bluuebunny/crossref_metadata_2025_split huggingface dataset:
Melanaspis corticosa: a new insect pest of olive trees in Europe
Authors: Mazzeo Gaetana , Pellizzari Giuseppina , Mateus Célia , da Silva Elsa Borges , Russo Agatino , Nucifora Salvatore , Soares Celestino , Tomé Dora , de Andrade Eugénia , Franco José Carlos 
Published: January 2023

AbstractThe presence of the South African Obscure Scale, Melanaspis corticosa (Brain) (Hemiptera, Diaspididae), was detected infesting olive trees, in Portugal. The identity of the scale insect was confirmed based on both morphological and molecular studies. Until now, this species was only known in a few African countries, including Guinea, Mozambique, South Africa and Zimbabwe. This is the first record of this species in Europe and in the Palearctic region. The scale was observed in 15 different locations, in the Algarve, since its first detection at the end of 2016. Samples were collected between 21 December 2016 and 10 March 2022, covering all seasonal periods. Most of the sampling sites resulted from private requests from farmers and proprietaries received by the Plant Protection Division of the Regional Directorate of Agriculture. Although it is considered a polyphagous species, it was not observed in other plant species, besides olive trees. The actual dispersion in the region suggests that M. corticosa became established and has been expanded its distribution since its arrival. This scale insect is a potential injurious pest of olive trees and needs to be studied to clarify its pest status and develop effective pest management strategies.
Retention potential of a drained wetland in the Lusatian lignite mining district, Briesener Niedermoor, Germany, Europe
Authors: Hartmann Clemens , Uhlmann Wilfried , Scheytt Traugott 
Published: January 2023

AbstractAcid mine drainage (AMD) is one of the most important global causes for polluted water. It occurs in mining areas due to oxidation of sulfide minerals. Depending on hydrological conditions, wetlands are considered suitable for passive treatment of AMD. The aim of the study was to characterize the long-term retention performance of a drained wetland, Briesener Niedermoor, in an iron and sulfate contaminated catchment area. The site is located in the Lusatian lignite mining district, Germany. Hydrological and hydrochemical measurements as well as time series of groundwater and surface water were evaluated and saturation conditions of iron species were determined using Phreeqc Interactive and EH–pH diagrams for the Fe–S–K–O–H system. Progressing peat degradation due to drainage leads to a hydraulic behaviour of the wetland that is more comparable to a rain bog than a fen, and thus to more pronounced oxic conditions. Groundwater feeds the wetland with significant loads of iron and sulfate, which are temporally stored in the wetland. At low surface water discharge, iron is removed and weak acidic pH is buffered by the dominant phase of schwertmannite (Fe8O8(OH)6SO4), usually during summer months. In winter, greater water availability initializes a high source strength from the wetland, that cannot be retarded. Thus, only during summer, there is a retention potential; in winter, the retention potential is not strong enough to prevent an inflow of iron and sulfate. Only a significant rewetting of the wetland may result in a restoration of the retention potential of the wetland Briesener Niedermoor.
Prognostic significance of tumor infiltrating lymphocytes on first-line pembrolizumab efficacy in advanced non-small cell lung cancer
Authors: Kaira Kyoichi , Yamaguchi Ou , Kawasaki Tomonori , Hashimoto Kousuke , Miura Yu , Shiono Ayako , Mouri Atsuto , Imai Hisao , Kobayashi Kunihiko , Yasuda Masanori , Kagamu Hiroshi 
Published: January 2023

Abstract
Aim
Tumor-infiltrating lymphocytes (TILs) in the tumor and stroma are expected to accurately predict the efficacy of programmed death-1 (PD-1) blockade therapy. However, little is known about the prognostic significance of TILs in first-line PD-1 therapy. We assessed TILs in patients with advanced or metastatic non-small cell lung cancer (NSCLC) treated with pembrolizumab in the palliative setting.

Methods
Multiplex immunohistochemistry staining of TILs (CD4, CD8, Foxp3, and PD-1) and immunohistochemical staining of CK and PD-L1 in the tumor and stroma was performed in tumor specimens of 107 NSCLC patients and correlated with clinical outcomes, as a single-center retrospective study. TILs and programmed death ligand-1 (PD-L1) were assessed on biopsies (N = 93) or surgical resections (N = 14) before first-line pembrolizumab.

Results
A low number of stromal CD4 TILs were significantly associated with bone metastasis and poor performance status (PS). The median progression-free survival (PFS) and overall survival (OS) were significantly higher in patients with a high number of stromal CD4 TILs (336 days and 731 days, respectively) than in those with low infiltration (204 days and 333 days, respectively). Patients with a high number of intratumoral CD8 TILs (731 days) yielded significantly better OS than those with low infiltration (333 days), but not for PFS. Multivariate analysis confirmed that stromal CD4 TILs were independent predictors of PFS, but not OS. Furthermore, intratumoral CD8 TILs were independent predictors of better OS. In the survival analysis of key subgroups, stromal CD4 TILs were identified as significant predictors of survival in patients with non-adenocarcinomatous histology and PD-L1 ≥ 50%.

Conclusion
Stromal CD4 TILs were identified as a significant marker for predicting the PFS after pembrolizumab therapy, especially in patients with non-adenocarcinoma and high PD-L1 expression. In addition, intratumoral CD8 TILs were identified as significant predictors of OS.
Identifying Landscape Relevant Natural Language using Actively Crowdsourced Landscape Descriptions and Sentence-Transformers
Authors: Baer Manuel F. , Purves Ross S. 
Published: January 2023

AbstractNatural language has proven to be a valuable source of data for various scientific inquiries including landscape perception and preference research. However, large high quality landscape relevant corpora are scare. We here propose and discuss a natural language processing workflow to identify landscape relevant documents in large collections of unstructured text. Using a small curated high quality collection of actively crowdsourced landscape descriptions we identify and extract similar documents from two different corpora (Geograph and WikiHow) using sentence-transformers and cosine similarity scores. We show that 1) sentence-transformers combined with cosine similarity calculations successfully identify similar documents in both Geograph and WikiHow effectively opening the door to the creation of new landscape specific corpora, 2) the proposed sentence-transformer approach outperforms traditional Term Frequency - Inverse Document Frequency based approaches and 3) the identified documents capture similar topics when compared to the original high quality collection. The presented workflow is transferable to various scientific disciplines in need of domain specific natural language corpora as underlying data.
URWalking: Indoor Navigation for Research and Daily Use
Authors: Ludwig Bernd , Donabauer Gregor , Ramsauer Dominik , Subari Karema al 
Published: January 2023

AbstractIn this report, we present the project URWalking conducted at the University of Regensburg. We describe its major outcomes: Firstly, an indoor navigation system for pedestrians as a web application and as an Android app with position tracking of users in indoor and outdoor environments. Our implementation showcases that a variant of the $$A^*$$

A
∗

-algorithm by Ullmann (tengetriebene optimierung präferenzadaptiver fußwegrouten durch gebäudekomplexe https://epub.uni-regensburg.de/43697/, 2020) can handle the routing problem in large, levelled indoor environments efficiently. Secondly, the apps have been used in several studies for a deeper understanding of human wayfinding. We collected eye tracking and synchronized video data, think aloud protocols, and log data of users interacting with the apps. We applied state-of-the-art deep learning models for gaze tracking and automatic classification of landmarks. Our results indicate that even the most recent version of the YOLO image classifier by Redmon and Farhadi (olov3: An incremental improvement. arXiv, 2018) needs finetuning to recognize everyday objects in indoor environments. Furthermore, we provide empirical evidence that appropriate machine learning models are helpful to bridge behavioural data from users during wayfinding and conceptual models for the salience of objects and landmarks. However, simplistic models are insufficient to reasonably explain wayfinding behaviour in real time—an open issue in GeoAI. We conclude that the GeoAI community should collect more naturalistic log data of wayfinding activities in order to build efficient machine learning models capable of predicting user reactions to routing instructions and of explaining how humans integrate stimuli from the environment as essential information into routing instructions while solving wayfinding tasks. Such models form the basis for real-time wayfinding assistance.
An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets
Authors: Wan Benting , Hu Zhaopeng , Garg Harish , Cheng Youyu , Han Mengjie 
Published: January 2023

AbstractIt is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.
Inpainting larger missing regions via progressive guidance decoding network
Authors: Dong Xiucheng , Jiang Jinyang , Hou Shuang , Yang Chencheng 
Published: January 2023

AbstractFor images corrupted for various reasons, the size of the corrupted area is often arbitrary and it has been a challenge to inpainting the larger missing areas. Though popular multistage networks ease the inpainting difficulty by repairing damaged image from coarse to fine, their common drawback is that the result of each stage is easily misguided by the wrong content generated in the previous stage. To address this problem, we propose a novel progressive guidance decoding network. First, multiple parallel decoding branches fill and refine the missing regions by top–down passing the reconstructed priors. This inpainting way of progressive guidance avoids adverse effects of inappropriate premises, since the decoding branches can learn what priors can be utilized. And convolution layers of decoder with different locations would pass down the different priors. The joint guidance of features and gradient priors helps the inpainting result contains the correct structure and rich details. The second fold of progressive guidance is achieved by our fusing strategy, combining ghost convolution and the designed cascaded efficient channel attention (CECA) to fuse and reweight the features from different branches. CECA explores the dependencies among adjant and non-adjant channels more effectively than popular ones. Finally, we merges the different-scale feature maps reconstructed by the last decoding branch and mapping them to the image space, which further improves the semantic plausibility of the restoration results. Extensive experiments verify the effectiveness of our method in both subjective and objective evaluation.
Application of UAS-Based Remote Sensing in Estimating Winter Wheat Phenotypic Traits and Yield During the Growing Season
Authors: Hassani Kianoosh , Gholizadeh Hamed , Taghvaeian Saleh , Natalie Victoria , Carpenter Jonathan , Jacob Jamey 
Published: January 2023

AbstractPhenotyping approaches have been considered as a vital component in crop breeding programs to improve crops and develop new high-yielding cultivars. However, traditional field-based monitoring methods are expensive, invasive, and time-intensive. Moreover, data collected using satellite and airborne platforms are either costly or limited by their spatial and temporal resolution. Here, we investigated whether low-cost unmanned/unoccupied aerial systems (UASs) data can be used to estimate winter wheat (Triticum aestivum L.) nitrogen (N) content, structural traits including plant height, fresh and dry biomass, and leaf area index (LAI) as well as yield during different winter wheat growing stages. To achieve this objective, UAS-based red–green–blue (RGB) and multispectral data were collected from winter wheat experimental plots during the winter wheat growing season. In addition, for each UAS flight mission, winter wheat traits and total yield (only at harvest) were measured through field sampling for model development and validation. We then used a set of vegetation indices (VIs), machine learning algorithms (MLAs), and structure-from-motion (SfM) to estimate winter wheat traits and yield. We found that using linear regression and MLAs, instead of using VIs, improved the capability of UAS-derived data in estimating winter wheat traits and yield. Further, considering the costly and time-intensive process of collecting in-situ data for developing MLAs, using SfM-derived elevation models and red-edge-based VIs, such as CIre and NDRE, are reliable alternatives for estimating key winter wheat traits. Our findings can potentially aid breeders through providing rapid and non-destructive proxies of winter wheat phenotypic traits.
Sleep Quality Moderates the Relationship Between Daily Mean Levels and Variability of Positive Affect
Authors: Song Jiyoung , Crawford Christopher M. , Fisher Aaron J. 
Published: January 2023

Abstract
Despite the well-established bidirectional association between sleep and daytime affect, most studies examining this relationship have focused on mean levels of affect. However, research solely focusing on mean levels of affect inherently neglects variability in affect, which has been shown to predict both psychological and physical well-being beyond mean levels. The present study assessed sleep quality and daytime affect using ecological momentary assessment in a combined sample of individuals (N = 80; 8,881 observations) with and without anxiety and mood disorders. Results from the present study partially replicated extant work on the negative association between negative affect (NA) variability and subsequent sleep quality. Furthermore, less satisfying sleep amplified the positive relationship between daily mean levels and variability of positive affect (PA). The results did not differ by clinical status. The present study offers novel evidence suggesting that previous night’s sleep quality influences the stability of varying daily levels of PA. Uncovering the dynamics of sleep and affect beyond mean levels will help further elucidate mechanisms linking sleep and subsequent affective experiences.
Diversity from the customer's perspective: Good or bad? The case of disability
Authors: Essa Musa 
Published: January 2023

AbstractThe effect of workforce diversity on customers is an important and understudied topic in marketing. The article reviews the published studies investigating how customers perceive and are influenced by employees with disabilities. Studies included for review examined how customers respond when served by an employee with a disability. Scopus was used as the primary database in the search for articles. A total of eight studies have been reviewed. Nearly all the studies are published recently, indicating the growing academic interest in the topic. Findings are discussed as there is no consensus on whether disability can positively or negatively affect the customer’s perception of the employee and the company. Research gaps, methodologies and limitations are addressed, and future research directions are suggested and discussed. This review indicates that this topic is under investigated and there is still room for research given the small number of published work and the methodological and theoretical gaps.
High plant density increases sunlight interception and yield of direct-seeded winter canola in China
Authors: Wang Rui , Wu Weixian , Cheng Xiaolei , Peng Wenli 
Published: January 2023

Summary
Rationally higher population density is crucial for seeking a balance that meets lodging resistance and maximizes seed yield in mechanized direct-seeded winter canola. In this study, a split-plot experiment with two cultivars (Huayouza9 and Zhongshuang11) and eleven planting densities (12–105 plants m-2) was conducted in a two-season field experiment to evaluate the high planting density in this cropping system and improve its production efficiency. Seed yield noticeably increased in planting density up to 80 plants m-2 in Zhongshuang11 (2187 kg hm-2) and 60 plants m-2 in Huayouza9 (2943 kg hm-2). The seed yield of Huayouza9 did not differ significantly from the local target seed yield. Higher plant density curtailed the luxurious vegetative growth of individual canola plants at the density of no less than 60–80 plants m-2, and high seed yield was derived from the increased ratio of main raceme and branch seed weight in winter canola. An increase in plant densities contributed to the reinforced sunlight interception at the pod-filling stage, providing a larger canopy photosynthetic area for the rapid growth of more canola pods at higher densities (60–105 plants m-2). Lodging resistance and breaking resistance decreased sharply with the plant density increasing from 12 to 60 plants m-2 while remaining almost steady as it further increased from 60 to 105 plants m-2 for Huayouza9 and Zhongshuang11. Hence, the population density of 60 plants m-2 reached a balance between lodging resistance and maximized seed yield in mechanized direct-seeded winter canola in China.
Cognitive-linguistic skills and vocabulary knowledge breadth and depth in children’s L1 Chinese and L2 English
Authors: Pan Dora Jue , Nakayama Mariko , McBride Catherine , Cheah Zebedee Rui En , Zheng Mo , Yeung Charlie Cheuk Laam 
Published: January 2023

AbstractThis study examined the correlates of different aspects of vocabulary knowledge in L1 Chinese and L2 English in Hong Kong bilingual children (N = 481, age = 6–12 years old). Their nonverbal IQ, cognitive-linguistic skills, receptive and expressive vocabulary knowledge breadth, and vocabulary knowledge depth in Chinese and English were measured. Results demonstrated that morphological awareness was uniquely correlated with different aspects of vocabulary knowledge across Chinese and English. Phonological processing skills played different roles in vocabulary knowledge in L1 and L2. In addition, receptive vocabulary breadth uniquely contributed to expressive vocabulary breadth across languages. Moreover, both receptive and expressive vocabulary breadth contributed to vocabulary knowledge depth in L1 Chinese and L2 English. The findings highlight some shared and unique aspects of different vocabulary constructs across languages.
Simulations of dark matter with frequent self-interactions
Authors: Fischer Moritz S. 
Published: January 2023

AbstractSelf-interacting dark matter (SIDM) is promising to solve or at least mitigate small-scale problems of cold collisionless dark matter. N-body simulations have proven to be a powerful tool to study SIDM within the astrophysical context. However, it turned out to be difficult to simulate dark matter (DM) models that typically scatter about a small angle, for example, light mediator models. We developed a novel numerical scheme for this regime of frequent self-interactions that allows for N-body simulations of systems like galaxy cluster mergers or even cosmological simulations. We have studied equal and unequal mass mergers of galaxies and galaxy clusters and found significant differences between the phenomenology of frequent self-interactions and the commonly studied large-angle scattering (rare self-interactions). For example, frequent self-interactions tend to produce larger offsets between galaxies and DM than rare self-interactions.
Fate of escaping orbits in barred galaxies
Authors: Mondal Debasish , Chattopadhyay Tanuka 
Published: January 2023

AbstractIn the present work, we have developed a two-dimensional gravitational model of barred galaxies to analyse the fate of escaping stars from the central barred region. For that, the model has been analysed for two different bar profiles viz. strong and weak. Here the phenomena of stellar escape from the central barred region have been studied from the perspective of an open Hamiltonian dynamical system. We observed that the escape routes correspond to the escape basins of the two index-1 saddle points. Our results show that the formation of spiral arms is encouraged for the strong bars. Also, the formation of grand design spirals is more likely for strong bars if they host central super massive black holes (SMBHs). In the absence of central SMBHs, the formation of less-prominent spiral arms is more likely. Again, for weak bars, the formation of inner disc rings is more probable.
GP-MOOD: a positivity-preserving high-order finite volume method for hyperbolic conservation laws
Authors: Lee Dongwook , Bourgeois Rémi 
Published: January 2023

AbstractWe present an a posteriori shock-capturing finite volume method algorithm called GP-MOOD. The method solves a compressible hyperbolic conservative system at high-order solution accuracy in multiple spatial dimensions. The core design principle in GP-MOOD is to combine two recent numerical methods, the polynomial-free spatial reconstruction methods of GP (Gaussian Process) and the a posteriori detection algorithms of MOOD (Multidimensional Optimal Order Detection). We focus on extending GP’s flexible variability of spatial accuracy to an a posteriori detection formalism based on the MOOD approach. The resulting GP-MOOD method is a positivity-preserving method that delivers its solutions at high-order accuracy, selectable among three accuracy choices, including third-order, fifth-order, and seventh-order.