive beamforming vectors simultaneously. However, the traditional design concepts for this algorithm only consider the monostatic radar system, which may not work as well for the multistatic radar system. The authors present a novel transmit and receive beamforming algorithm for the multistatic radar system that includes a common transmitter and multiple receivers. The transmit beamforming vector can directly influence the output signal‐to‐interference‐plus‐noise ratio of each radar subsystem. To ensure the balanced performance of the subsystems, the authors propose a weighted sum of the signal‐to‐interference‐plus‐noise ratio optimisation problem to co‐design the transmit and receive beamforming. The proposed problem is non‐convex, and the authors construct an iterative algorithm to solve it using semi‐definite relaxation and slack‐variable replacement techniques. By using pre‐determined weights, the output performance of each radar subsystem can be effectively regulated. The simulation results confirm that the proposed algorithm can ensure better output signal‐to‐interference‐plus‐noise ratio for the entire multistatic radar system than the conventional joint transmit and receive beamforming algorithm.
Assessing the impact of triaxial strain on carrier mobility and dielectric properties in cubic CsBCl3 (B = Pb, Sn, or Ge) perovskites: A first principles study
Authors: Yadav Kiran , Ray Nirat 
Published: December 2023

Investigating the strain and pressure dependence of perovskite materials can provide valuable insights into their structural and electronic responses, enabling the fine-tuning of their properties for various technological applications. This study investigates the influence of controlled lattice compression and expansion on the acoustic phonon-limited carrier mobility in CsBCl3 (B = Pb, Sn, Ge) perovskites, revealing tunable electronic bandgaps ranging from 0.3 to 1.2 eV by varying the B cation type and applied triaxial strain. The research demonstrates significant and monotonic carrier mobility modulation under pressure, with changes of up to 124% even at modest strain levels of −2%, along with linearly increasing exciton binding energy with lattice parameter expansion.
Setting research priorities for English as an Additional Language
Authors: Chalmers Hamish , Faitaki Faidra , Murphy Victoria A. 
Published: December 2023

Abstract
We report a priority setting partnership (PSP), which sought to establish the priorities for new research among research users (educators, parents, and learners) in the field of English as an Additional Language (EAL). A steering committee consisting of members of these research user groups was established to oversee the project. An online ‘uncertainty questionnaire’ was distributed to research users inviting them to identify areas of their practice about which they are uncertain. These ‘raw’ uncertainties were consolidated and converted into a list of research questions, which was then distributed to the same groups to rank in order of priority. The 25 highest ranked questions were then discussed and debated in a workshop, consisting of research user representatives, to collectively and democratically produce a final Top 10 list of questions. The Top 10 shows what EAL research users regard as priorities, which we hope will inform research agendas and funding decisions.
Temperature gradient and asymmetric steady state correlations in dissipatively coupled cascaded optomechanical systems
Authors: Pellitteri Claudio , Palma G Massimo , Lorenzo Salvatore 
Published: December 2023

Abstract
The interaction between a light mode and a mechanical oscillator via radiation pressure in optomechanical systems is an excellent platform for a multitude of applications in quantum technologies. In this work we study the dynamics of a pair of optomechanical systems interacting dissipatively with a wave guide in a unidirectional way. Focusing on the regime where the cavity modes can be adiabatically eliminated, we derive an effective coupling between the two mechanical modes and explore the classical and quantum correlations established between the modes in both the transient and the stationary regime, highlighting their asymmmetrical nature due to the unidirectional coupling. Noteworthy, we find that a constant amount of steady correlations can exist at long times. Furthermore we show that this unidirectional coupling establishes a temperature gradient between the mirrors, depending on the frequencies’ detuning. We additionally analyze the power spectrum of the output guide field and we show how, thanks to the chiral coupling, from such spectrum it is possible to reconstruct the spectra of each single mirror.
Driver–passenger guidance model for Chinese freeway service areas based on path selection and parking delay
Authors: Li Xia , Liang Yaxin , Guo Peilun , Ma Xinwei , Liu Yimei 
Published: December 2023

Abstract
The driver–passenger guidance model proposed in this article is designed for large-scale freeway service areas that integrate various services such as shopping, leisure and catering. The model focuses on analyzing the impact of perceived travel distance and parking delay on path selection and parking space selection to optimize passenger flow efficiency. A perceived travel distance function is used to construct a pedestrian path selection model whose parameters are calibrated by a stated preference survey. An optimal parking space selection model is established based on the analytic hierarchy process-technique for order of preference based on similarity to the ideal solution method and considers parking distance, outdoor walking distance, indoor perceived travel distance after parking, and the impact of pedestrian crossings on parking delays. The model also calculates the vehicle arrival rate with parking time and the critical gap to ensure pedestrian safety. A parking delay calculation model is established based on the acceptable gap theory. The model is verified using the Yihezhuang service area of the Xiongan freeway as an example. The results show that the pedestrian path selection model improves passenger flow balance and reduces the variance in road passenger flow by 12% and per capita travel time by 9%. The proposed parking delay calculation model reduces parking delays by 30 s compared to models that do not consider pedestrian crossings, indicating its smoothness and convenience.
Conformational gap and heat capacity peaks of short lattice polymers
Authors: Tanouye Fernando Takeshi 
Published: December 2023

Abstract
From a simple and exact approach, we investigated the thermodynamic behavior of short HP-polymers (composed of hydrophobic and polar coarse-grained monomers) on the square lattice, for lengths N < 20 residues. In particular, we analyzed the heat capacity excitation peak, showing the strong correlation between its occurrence, at low temperatures, and a sharp conformational gap from ground states to first excited states (

Ω

1

/

Ω

0

≫
1

). This relation is observed for both homopolymers and heteropolymers.
Optimizing deep learning-based piled-up pulse height correction method for high radiation-field application
Authors: Kim W. , Ko K. , Lee S. , Park J. , Song G. , Lim K. , Cho G. 
Published: December 2023

Abstract
In high-radiation environments, measured pile-up pulses can lead to unavoidable issues such as total count loss and spectrum distortion. Additionally, the recording of large volumes of data within a short period makes real-time processing difficult. In this study, a deep learning-based pulse height estimation (PHE) method was optimized to perform pile-up signal correction in high-radiation fields. First, we adopted a previous deep-learning-based PHE method that allows for fast correction without being restricted to specific detectors. However, the peak-finding method was slightly modified to improve the count restoration rate. Moreover, the input data length of the deep learning model was optimized for convolutional neural networks (CNN) and deep neural networks (DNN) to achieve the maximum correction performance using minimal input data. A series of single pulses was experimentally obtained from a LaBr3 detector with a short decay time to prepare a dataset for training the deep learning models. The pile-up signals were generated by randomly synthesizing single pulses. Samples around their peaks were sliced using the peak-finding method and used as input data for the deep learning models. As a result of the optimization, the modified peak-finding method improved the count restoration rate compared to the previous method by effectively detecting the peaks of tail pile-up, and peak pile-up pulses. Furthermore, the input data length and region were optimized based on the performance evaluation of each deep learning model. Despite having a simpler architecture than the CNN model, the DNN model demonstrated excellent PHE performance. The results of this study showed the efficient and practical considerations necessary for applying pile-up signal correction in high-radiation fields.
Reconciling roles of the South China Sea summer monsoon and ENSO in prediction of the Indian Ocean dipole
Authors: Zhang Yazhou , Li Jianping , Diao Yina , Wang Qiuyun , Wu Renguang , Liu Ting , Jin Yishuai , Hou Zhaolu , Wang Haili 
Published: December 2023

Abstract
The Indian Ocean dipole (IOD) is a remarkable interannual variability in the tropical Indian Ocean. The improved prediction of IOD is of a great value because of its large socioeconomic impacts. Previous studies reported that both El Niño-Southern Oscillation (ENSO) and South China Sea summer monsoon (SM) play a dominant role in the western and eastern pole of the IOD, respectively. They can be used as predictors of the IOD at 3 month lead beyond self-persistence. Here, we develop an empirical model of multi-factors in which the western pole is predicted by ENSO and persistence and the eastern pole is predicted by SM and persistence. This new empirical model outperforms largely the average level of the dynamical models from the North American multi-model ensemble (NMME) project in predicting the peak IOD in boreal autumn, with a correlation coefficient of ∼0.86 and a root mean square error of ∼0.24 °C. Furthermore, the hit rate of positive culminated IOD in this new empirical model is equivalent to that in current NMME models (above 65%), much higher than that for negative culminated IOD. This improvement of skill using the empirical model suggests a perspective for better understanding and predicting the IOD.
Another ‘Bleak House’—the decision of the German Federal Supreme Court on the inadmissibility of intra-EU ICSID arbitrations
Authors: Scherpf Georg , Hermes Simon 
Published: December 2023

Abstract
The decision of the German Federal Supreme Court of 27 July 2023 has sparked intense discussions. Unexpectedly, the Court granted a declaration of inadmissibility of intra-EU ICSID proceedings under section 1032(2) of the German Code of Civil Procedure. The decision is of significant practical relevance and yet another push against intra-EU Investor–State Dispute Resolution. After a short introduction (i), we will describe the legal and factual background of the decision (ii) and summarize the Court’s reasoning (iii). Thereafter, we will comment on the decision (iv) and conclude by pointing out possible implications for future intra-EU ICSID proceedings (v).
MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction
Authors: Xu Fanding , Yang Zhiwei , Wang Lizhuo , Meng Deyu , Long Jiangang 
Published: December 2023

Abstract
Identifying task-relevant structures is important for molecular property prediction. In a graph neural network (GNN), graph pooling can group nodes and hierarchically represent the molecular graph. However, previous pooling methods either drop out node information or lose the connection of the original graph; therefore, it is difficult to identify continuous subtructures. Importantly, they lacked interpretability on molecular graphs. To this end, we proposed a novel Molecular Edge Shrinkage Pooling (MESPool) method, which is based on edges (or chemical bonds). MESPool preserves crucial edges and shrinks others inside the functional groups and is able to search for key structures without breaking the original connection. We compared MESPool with various well-known pooling methods on different benchmarks and showed that MESPool outperforms the previous methods. Furthermore, we explained the rationality of MESPool on some datasets, including a COVID-19 drug dataset.
Integrative approaches based on genomic techniques in the functional studies on enhancers
Authors: Wang Qilin , Zhang Junyou , Liu Zhaoshuo , Duan Yingying , Li Chunyan 
Published: December 2023

Abstract
With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.