bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Thermochromic, Viscoelastic Nacre-like Nanocomposite for the Smart Thermal Management of Planar Electronics
Authors: Wang Jiemin , Yang Tairan , Wang Zequn , Sun Xuhui , An Meng , Liu Dan , Zhao Changsheng , Zhang Gang , Lei Weiwei 
Published: July 2023

AbstractCutting-edge heat spreaders for soft and planar electronics require not only high thermal conductivity and a certain degree of flexibility but also remarkable self-adhesion without thermal interface materials, elasticity, arbitrary elongation along with soft devices, and smart properties involving thermal self-healing, thermochromism and so on. Nacre-like composites with excellent in-plane heat dissipation are ideal as heat spreaders for thin and planar electronics. However, the intrinsically poor viscoelasticity, i.e., adhesion and elasticity, prevents them from simultaneous self-adhesion and arbitrary elongation along with current flexible devices as well as incurring high interfacial thermal impedance. In this paper, we propose a soft thermochromic composite (STC) membrane with a layered structure, considerable stretchability, high in-plane thermal conductivity (~ 30 W m−1 K−1), low thermal contact resistance (~ 12 mm2 K W−1, 4–5 times lower than that of silver paste), strong yet sustainable adhesion forces (~ 4607 J m−2, 2220 J m−2 greater than that of epoxy paste) and self-healing efficiency. As a self-adhesive heat spreader, it implements efficient cooling of various soft electronics with a temperature drop of 20 °C than the polyimide case. In addition to its self-healing function, the chameleon-like behavior of STC facilitates temperature monitoring by the naked eye, hence enabling smart thermal management.
Generalised Hausdorff measure of sets of Dirichlet non-improvable matrices in higher dimensions
Authors: Bakhtawar Ayreena , Simmons David 
Published: July 2023

AbstractLet $$\psi :\mathbb {R}_{+}\rightarrow \mathbb {R}_{+}$$

ψ
:

R
+

→

R
+

be a non-increasing function. A pair $$(A,{\textbf{b}}),$$

(
A
,
b
)
,

where A is a real $$m\times n$$

m
×
n

matrix and $${\textbf{b}}\in \mathbb {R}^{m},$$

b
∈

R

m

,

is said to be $$\psi $$
ψ
-Dirichlet improvable, if the system $$\begin{aligned} \Vert A{\textbf{q}} +{\textbf{b}}-{\textbf{p}}\Vert ^m<\psi (T), \quad \Vert {\textbf{q}}\Vert ^n<T \end{aligned}$$

‖
A
q
+
b
-
p
‖

m

<
ψ

(
T
)

,

‖
q
‖

n

<
T

is solvable in $${\textbf{p}}\in \mathbb {Z}^{m},$$

p
∈

Z

m

,

$${\textbf{q}}\in \mathbb {Z}^{n}$$

q
∈

Z

n

for all sufficiently large T where $$\Vert \cdot \Vert $$

‖
·
‖

denotes the supremum norm. For $$\psi $$
ψ
-Dirichlet non-improvable sets, Kleinbock–Wadleigh (2019) proved the Lebesgue measure criterion whereas Kim–Kim (2022) established the Hausdorff measure results. In this paper we obtain the generalised Hausdorff f-measure version of Kim–Kim (2022) results for $$\psi $$
ψ
-Dirichlet non-improvable sets.
Sparse self-attention guided generative adversarial networks for time-series generation
Authors: Ahmed Nourhan , Schmidt-Thieme Lars 
Published: July 2023

AbstractRemarkable progress has been achieved in generative modeling for time-series data, where the dominating models are generally generative adversarial networks (GANs) based on deep recurrent or convolutional neural networks. Most existing GANs for time-series generation focus on preserving correlations across time. Although these models may help in capturing long-term dependencies, their capacity to pay varying degrees of attention over different time steps is inadequate. In this paper, we propose SparseGAN, a novel sparse self-attention-based GANs that allows for attention-driven, long-memory modeling for regular and irregular time-series generation through learned embedding space. This way, it can yield a more informative representation for time-series generation while using original data for supervision. We evaluate the effectiveness of the proposed model using synthetic and real-world datasets. The experimental findings indicate that forecasting models trained on SparseGAN-generated data perform comparably to forecasting models trained on real data for both regularly and irregularly sampled time series. Moreover, the results demonstrate that our proposed generative model is superior to the current state-of-the-art models for data augmentation in the low-resource regime and introduces a novel method for generating realistic synthetic time-series data by leveraging long-term structural and temporal information.
The Buffering Effect of Perceptions of Teacher and Student Defending on the Impact of Peer Victimization on Student Subjective Wellbeing
Authors: Meter Diana J. , Butler Kevin J. , Renshaw Tyler L. 
Published: July 2023

AbstractPeer victimization is associated with unwanted outcomes including less school belongingness and lower academic ability and school achievement. This study expanded on previous research by investigating how a perception of defending by peers and teachers interacted with peer victimization to predict four aspects of student subjective wellbeing: school connectedness, joy of learning, educational purpose, and academic efficacy. Participants were 1058 (488 girls, 524 boys, 20 who reported “other,” and 26 who did not report gender) U.S. adolescent 6th–9th graders, many who received free/reduced-price lunch, often from urban schools. Their mean age was 12.96 years (SD = 1.20); most identified as Hispanic (62%), followed by White (13%), Black (11%), Multiracial (7%), Asian (6%), and less than 1% American Indian or Pacific Islander. Results showed that perceived defending from students can impact victimized adolescents’ sense of connectedness to their schools and their joy of learning. It is important to understand how perceived defending impacts victims of peer victimization in order to evaluate intervention effectiveness.
Study of AI-Driven Fashion Recommender Systems
Authors: Shirkhani Shaghayegh , Mokayed Hamam , Saini Rajkumar , Chai Hum Yan 
Published: July 2023

AbstractThe rising diversity, volume, and pace of fashion manufacturing pose a considerable challenge in the fashion industry, making it difficult for customers to pick which product to purchase. In addition, fashion is an inherently subjective, cultural notion and an ensemble of clothing items that maintains a coherent style. In most of the domains in which Recommender Systems are developed (e.g., movies, e-commerce, etc.), the similarity evaluation is considered for recommendation. Instead, in the Fashion domain, compatibility is a critical factor. In addition, raw visual features belonging to product representations that contribute to most of the algorithm’s performances in the Fashion domain are distinguishable from the metadata of the products in other domains. This literature review summarizes various Artificial Intelligence (AI) techniques that have lately been used in recommender systems for the fashion industry. AI enables higher-quality recommendations than earlier approaches. This has ushered in a new age for recommender systems, allowing for deeper insights into user-item relationships and representations and the discovery patterns in demographical, textual, virtual, and contextual data. This work seeks to give a deeper understanding of the fashion recommender system domain by performing a comprehensive literature study of research on this topic in the past 10 years, focusing on image-based fashion recommender systems taking AI improvements into account. The nuanced conceptions of this domain and their relevance have been developed to justify fashion domain-specific characteristics.
Defining Insights
Authors: Cadogan Alicia A. , Lau Jerry , Wnorowski Susan , Kelsch Geri R. , Oreper Jane , Chavez Lillian , Weidman Joseph J. , Hermes-DeSantis Evelyn R. 
Published: July 2023

Abstract
Background
Insights, when acted upon, can result in positive changes to the business, for HCPs, and ultimately for patients. Medical Information, as a customer facing function, is one of the groups that generate insights. Data and insights across different functions of an organization need to be compiled to provide a comprehensive view. The purpose of this paper is to develop a shared definition of insights and to provide a working guidance for the insight process.

Methods
Two surveys were conducted of the phactMI membership first to establish a shared definition of insights and then to benchmark current insight process. From this data and the shared experience of the working group a proposed guidance was developed.

Results
The developed definition of an insight is “An insight is the deeper understanding of the why behind trends of information that lead us to determine if an action is warranted”. For the most robust outcomes, insight identification needs to be a cross functional activity. The proposed structured approach can be leveraged and customized for any organization and include the following five steps: INvestigate, Scrutinize, Identify, Take Action, and Enlighten (INSITE).

Conclusion
The INSITE process provides a simple framework that should become routine for all Medical Information colleagues who are leading the work around insights. The process should be shared across all functions that participate in the insight generation process. This is another area where Medical Information can demonstrate leadership and highlight their value to the organization.
Nominal and real wages in the UK, 1750–2015: mean reversion, persistence and structural breaks
Authors: Caporale Guglielmo Maria , Gil-Alana Luis A. 
Published: July 2023

AbstractThis paper analyses the stochastic properties of UK nominal and real wages over the period 1750–2015 using fractional integration techniques. Both the original series and logged ones are analysed. The results generally suggest that nominal wages exhibit a higher degree of persistence, which reflects relatively long lags between inflation and wage adjustments. Endogenous break tests are also carried out and various structural breaks are identified in both series. On the whole, the corresponding subsample estimates imply an increase over time in the degree of persistence of both series.
Pion PDFs confronted by fixed-target charmonium production
Authors: Chang Wen-Chen , Hsieh Chia-Yu , Lian Yu-Shiang , Peng Jen-Chieh , Platchkov Stephane , Sawada Takahiro 
Published: July 2023

AbstractThe pion, as the Goldstone boson of the strong interaction, is the lightest QCD bound state and responsible for the long-range nucleon-nucleon interaction inside the nucleus. Our knowledge on the pion partonic structure is limited by the existing Drell-Yan data which are primarily sensitive to the pion valence-quark distributions. The recent progress of global analysis of pion’s parton distribution functions (PDFs) utilizing various experimental approaches are introduced. From comparisons between the pion-induced $$J/\psi$$

J
/
ψ

and $$\psi (2S)$$

ψ
(
2
S
)

production data with theoretical calculations using the CEM and NRQCD models, we show how these charmonium production data could provide useful constraints on the pion PDFs.
Reasons for Belief in Context
Authors: Bradley Darren 
Published: July 2023

AbstractThere is currently a lively debate about whether there are practical reasons for belief, epistemic reasons for belief, or both. I will argue that the intuitions on all sides can be fully accounted for by applying an independently motivated contextualist semantics for normative terms. Specifically, normative terms must be relativized to a goal. One possible goal is epistemic, such as believing truly and not believing falsely, while another possible goal is practical, such as satisfying desires, or maximizing value. I will argue that we have practical reasons given the practical goal and epistemic reasons given the epistemic goal. Disagreement disappears when we make the context explicit. The result is an independently motivated version ofpluralism.
Spatiotemporally mode-locked soliton fiber laser at 2.8 μm
Authors: Chen Ying’an , Zhou Yicheng , Qin Zhipeng , Xie Guoqiang , Yuan Peng , Ma Jingui , Qian Liejia 
Published: July 2023

AbstractSpatiotemporal mode-locking creates great opportunity for pulse energy scaling and nonlinear optics research in fiber. Until now, spatiotemporal mode-locking has only been realized in normal-dispersion dissipative soliton and similariton fiber lasers. In this paper, we demonstrated the first experimental realization of a spatiotemporally mode-locked soliton laser in mid-infrared fluoride fiber with anomalous dispersion. The mode-locked fluoride fiber oscillator directly generated a record pulse energy of 16.1 nJ and peak power of 74.6 kW at 2.8 μm wavelength. This work extends the spatiotemporal mode-locking to soliton fiber lasers and should have a wide interest for the laser community.
ELEMENTARY EQUIVALENCE IN POSITIVE LOGIC VIA PRIME PRODUCTS
Authors: MORASCHINI TOMMASO , WANNENBURG JOHANN J. , YAMAMOTO KENTARO 
Published: July 2023

Abstract
We introduce prime products as a generalization of ultraproducts for positive logic. Prime products are shown to satisfy a version of Łoś’s Theorem restricted to positive formulas, as well as the following variant of the Keisler Isomorphism Theorem: under the generalized continuum hypothesis, two models have the same positive theory if and only if they have isomorphic prime powers of ultrapowers.
A novel approach to control four multi-rotor drones in cooperative paired control using relative Jacobian
Authors: Thebe Keletso Z. , Jamisola Rodrigo S. , Ramalepa Larona P. 
Published: July 2023

AbstractThis work presents a new formulation to holistically control four cooperative multi-rotor drones controlled in two pairs. This approach uses a modular relative Jacobian with components consisting of the Jacobians of each individual drone. This type of controller relies mainly on the relative motion between the drones, consequently releasing unnecessary constraints inherent to the control of drones in absolute motion. We present the derivations of all the necessary equations of the modular relative Jacobian to control the four multi-rotor drones. We also present the derivations of the Jacobian for each drone. We implement our proposed method in the Gazebo RotorS simulation using four hexa-rotor drones modeled from Ascending Technologies called firefly drones. We present the simulation results and analyze them to show the effectiveness of our proposed approach.
Greater affective network maturity is associated with better clinical outcomes in women with early sexual trauma
Authors: Kleshchova Olena , Grinband Jack , Weierich Mariann R. 
Published: July 2023

AbstractEarly-life adversity accelerates the maturation of affect-related circuitry, which might be a short-term adaptation with long-term tradeoffs. Sexual trauma is associated with a particularly strong impact on pubertal development and mental health outcomes. Our objective was to test the relations between trauma type, affective network maturity, and mental health outcomes in young women with trauma history. Trauma-exposed women aged 18–29 completed a clinical interview (n = 35) and an fMRI scan (n = 28). We used a public data set to train a machine learning algorithm to predict age from resting-state affective network connectivity and calculated network maturity as the difference between predicted and true age. We also performed principal component analysis on mental health outcomes and retained two components: clinical and state psychological outcomes. Compared to nonsexual trauma (n = 17), sexual trauma (n = 11) was associated with greater affective network maturity. In addition, for sexual trauma only, greater affective network maturity was associated with better clinical but not state psychological outcomes. These results suggest that sexual trauma during development might uniquely alter the maturational trajectory of affect-related circuitry, with distinct mental health consequences in emerging adulthood. Whereas delayed affective network maturation is associated with adverse clinical outcomes, accelerated affective network maturation might confer resilience in survivors.
A (machine-oriented) logic based on pattern matching
Authors: Lethen Tim 
Published: July 2023

AbstractRobinson’s unification algorithm can be identified as the underlying machinery of both C. Meredith’s rule D (condensed detachment) in propositional logic and of the construction of principal types in lambda calculus and combinatory logic. In combinatory logic, it also plays a crucial role in the construction of Meyer, Bunder & Powers’ Fool’s model. This paper now considers pattern matching, the unidirectional variant of unification, as a basis for logical inference, typing, and a very simple and natural model for untyped combinatory logic. An analysis of the new typing scheme will enable us to characterize a large class of terms of combinatory logic which do not change their principal type when being weakly reduced. We also consider the question whether the major or the minor premisse should be used as the fixed pattern.
Human Trafficking and Emergency Medical Services (EMS)
Authors: Shekhar Aditya C. , Macias-Konstantopoulos Wendy L. 
Published: July 2023

AbstractHuman trafficking is associated with wide-ranging mental and physical morbidity, as well as mortality, in the United States and globally. Emergency Medical Services (EMS) providers are often first responders to victims of human trafficking. Given their proximity to patients’ social and environmental circumstances, these clinicians need to be familiar with the signs and symptoms of human trafficking, as well understand how to best provide care for suspected or confirmed trafficked patients. Evidence from multiple studies indicates that providers who have received formal training may be better able to recognize the signs and symptoms of human trafficking, and thus, can provide better care to potential victims of human trafficking. This review will summarize the relevance of human trafficking to prehospital emergency care, touch on best practices for the care of patients with suspected or confirmed ties to human trafficking, and outline future directions for education and research.
Ecological risk and protective factors for food insufficiency in Los Angeles County during the COVID-19 pandemic
Authors: de la Haye Kayla , Saw Htay-Wah , Miller Sydney , Bruine de Bruin Wändi , Wilson John P , Weber Kate , Frazzini Alison , Livings Michelle , Babboni Marianna , Kapteyn Arie 
Published: July 2023

AbstractObjective:The COVID-19 pandemic increased food insufficiency: a severe form of food insecurity. Drawing on an ecological framework, we aimed to understand factors that contributed to changes in food insufficiency from April to December 2020, in a large urban population hard hit by the pandemic.Design:We conducted internet surveys every 2 weeks in April–December 2020, including a subset of items from the Food Insecurity Experience Scale. Longitudinal analysis identified predictors of food insufficiency, using fixed effects models.Setting:Los Angeles County, which has a diverse population of 10 million residents.Participants:A representative sample of 1535 adults in Los Angeles County who are participants in the Understanding Coronavirus in America tracking survey.Results:Rates of food insufficiency spiked in the first year of the pandemic, especially among participants living in poverty, in middle adulthood and with larger households. Government food assistance from the Supplemental Nutrition Assistance Program was significantly associated with reduced food insufficiency over time, while other forms of assistance such as help from family and friends or stimulus funds were not.Conclusions:The findings highlight that during a crisis, there is value in rapidly monitoring food insufficiency and investing in government food benefits.
Observation‐Constrained Projection of Flood Risks and Socioeconomic Exposure in China
Authors: Kang Shengyu , Yin Jiabo , Gu Lei , Yang Yuanhang , Liu Dedi , Slater Louise 
Published: July 2023

AbstractAs the planet warms, the atmosphere's water vapor holding capacity rises, leading to more intense precipitation extremes. River floods with high peak discharge or long duration can increase the likelihood of infrastructure failure and enhance ecosystem vulnerability. However, changes in the peak and duration of floods and corresponding socioeconomic exposure under climate change are still poorly understood. This study employs a bivariate framework to quantify changes in flood risks and their socioeconomic impacts in China between the past (1985–2014) and future (2071–2100) in 204 catchments. Future daily river streamflow is projected by using a cascade modeling chain based on the outputs of five bias‐corrected global climate models (GCMs) under three shared socioeconomic CMIP6 pathways (SSP1‐26, SSP3‐70, and SSP5‐85), a machine learning model and four hydrological models. We also utilize the copula function to build the joint distribution of flood peak and duration, and calculate the joint return periods of the bivariate flood hazard. Finally, the exposure of population and regional gross domestic product to floods are investigated at the national scale. Our results indicate that flood peak and duration are likely to increase in the majority of catchments by 25%–100% by the late 21st century depending on the shared socioeconomic pathway. China is projected to experience a significant increase in bivariate flood risks even under the lowest emission pathway, with 24.0 million dollars/km2 and 608 people/km2 exposed under a moderate emissions scenario (SSP3‐70). These findings have direct implications for hazard mitigation and climate adaptation policies in China.
Top‐Down Regulation by a Reindeer Herding System Limits Climate‐Driven Arctic Vegetation Change at a Regional Scale
Authors: Spiegel Marcus P. , Volkovitskiy Alexander , Terekhina Alexandra , Forbes Bruce C. , Park Taejin , Macias‐Fauria Marc 
Published: July 2023

AbstractWarming‐driven growth of tall woody vegetation in the Arctic has the potential to accelerate climate change through multiple positive feedbacks. Local‐scale evidence suggests that large herbivores limit this vegetation shift, but there is uncertainty at larger, regional scales whether current herbivory pressure is a major top‐down control on ecosystem structure and functioning. Across a 67,000 km2 region of the Yamal Peninsula in West Siberia, we integrated satellite remote sensing with a novel data set mapping the migrations of herds comprising 151,000 domesticated reindeer. Where reindeer numbers varied over space, higher reindeer herbivory pressure was consistently linked with lower coverage of tall woody vegetation. Within areas dominated by this vegetation type, productivity and climate were increasingly decoupled where reindeer density was higher. Our spaceborne fingerprint detection suggests that large herbivores, at current population densities, counteract Arctic vegetation responses to climate change over large spatial scales.