bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pollen provisioning attenuates pesticide side‐effects on a phytoseiid predator
Authors: Samaras Konstantinos , Pappas Maria L , Pozzebon Alberto , Broufas George D 
Published: January 2024

AbstractBACKGROUNDBiological control with predatory mites is applied against pests in greenhouse crops. Chemical control with the use of selective, reduced‐risk pesticides, is an important component of Integrated Pest Management (IPM) programs, that often needs to be combined with biological control. Here, we evaluated the effect of plant pollen when used as supplementary food on the survival, reproduction and predation of the predatory mite Amblydromalus limonicus (Acari: Phytoseiidae) after exposing young larvae and adults to flonicamid, an insecticide of moderate toxicity to phytoseiids. Pollen is an important alternative food for generalist phytoseiids ensuring survival and supporting populations build‐up during periods of prey scarcity. Two regimes of cattail (Typha angustifolia L.) pollen differing in application frequency were used. In the first, the total amount of pollen was supplied once, within 30 min after insecticide application, whereas in the second regime, the same amount of pollen was supplied gradually, i.e., every 48 h.RESULTSRegardless of the frequency of application, pollen provisioning results in a reduction in prey (thrips) consumption relative to the control (no pollen provisioning). Nevertheless, when adult mites were directly exposed to flonicamid residues, pollen provisioning attenuated the reduction in prey consumption as compared to the control. In addition, the gradual (every 48 h) provisioning of pollen to adult predators exposed to flonicamid residues impacted positively the intrinsic rate of population increase (rm) of A. limonicus as compared to when feeding on prey.CONCLUSIONOur results reveal an unexpected role of pollen provisioning in alleviating pesticides side‐effects on phytoseiids. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Crystallization of Amorphous N‐Doped Ge‐Rich GST Layers Deposited on a Polycrystalline GST Template
Authors: Luong Minh‐Anh , Rahier Eloïse , Ran Sijia , Claverie Alain 
Published: January 2024

Herein, a detailed experimental study of the crystallization of amorphous N‐doped Ge‐rich GeSbTe layers (GGSTN) when in contact with a polycrystalline GeSbTe (GST template is reported on. By combining in situ annealing in the transmission electron microscope and ex situ chemical analysis, it is shown that, as observed in many materials, the layer in contact with the crystalline template crystallizes at a lower temperature than needed for bulk crystallization. At surprise, this characteristic is not due to the solid‐phase epitaxy of the GGSTN on the crystalline GST template but to a decrease of the Ge content in the GGSTN layer. Indeed, during annealing, Ge diffuses from the amorphous layer into the crystalline GST layer where it gets trapped and forms grains which grow, eventually leading to the formation of a pure crystalline Ge layer. As a result of this Ge depletion, the thermal budget necessary to drive the phase separation limiting the crystallization of the remaining GGSTN alloy is smaller.
Sparse Bayesian variable selection in high‐dimensional logistic regression models with correlated priors
Authors: Ma Zhuanzhuan , Han Zifei , Ghosh Souparno , Wu Liucang , Wang Min 
Published: January 2024

AbstractIn this paper, we propose a sparse Bayesian procedure with global and local (GL) shrinkage priors for the problems of variable selection and classification in high‐dimensional logistic regression models. In particular, we consider two types of GL shrinkage priors for the regression coefficients, the horseshoe (HS) prior and the normal‐gamma (NG) prior, and then specify a correlated prior for the binary vector to distinguish models with the same size. The GL priors are then combined with mixture representations of logistic distribution to construct a hierarchical Bayes model that allows efficient implementation of a Markov chain Monte Carlo (MCMC) to generate samples from posterior distribution. We carry out simulations to compare the finite sample performances of the proposed Bayesian method with the existing Bayesian methods in terms of the accuracy of variable selection and prediction. Finally, two real‐data applications are provided for illustrative purposes.
Sustainable urban expansion and human development in China: An analysis using an environmentally improved human development index
Authors: Shao Jiaqi , Kong Xuesong 
Published: January 2024

AbstractUrban expansion poses a substantial challenge to sustainable regional development, but the spatiotemporal relationship between urban expansion and human development remains unclear. This paper proposed an improved human development index (IHDI) incorporating an environmental dimension and analyzed the spatiotemporal correlation between urban expansion and IHDI growth in China from 1990 to 2020. Significant increases in both urban expansion and human development levels were observed, and environmental differences between Eastern and Western China were effectively detected based on the IHDI, which also well reflected the geographical patterns bounded by the Hu Huanyong Line. In 1990, a significant disparity existed in the IHDI between the provincial capital cities and the entire province; by 2020, this gap had narrowed, and the prioritized development of provincial capitals subsequently propelled regional growth. Although positive correlations between urban expansion and IHDI growth in China were found from 2010 to 2020, the percentage of expansive negative decoupling between urban expansion and IHDI growth gradually increased, indicating a less sustainable relationship involved in urban expansion and human development in China. This study contributes to a comprehensive understanding of the spatiotemporal correlation between urban expansion and human development, and provides valuable insights for achieving sustainable development goals in China.
Sustainable investing and financing for sustainable development: A hybrid review
Authors: Joshipura Mayank , Mathur Sachin , Kedia Nikita 
Published: January 2024

AbstractSustainable investing and financing play a central role in achieving sustainable development goals. Using high‐quality articles from the Scopus database, this study deploys bibliometric analysis followed by content analysis on 528 and 33 articles to synthesise the academic literature on sustainable investing and financing published between 2012 and mid‐2022 and analyse the field's evolution and progress in research. The performance analysis identifies the field's most influential studies, and the conceptual and intellectual structures shed light on foundational themes and research progression. An in‐depth content analysis identifies and reveals novel insights on three primary research themes: financing sustainability, asset returns, and sustainable outcomes. The key issues encompass investor motivations and investment performance of sustainable investments compared to conventional investments, challenges, and policy enablers that aid the development of sustainable financing and sustainability outcomes. The study also offers future research directions and concisely summarises research using the theories‐context‐characteristics‐methods framework. The findings will help further theory development in academic research on sustainable investing and financing and have practical implications for investors, issuers, and regulators. Investors can gain insights into the performance of sustainable investments, issuers can understand the benefits of adopting sustainable finance, and regulators can obtain insights into the policy enablers that would result in more efficient outcomes. The limitation of the study is that it is qualitative and does not quantify factors or models that lead to sustainable development.
Agroecology, tourism, and community adaptability under UNESCO biosphere reserve: A case study of smallholders in northern Thailand
Authors: Amloy Aumpika , Wonglangka Warong , Ounchanum Pandin , Ruangwitthayanusorn Supakul , Siriphon Aranya , Oranratmanee Rawiwan 
Published: January 2024

AbstractThis study investigates the intersection of agroecology and sustainable tourism, shedding light on the adaptive resilience within rural communities. Focused on the delicate balance between tourism‐driven economic gains, social‐ecological enhancements, and environmental conservation, the research centers on a case study involving four communities within the Mueang Khong subdistrict of Chiang Dao, Northern Thailand. Methodologically, the fieldwork conducted in 2022 combined ecological and landscape surveys with qualitative methods, including interviews, observations, and document analysis, to capture insights into local communities' perspectives, behaviors, and experiences. By adopting an agroecological approach to tourism, these communities have demonstrated their dynamism of adaptability and resilience in response to challenges posed by existing tourism activities and emerging circumstances. A significant catalyst contributing to this adaptability is the UNESCO recognition of Doi Chiang Dao as a new biosphere reserve, fostering new collaborations among diverse stakeholders and raising awareness regarding positive ecological conservation, food diversity and security, and improved livelihoods. This study argues that the ability to adapt, aiming to cultivate thriving communities and achieve balanced economic growth, responsible tourism, social inclusivity, and resource management, stands as a fundamental pillar for sustainable development in rural areas of Northern Thailand.
Being a parent of a soldier is a challenging experience – stress, anxiety, and depression among parents of Israeli soldiers
Authors: Savitsky Bella , Shvartsur Rachel 
Published: January 2024

AbstractAt any given moment, a notable proportion of parents worldwide have at least one child serving in the military. The aim of this study is to assess the prevalence of depression, anxiety, and stress in the sample of parents of Israeli soldiers and to assess the difference in this prevalence by type of service (combat vs. non‐combat) and other demographic characteristics of parents and soldiers. A cross‐sectional study of a convenience sample of 202 Israeli parents who were interviewed during January‐September 2023. Depression Anxiety Stress Scale (DASS‐21) was used to measure the emotional states of depression, anxiety, and stress (with cut‐off points of ≥5, ≥4, and ≥8, respectively). Having depression, anxiety, or stress was defined as psychological distress. Parents' demographics and child's service characteristics were included in the multivariable logistic regression model, with psychological distress as a dependent variable. Almost a quarter (22.8%) of parents experienced distress, defined as having high depression, anxiety, or stress score. In a multivariable model, combat service (vs. non‐combat) was significantly associated with distress: parents of combat soldiers were four times more likely to report distress than parents of non‐combat soldiers (OR = 3.9; 95% CI: 1.3–11.8). Highly classified service preventing the child from sharing information with the parents was significantly associated with distress (OR = 2.6; 95% CI: 1.2–5.3). Most distressed parents (78.3%) did not seek professional help, with the vast majority of those seeking assistance were female. Given the substantial proportion of parents suffering from mental distress found in this study, especially parents of combat soldiers and those serving in highly classified positions, healthcare professionals should be aware of parents' difficulties, be proactive in gathering information about their mental well‐being, and be prepared to provide professional help.
Borrowing networks for innovation: The role of attention allocation in secondhand brokerage
Authors: Rhee Luke , Leonardi Paul 
Published: January 2024

AbstractResearch SummaryThis study investigates how people connecting with brokers who span structural holes in a firm's communication networks can enhance their innovative performance. Through sociometric data from a large software company, we observe that individuals who pay attention to information from brokers achieve higher innovative performance compared with those who ignore such information. The advantage of paying attention to brokers' information is more pronounced when people operate in highly constrained networks. Yet, our post hoc analysis reveals that people typically allocate less attention to information from brokers than from local colleagues—they systematically do the opposite of what they should do for innovative performance. Our findings regarding the role of attention for secondhand brokerage make significant contributions to studies of networks and innovation for behavioral strategy.Managerial SummaryThis study investigates how innovation is influenced by paying attention to brokers within a company. Brokers are individuals who bridge different groups, providing access to varied information. Analysis of internal data from a large software company reveals that employees who heed the ideas of brokers tend to be more innovative, especially in close‐knit networks. However, there is a notable tendency for people to ignore brokers, favoring information from familiar colleagues instead. This indicates a significant attention bias, where the potentially most valuable sources for innovation are often unattended. Our research highlights the critical need for a strategic approach to distributing attention within company networks, which can significantly boost innovative performance.
Earliest Danian outer neritic elasmobranch assemblages reveal an environmentally controlled faunal turnover at the Cretaceous–Palaeogene boundary in the northern Tethyan Realm (Austria)
Authors: Feichtinger Iris , Pollerspöck Jürgen , Harzhauser Mathias , Auer Gerald , Ćorić Stjepan , Kranner Matthias , Beaury Bernhard , Guinot Guillaume 
Published: January 2024

AbstractThis study reports elasmobranch remains from two fossil‐rich horizons in the earliest Danian Olching Formation at Waidach, Austria. These outer neritic assemblages complement previous fine‐scale bulk‐sampling of latest Maastrichtian horizons at Waidach and document a regional elasmobranch faunal turnover across the Cretaceous–Palaeogene (K–Pg) boundary. The Danian assemblages show homogeneity in species richness and are dominated by squaliforms. The fauna comprises 16 species belonging to 12 genera including several new taxa (Centrodeania rugosa gen. et sp. nov., Centrodeania annae gen. et sp. nov., Incognitorapax fernsebneri gen. et sp. nov., Scyliorhinus alaformis sp. nov.). Comparison with latest Maastrichtian assemblages from Waidach revealed a marked faunal turnover across the K–Pg boundary associated with an increase in species richness and shift in abundance from Squaliformes to Carcharhiniformes. This is associated with marked environmental changes from a deep marine, dysoxic setting in the Maastrichtian to a more oxygenated, shallower environment in the earliest Danian. The turnover was driven by environmentally induced regional changes in species geographic ranges. High diversity in the Danian fauna suggests that the habitability of the corresponding palaeoenvironment was preserved or recovered immediately after the K–Pg event. Comparison with other elasmobranch assemblages across the K–Pg boundary highlights a strong control of local palaeoenvironmental settings over the timing and magnitude of the turnover. Our study emphasizes the importance of successive sampling to disentangle local from general patterns of faunal turnover during the K–Pg event and to better assess the consequences of this extinction event over elasmobranch diversity.
Eurocode 2 design of recycled aggregate concrete elements under compression: Evaluation through model uncertainties from tests on columns
Authors: Knapcová Veronika , Pacheco João , de Brito Jorge , Sonnenschein Robert 
Published: January 2024

AbstractRecycled aggregate concrete contributes to the recovery of construction and demolition waste, which is a growing societal concern. The technical feasibility of recycled aggregate concrete has been extensively tested and validated and the revisions of codes are already including clauses for recycled aggregate concrete specification and design. However, there are still doubts regarding its structural behavior. This article assesses the suitability of Eurocode 2 design clauses for concrete elements under compression when used for recycled aggregate concrete design. This is done through the analysis of model uncertainties that are estimated from a database collected from laboratory experiments on columns. Both the current version of Eurocode 2 (with and without taking confinement into account) and the upcoming (second generation) Eurocode 2 are considered. A database was collected with clearly defined criteria and includes coarse recycled aggregates produced from concrete waste, since columns made with other types of recycled aggregates are scarcely tested and do not represent the current status for structural recycled aggregate concrete design and production. The statistics of the model uncertainty for natural and recycled concrete columns were compared and it was observed that the mean model uncertainty tends to decrease slightly when recycled aggregates are used. However, a preliminary assessment of whether a partial factor for recycled aggregate concrete compression design should be used concluded that this partial factor is not necessary to ensure reliable design and the design of columns may be carried out using the same formulae used for conventional concrete. The format proposed may be used for other resistance models, provided that the partial factor is calibrated for each resistance model.
Exploring m6A‐linked aging genes in osteoarthritis and broad cancer spectrum: Prospects for diagnostic and therapeutic advancements
Authors: Cai Qiuchen , Xia Wenyang , Su Qihang , Ge Heng'an , Chen Liyang , Liu Centao , Zhao Bin'an , Xue Chao , Huang Jinbiao , Huang Chenlong , Li Jun , Wu Peng , Cheng Biao 
Published: January 2024

AbstractOsteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts individuals and healthcare systems worldwide. However, the exploration of N6‐methyladenosine (m6A)‐related aging genes in OA pathogenesis remains largely underexplored. This study aimed to elucidate the role of m6A‐related aging genes in OA and to develop a robust diagnostic model based on their expression profiles. Leveraging publicly available gene expression datasets, we conducted consensus clustering to categorize OA into distinct subtypes, guided by the expression patterns of m6A‐related aging genes. Utilizing XGBoost, a cutting‐edge machine learning approach, we identified key diagnostic genes and constructed a predictive model. Our investigation extended to the immune functions of these genes, shedding light on potential therapeutic targets and underlying regulatory mechanisms. Our analysis unveiled specific OA subtypes, each marked by unique expression profiles of m6A‐related aging genes. We pinpointed a set of pivotal diagnostic genes, offering potential therapeutic avenues. The developed diagnostic model exhibited exceptional capability in distinguishing OA patients from healthy controls. To corroborate our computational findings, we performed quantitative real‐time polymerase chain reaction analyses on two cell lines: HC‐OA (representing adult osteoarthritis cells) and C‐28/I2 (representative of normal human chondrocytes). The gene expression patterns observed were consistent with our bioinformatics predictions, further validating our initial results. In conclusion, this study underscores the significance of m6A‐related aging genes as promising biomarkers for diagnosis and prognosis, as well as potential therapeutic targets in OA. Although these findings are encouraging, further validation and functional analyses are crucial for their clinical application.
Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques
Authors: Fernandes Joana Filipa Teixeira , Freitas Diamantino Rui , Teixeira João Paulo 
Published: January 2024

AbstractUsing acoustic analysis to classify and identify speech disorders non-invasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread throughout 19 pathologies, and 194 control people were used. There are nine sound files per subject, three vowels in three tones, for each subject. From each sound file, 13 parameters were extracted. For the classification of healthy/pathological individuals, a variety of classifiers based on Machine Learning models were used, including decision trees, discriminant analyses, logistic regression classifiers, naive Bayes classifiers, support vector machines, classifiers of closely related variables, ensemble classifiers and artificial neural network classifiers. For each patient, 118 parameters were used initially. The first analysis aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the Ensemble Sub-space Discriminant classifier. The second and third analyses aimed to improve ground accuracy using preprocessing methodologies. Therefore, in the second analysis, the PCA technique was used, with an accuracy of 80.2%. The third analysis combined several outlier treatment models with several data normalization models and, in general, accuracy improved, obtaining the best accuracy (82.9%) with the combination of the Greebs model for outliers treatment and the range model for the normalization of data procedure.
Exploring Features to Classify Occupational Accidents in the Retail Sector
Authors: Sena Inês , Braga Ana Cristina , Novais Paulo , Fernandes Florbela P. , Pacheco Maria F. , Vaz Clara B. , Lima José , Pereira Ana I. 
Published: January 2024

AbstractThe Machine Learning approach is used in several application domains, and its exploitation in predicting accidents in occupational safety is relatively recent. The present study aims to apply different Machine Learning algorithms for classifying the occurrence or non-occurrence of accidents at work in the retail sector. The approach consists of obtaining an impact score for each store and work unit, considering two databases of a retail company, the preventive safety actions, and the action plans. Subsequently, each score is associated with the occurrence or non-occurrence of accidents during January and May 2023. Of the five classification algorithms applied, the Support Vector Machine was the one that obtained the best accuracy and precision values for the preventive safety actions. As for the set of actions plan, the Logistic Regression reached the best results in all calculated metrics. With this study, estimating the impact score of the study variables makes it possible to identify the occurrence of accidents at work in the retail sector with high precision and accuracy.