bluuebunny/crossref_metadata_2025_split huggingface dataset:
Luteolin suppresses inflammation and oxidative stress in chronic obstructive pulmonary disease through inhibition of the NOX4‐mediated NF‐κB signaling pathway
Authors: Li Mingfei , Wang Huifang , Lu Yun , Cai Jinwen 
Published: April 2023

AbstractIntroductionChronic obstructive pulmonary disease (COPD) is associated with chronic inflammation that predominantly affects the lung and peripheral airways. Previous investigation has underlined the efficacy of luteolin in the treatment of inflammation‐related symptoms. Accordingly, our study concentrates on unveiling the effect of luteolin on COPD.MethodsMice or A549 cells were treated with cigarette smoke (CS) to establish COPD models in vivo and in vitro. Then, the serum and bronchoalveolar lavage fluid of mice were harvested. The lung tissues of mice were subjected to hematoxylin‐eosin staining to observe the degree of damage. The inflammation and oxidative stress factors level were calculated via enzyme‐linked immunosorbent assay and quantitative real‐time polymerase chain reaction. The expressions of nuclear factor‐kappa B (NF‐κB) pathway‐related factors were detected by Western blot.ResultsIn in vivo experiments, CS treatment reduced the weight of mice and promoted lung tissue damage, while luteolin attenuated the effect of CS on the mice. Moreover, luteolin inhibited the inflammation factors level, oxidative stress, and NADPH oxidase 4 (NOX4)‐mediated NF‐κB signaling pathway in CS‐induced COPD mice. Similar results were obtained in in vitro experiments that luteolin alleviated CS‐induced inflammation, oxidative stress, and NOX4‐mediated NF‐κB signaling pathway activation in CS‐treated A549 cells. Besides, NOX4 overexpression offset the impacts of luteolin on the CS‐induced A549 cells.ConclusionLuteolin alleviates inflammation and oxidative stress in COPD via NOX4‐mediated NF‐κB signaling pathway, which provides a theoretical basis for the treatment of COPD with luteolin.
Meat processing plant survival: The role of plant and regional characteristics
Authors: Isley Catherine , Low Sarah A. 
Published: April 2023

AbstractFederal and state governments are making major investments in expanded meat processor capacity and improved industry resilience. To improve decision‐making, this research identifies characteristics related to the probability of meat processing plant survival using hazard analysis and establishment‐level data on US meat processors (including beef, pork, goat, lamb and mutton, and large game processors) 1997–2020. We find plant survival is associated with both plant characteristics and local context, though specific factors related to survival vary with plant size and rurality. Smaller plants are less likely to survive than larger plants, and for smaller plants survival is most strongly related to business diversification. For larger plants, local context, including workforce variables, has the strongest relationship with survival. Our analysis shows little relationship between meat processing industry concentration and plant survival, though we find weak evidence of a positive relationship between industry concentration and large nonmetro plant survival.
Enhancing power distribution network operational resilience to extreme wind events
Authors: Donaldson Daniel L. , Ferranti Emma J.S. , Quinn Andrew D. , Jayaweera Dilan , Peasley Thomas , Mercer Mark 
Published: April 2023

AbstractExtreme weather events can cause significant damage to power distribution network infrastructure, often resulting in power outages. Distribution Network Operators (DNOs) are faced with the challenging task of responding to these outages in real time while maintaining a resilient grid. Our paper presents an innovative approach to alert operators about the potential risk associated with upcoming extreme weather through a normalized fragility curve. The uniqueness of the curve is the ability to capture regional differences across a DNO's territory while presenting operators with a means of setting unified risk thresholds. This can support a proactive response and allow the staging of necessary resources to minimize the threat posed by such events. Our approach captures the changes in failure probability associated with differing wind regimes and demonstrates the benefit of sub‐regional meteorological information. The proposed approach is demonstrated for wind events using 20 years of historical fault records from a DNO in the United Kingdom (UK). While its efficacy is demonstrated for windstorms in the UK, the approach could be applied globally to develop normalized fragility curves for other types of seasonal extreme weather events such as snowstorms, hurricanes, or linked hazards such as wildfires. The approach can also facilitate an understanding of how infrastructure may operate under future climate conditions, supporting proactive adaptation.
A V‐band digitally controlled low noise variable gain amplifier with 3.5° phase variation in 28‐nm bulk CMOS
Authors: Lin Haitao , Li Hui‐Yang , Xu Jin‐Xu , Zhang Xiuyin 
Published: April 2023

AbstractThis paper presents a V‐band digitally controlled low noise variable gain amplifier (LNVGA) in 28‐nm bulk CMOS process. The amplifier includes three stages and the first stage is used to realize wideband high gain and low noise figure (NF). The gain control is implemented in the second and third stages with current steering and split cascode topology, respectively. A source degeneration inductor and a series inductor are introduced to reduce the phase variation in the second stage. The phase response function is analyzed, and the phase reduction of 1.2° is achieved. Moreover, with the combination of these two different topologies, the phase variation can be further reduced. The measurement results show the LNVGA has a peak gain of 16 dB with a 3‐dB bandwidth of 45–59 GHz. The measured gain control range is 0–16 dB in 16 different gain states and the minimum phase variation is 3.5° with power consumption of 23 mW.
Time‐dependent diffusion MRI using multiple stimulated echoes
Authors: Dan Guangyu , Sun Kaibao , Luo Qingfei , Zhou Xiaohong Joe 
Published: April 2023

PurposeTo develop a time‐efficient pulse sequence that acquires multiple diffusion‐weighted images with distinct diffusion times in a single shot by using multiple stimulated echoes (mSTE) with variable flip angles (VFA).MethodsThe proposed diffusion‐weighted mSTE with VFA (DW‐mSTE‐VFA) sequence begins with two 90° RF pulses that straddle a diffusion gradient lobe (GD) to excite and restore one half of the magnetization into the longitudinal axis. The restored longitudinal magnetization was successively re‐excited by a series of RF pulses with VFA, each followed by another GD, to generate a set of stimulated echoes. Each of the multiple stimulated echoes was acquired with an EPI echo train. As such, the train of multiple stimulated echoes produced a set of diffusion‐weighted images with varying diffusion times in a single shot. This technique was experimentally demonstrated on a diffusion phantom, a fruit, and healthy human brain and prostate at 3 T.ResultsIn the phantom experiment, the mean ADC measured at different diffusion times using DW‐mSTE‐VFA were highly consistent (r = 0.999) with those from a commercial spin‐echo diffusion‐weighted EPI sequence. In the fruit and brain experiments, DW‐mSTE‐VFA exhibited similar diffusion‐time dependence to a standard diffusion‐weighted stimulated echo sequence. The ADC showed significant time dependence in the human brain (p = 0.003 in both white matter and gray matter) and prostate tissues (p = 0.003 in both peripheral zone and central gland).ConclusionDW‐mSTE‐VFA offers a time‐efficient tool for investigating the diffusion‐time dependency in diffusion MRI studies.
Simulating the impact of varying vegetation on West African monsoon surface fluxes using a regional convection‐permitting model
Authors: Bamba Adama , Kouadio Kouakou , Toure N’Datchoh E. , Jackson Lawrence , Marsham John , Roberts Alex , Yoshioka Masaru , Anquetin Sandrine , Diedhiou Arona 
Published: April 2023

AbstractThis study assessed the sensitivity of the West African climate to varying vegetation fractions. The assessment of a such relationship is critical in understanding the interactions between land surface and atmosphere. Two sets of convection‐permitting simulations from the UK Met Office Unified Model at 12 km horizontal resolution covering the monsoon period May–September (MJJAS) were used, one with fixed vegetation fraction (MF‐V) and the other with time‐varying vegetation fraction (MV‐V). Vegetation fractions are based on MODIS retrievals between May and September. We focused on three climatic zones over West Africa: Guinea Coast, Sudanian Sahel, and the Sahel while investigating heat fluxes, temperature, and evapotranspiration. Results reveal that latent heat fluxes are the most strongly affected by vegetation fraction over the Sahelian and Sudanian regions while sensible heat fluxes are more impacted over the Guinea Coast and Sudanian Sahel. Also, in MV‐V simulation there is an increase in evapotranspiration mainly over the Sahel and some specific areas in Guinea Coast from June to September. Moreover, it is noticed that high near‐surface temperature is associated with a weak vegetation fraction, especially during May and June. Finally, varying vegetation seems to improve the simulation of surface energy fluxes and in turn impact on climate parameters. This suggests that climate modelers should prioritize the use of varying vegetation options to improve the representation of the West African climate system.
Reaching new environments through illegal trade: evidence of a widely traded turtle in Colombia
Authors: Julián Arango-Lozano , Dahian Patiño-Siro , Felipe Toro-Cardona 
Published: April 2023

AbstractA major threat to biodiversity is illegal trade, with many unwanted wildlife pets released into exotic environments outside their native distribution. Therefore, many potential invasive species have established in new ecosystems. Ecological niche modeling (ENM) has been used to predict and compare the environmental conditions of natural and exotic population in many groups. We used ENM to compare the climatic niche between natural and exotic areas ofTrachemys venusta callirostris, one of the most traded turtles in Colombia. We generated a niche model using the MaxEnt algorithm through the R package kuenm to test several parametrizations and four sets of fresh water environmental predictors. Models were calibrated in the native distribution and projected to non-native zones in Colombia to identify suitable areas for the species. Further, we use a niche similarity test to compare native and exotic environmental space. We found few suitable areas within the projected zone even when using extrapolation; there was a greater suitability in the Magdalena River basin than in the Cauca River basin. Low similarity was detected between the niche comparison of native and exotic areas, suggesting that exotic populations have reached different environmental conditions than the native zone through ilegal trade. Although there was low extrapolation in the exotic area, the models projected ideal conditions in localities with new records for this turtle. The generalist strategies for feeding, thermoregulation, and reproduction in changing conditions may help thisT. v. callirostrisestablish in new ecosystems, and with no current knowledge on dynamics between this exotic species and local fauna, its effects on aquatic communities are unpredictable.
A self-guided approach for navigation in a minimalistic foraging robotic swarm
Authors: Adams Steven , Jarne Ornia Daniel , Mazo Manuel 
Published: April 2023

AbstractWe present a biologically inspired design for swarm foraging based on ant’s pheromone deployment, where the swarm is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and the swarm is fully decentralized and needs no infrastructure in place. Additionally, the system only requires one-hop communication over the robot network, we do not make any assumptions about the connectivity of the communication graph and the transmission of information and computation is scalable versus the number of agents. This is done by letting the agents in the swarm act as foragers or as guiding agents (beacons). We present experimental results computed for a swarm of Elisa-3 robots on a simulator, and show how the swarm self-organizes to solve a foraging problem over an unknown environment, converging to trajectories around the shortest path, and test the approach on a real swarm of Elisa-3 robots. At last, we discuss the limitations of such a system and propose how the foraging efficiency can be increased.
Extended reality for procedural planning and guidance in structural heart disease – a review of the state-of-the-art
Authors: Stephenson Natasha , Pushparajah Kuberan , Wheeler Gavin , Deng Shujie , Schnabel Julia A , Simpson John M 
Published: April 2023

AbstractExtended reality (XR), which encompasses virtual, augmented and mixed reality, is an emerging medical imaging display platform which enables intuitive and immersive interaction in a three-dimensional space. This technology holds the potential to enhance understanding of complex spatial relationships when planning and guiding cardiac procedures in congenital and structural heart disease moving beyond conventional 2D and 3D image displays. A systematic review of the literature demonstrates a rapid increase in publications describing adoption of this technology. At least 33 XR systems have been described, with many demonstrating proof of concept, but with no specific mention of regulatory approval including some prospective studies. Validation remains limited, and true clinical benefit difficult to measure. This review describes and critically appraises the range of XR technologies and its applications for procedural planning and guidance in structural heart disease while discussing the challenges that need to be overcome in future studies to achieve safe and effective clinical adoption.
Words matter: judges’ value judgments in sentence pronouncements remarks
Authors: de Castro Rodrigues Andreia , Cunha Olga S. , de Oliveira Jorge Q. , Gonçalves Rui A. , Sacau-Fontenla Ana 
Published: April 2023

AbstractThis study examines judges’ value judgments on their remarks during sentence pronouncements. We performed a content analysis of 93 sentence pronouncements from the 13 judges from a Portuguese criminal court. Within these discourses, 299 discourse units were codified as judges’ value judgments, that is, personal contents beyond strict legal issues. From these 299, 107 were recommendations (comprehending advice to change, to not reoffend, to rethink life, and action instructions), and 192 were opinions about the individual, the society, and the judicial system. The existence of value judgments in sentence pronouncements carries important implications for the sentencing process. Namely, these value judgments allow the identification of judges’ personal ideas, and issues particularly vulnerable for simplistic reasonings, as well as subjective considerations. This identification and the discussion on the powerful role of language in the context of sentencing are key features to strengthen judges’ training and, consequently, to improve the implementation of penal justice.
Development of Family Adaptability and Cohesion from Adolescence to Young Adulthood and Associations with Parental Behavior
Authors: Spitz Andrea , Steinhausen Hans-Christoph 
Published: April 2023

AbstractPrevious research has demonstrated the influence of family functioning on developmental outcomes but only a few studies have addressed the normative changes of family functioning during adolescence. While there is evidence that family adaptability is stable, findings regarding the development of family cohesion levels are controversial. The focus on the association of family functioning with parenting behavior has also been quite limited. Some studies have revealed that an authoritative parenting style is connected with better family functioning, but only a small body of research has analyzed the association with the main dimensions of parenting behavior. The current study investigated developmental trajectories of family adaptability and cohesion from adolescence to young adulthood. The impact of sex, number of siblings, marital status, socioeconomic status and parenting behavior was studied in a sample of N = 619 participants from a longitudinal Swiss study at two measurement times. Repeated measures ANOVAs and cross-sectional linear regression models were used to analyze the data. There was a significant developmental decline in perceived family cohesion but no change in adaptability from adolescence to young adulthood. In addition, there was a significant main effect of socioeconomic status on adaptability and of parental divorce on cohesion. Boys experienced a significantly steeper decline in family cohesion than girls. Adaptability and cohesion were predicted by perceived parental acceptance and psychological control at both measurement times while cohesion was also significantly predicted by perceived parental structure. The findings reflect normative developmental processes in the transition period.
Common-weights fuzzy DEA model in the presence of undesirable outputs with ideal and anti-ideal points: development and prospects
Authors: Majdi Meysam , Ebrahimnejad Ali , Azizi Amir 
Published: April 2023

AbstractIn the literature, fuzzy arithmetic approach has been used for solving fuzzy data envelopment analysis (DEA) model in the presence of undesirable outputs by use of ideal and anti-ideal decision-making units (DMUs). In order to obtain the best and the worst fuzzy efficiencies, such an approach allows each DMU to choose the most favorable weights via optimizing its own evaluation that needs to solve various mathematical models. On the one hand, solving one mathematical model to find the best fuzzy efficiency and solving another mathematical model to find the worst fuzzy efficiency for each DMU increase the computational complexity significantly. On the other hand, the fully flexibility of weights leads to the different set of weights that may not be desirable. This paper proposes a common-weight method from two optimistic and pessimistic perspectives in fuzzy environment to determine the common sets of weights (CWS) to compute the best and the worst fuzzy efficiencies of all DMUs. The advantages of using common-weight DEA method based upon fuzzy arithmetic approach are reduction of the computational complexities and evaluation of equitably the best and the worst fuzzy efficiencies on the same base. The proposed approach first solves one linear programming model to compute each of the best and the worst fuzzy efficiencies of all DMUS, and then combines them to find a relative closeness index for the overall assessment. The developed approach is illustrated by a numerical example form the literature and the obtained results are compared with those from the existing ones.
Weight normalization optimization movie recommendation algorithm based on three-way neural interaction networks
Authors: Liang Zhenlu , Yang Zhisheng , Cheng Jingyong 
Published: April 2023

AbstractHeterogeneous information networks are increasingly used in recommendation algorithms. However, they lack an explicit representation of meta-paths. In using bidirectional neural interaction models for recommendation models, interaction between users and items is often ignored, with an integral impact on the accuracy of the recommendations. To better apply the interaction information, this study proposes a weight-normalized movie recommendation model (SCLW_MCRec) based on a three-way neural interaction network. The model constructs a three-way neural interaction network $$\langle $$
⟨
user, meta-path, item$$\rangle $$
⟩
from meta-path contextual information, introducing meta-paths on top of the user-item representation to represent the user-item interaction information. Introduction of a two-layer, one-dimensional convolutional neural network helps capture higher-order interaction features between the user and the item, making the model more powerful in terms of interaction. Adding a dropout layer to the interaction model and using a two-layer convolutional neural network can prevent overfitting and discard irrelevant information features to improve the recommendation. In addition, an extreme cross-entropy loss (argmaxminloss) that incorporates the properties of the argmin and argmax functions is designed to reduce the model loss. A weight-normalization optimization approach is used to better optimize the model and accelerate convergence of the stochastic gradient descent optimization. Compared to current state-of-the-art recommendation models, the SCLW_MCRec model improves the Prec evaluation index by 2.94–35.8%, Recall by 1.15–53.51%, and NDCG by 6.7–49.37% on the MovieLens dataset. The framework provides a significant improvement in recommendation accuracy and also solves the cold-start problem with application of interaction information.
Migration Responses to Earthquakes: Evidence from Italy
Authors: Basile Roberto , Centofanti Francesca , Giallonardo Luisa , Licari Francesca 
Published: April 2023

AbstractIn this paper, we analyze the migration responses to natural disasters by focusing on the three most devastating earthquakes in Italy in recent decades: L’Aquila 2009, Emilia Romagna 2012, and Central Italy 2016. Using municipality-level data for 2002–2019 and adopting a new difference-in-difference approach with multiple periods and multiple groups, we evaluate the causal effect of these events on internal and international inbound and outbound migration of both Italian and foreign citizens. The results suggest that, despite the massive destruction, there is no evidence that these earthquakes significantly impacted the migration of Italian citizens. We only found evidence of the effect of the earthquake in L’Aquila on the short-distance migration of foreign citizens.
Containment Measures and Business Confidence in COVID Times: A Global Study
Authors: Margaretic Paula , Finchelstein Diego , Sotelo Agustín 
Published: April 2023

ABSTRACTIn this article, we take a global perspective to assess the impact of the exogenous COVID pandemic shock on business confidence. Through a quantitative analysis of 31 advanced and 12 emerging economies over the period from January 2018 to December 2020, we provide a novel investigation of a unique worldwide event, in contrast to the most frequent exogenous shocks, which typically have a more limited local or regional scope. We proxy business expectations with the business confidence indicator or BCI. First, we find that the containment measures for the COVID pandemic have negatively affected business confidence, with the compulsory policies having a greater negative effect on BCI than the voluntary ones. Second, we find positive spillover effects on the local BCIs from the containment measures implemented in neighboring countries. This suggests that business people are not against compulsory measuresper se, but rather that they are less inclined to assume the costs of these. Third, we find that while the severity of containment measures has been greater in emerging countries, the negative impact on BCI of these containment measures has been larger in advanced economies.