bluuebunny/crossref_metadata_2025_split huggingface dataset:
ESG-CFP relationship: exploring the moderating role of financial slack
Authors: Singh Aanchal , Verma Subir , Shome Samik 
Published: June 2023

PurposeThis study aims at examining the contentions of the agency theory by exploring the direct relationship between environmental, social and governance (ESG) disclosure score and corporate financial performance (CFP) from the years 2016–2020. It also tests for the adaptability of slack resources theory by testing this relationship in the presence of a moderating variable (financial slack).Design/methodology/approachThe study relies on the data obtained from Bloomberg database of 112 companies belonging to different sectors. It employs the use of partial least square structure equation modelling (PLS-SEM) for carrying out the empirical analysis.FindingsThe results obtained show that there exists a negative relationship between ESG and CFP of the sample firms. These results lend support to the propositions of both the agency theory. Further, the financial slack in the organizations does not ensure a firm's responsible behavior.Research limitations/implicationsThe paper provides important implications both from the perspective of managers as well as policymakers. The results of this study will aid the managers in reducing the instances of information asymmetry in the market, thereby tackling the issue of principle agent problems within an organization. From the policy marking perspective, the results of this study will help the regulatory authorities in implementing the necessary rules, regulations and laws that will ensure increased participation from the corporate sector in disclosing their sustainability-related information.Originality/valueThis study is one of its kind to explore the impact of a moderating variable on the ESG-CFP relationship in the context of an emerging economy. It also contributes to the present stream of literature by providing both a theoretical and empirical support to the propositions under consideration.
User influence and microblog marketing: a case study of Sina Weibo in China
Authors: Wang Xiaoguang , Gao Yijun , Lu Zhuoyao 
Published: June 2023

PurposeMicroblogs are communication platforms for companies and consumers that challenge companies' brand marketing strategies. This paper provides a theoretical basis for expanding microblog applications and a practical basis for improving the effectiveness of brand marketing.Design/methodology/approachThe authors use factor analysis to extract the factors of microblog user influence and construct a structural equation model to reveal the interaction mechanism of the influencing factors. Additionally, the authors clarify the promotion and enhancement effects of these factors.FindingsMicroblog user influence can be converted into richness, interaction and value factors. The richness factor significantly affects the latter two, whereas the interaction factor does not affect the value factor.Research limitations/implicationsFirst, the sample used is limited to media industry practitioners. To increase generalizability, diverse groups should be included in future studies. Second, this model's theoretical explanatory ability can be further developed by adding other meaningful factors beyond the existing ones.Originality/valueThis study analyzes the factors of microblog user influence in China and validates the relevant elements. As a result, it improves the influence research on social media users and benefits the practice of information recommendation and microblog marketing.
Graphene-based resistant sensor decorated with Mn, Co, Cu for nitric oxide detection: Langmuir adsorption & DFT method
Authors: Mollaamin Fatemeh , Monajjemi Majid 
Published: June 2023

Purpose
The purpose of this paper is to investigate the ability of transition metals (TMs) of iron-, nickel- and zinc-doped graphene nanosheet for adsorption of toxic gas of nitric oxide (NO). The results of this paper have provided a favorable understanding of the interaction between TM-doped graphene nanosheet and NO molecule.

Design/methodology/approach
A high performance of TM-doped graphene nanosheet as a gas sensor is demonstrated by modeling the material’s transport characteristics by means of the Langmuir adsorption and three-layered ONIOM/ density functional theory method. The Langmuir adsorption model has been done with a three-layered ONIOM using CAM-B3LYP functional and LANL2DZ and 6–311G (d, p) basis sets by Gaussian 16 revision C.01 program towards the formation of of NO→TM(Mn, Co, Cu)-doped on the Gr nanosheet.

Findings
The changes of charge density for Langmuir adsorption of NO on Mn-, Co- and Cu-doped graphene nanosheet orderly have been achieved as: ΔQCo-doped = +0.309 >> ΔQMn-doped = −0.074 > ΔQCu-doped = −0.051. Therefore, the number of changes of charge density have concluded a more remarkable charge transfer for Mn-doped graphene nanosheet. However, based on nuclear magnetic resonance spectroscopy, the sharp peaks around Cu doped on the surface of graphene nanosheet and C19 close to junction of N2 and Co17 have been observed. In addition, Cu-doped graphene sheet has a large effect on bond orbitals of C8–Cu 17, C15–Cu 17 and C16–Cu17 in the adsorption of NO on the Cu-doped/Gr which has shown the maximum occupancy. The amounts of

ΔGads,NO→Mn−Co through IR computations based on polarizability have exhibited that

ΔGads,NO→Mn−Co has indicated the most energy gap because of charge density transfer from the nitrogen atom in NO to Mn-doped graphene nanosheet, though

ΔG(NO→Cu−C)0>

ΔG(NO→Co−C)0>ΔG(NO→Mn−C)0.

Originality/value
This research aims to explore the adsorption of hazardous pollutant gas of “NO” by using carbon nanostructure doped by “TM” of iron, nickel and zinc to evaluate the effectiveness of adsorption parameters of various TM-doped graphene nanosheets.
A comparison of deep and shallow models for the detection of induced seismicity
Authors: Goel Akshat , Gorse Denise 
Published: June 2023

AbstractCan an interpretable logistic regression model perform comparably to a deep learning model in the task of earthquake detection? In spite of the recent focus in academic seismological research on deep learning, we find there is hope that it can. Using data from the Groningen Gas Field in the Netherlands, relating to low‐magnitude induced seismicity, we build on a recently presented four‐input logistic regression model by adding to it four further statistically derived features. We evaluate the performance of our feature‐enhanced model relative to both the original logistic regression model (shallow machine learning model) and a deep learning model proposed by the same research group. We discover that at the signal‐to‐noise ratio of this earlier work, our enhanced logistic regression model in fact overall outperforms the deep learning model and displays no false negative errors. At the lower signal‐to‐noise ratios also considered here, while the number of false positive errors made by the logistic regression model increases, the number of undetected earthquakes remains zero. Though the number of false positives is for the highest imbalance ratios currently prohibitive, the benefit of our four additional features, which increases as the signal‐to‐noise ratio decreases, suggests that an interpretable model might be made to perform comparably to a more complex deep learning model at real‐world class imbalance ratios if further useful inputs could be identified.
Least‐squares reverse time migration using the most energetic source wavefields based on excitation amplitude imaging condition
Authors: Kim Sumin , Kim Young Seo , Chung Wookeen 
Published: June 2023

AbstractLeast‐squares reverse time migration, a linearized inversion problem, can provide high‐quality migration image by minimizing the misfit function, which is defined by predicted and observed data. According to the theory of data‐domain least‐squares reverse time migration, a forward source wavefield that is simulated with a fixed background velocity does not change during iterations. However, storing the forward source wavefield directly into computer memory involves substantial memory consumption. Although a source wavefield reconstruction technique can be applied during least‐squares reverse time migration iterations, this approach can increase the computational cost because of the need for additional wavefield simulations. To alleviate this computational issue in storing the forward source wavefield, we propose an efficient least‐squares reverse time migration scheme based on an excitation amplitude method. Unlike conventional excitation amplitude imaging conditions, the proposed least‐squares reverse time migration scheme enables one to reconstruct the forward source wavefield by convolving a source wavelet with the excitation amplitude of Green's function at the excitation time. With this excitation amplitude method, the forward source wavefield can be efficiently stored in the computer memory because the sizes of the excitation amplitude and excitation time maps are equal to the size of one snapshot. To validate the feasibility of our least‐squares reverse time migration scheme, we perform dot‐product tests and compare forward source wavefields, demigrated data and gradient vectors obtained by conventional least‐squares reverse time migration and our proposed least‐squares reverse time migration. Using numerical tests on synthetic data, we confirm that our least‐squares reverse time migration can produce high‐quality migration results with a significant improvement in computational efficiency with respect to performance time and memory consumption.
Novel broad‐spectrum bacteriophages against Xanthomonas oryzae and their biocontrol potential in rice bacterial diseases
Authors: Liu Mengjiao , Hu Ran , Xia Mian , He Xiaoqing , Jin Yi 
Published: June 2023

AbstractBacterial leaf blight (BLB) and bacterial leaf streak (BLS)—caused by Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), respectively—are two major bacterial diseases that threaten the safe production of rice, one of the most important food crops. Bacteriophages are considered potential biocontrol agents against rice bacterial pathogens, due to their host specificity and environmental safety. It is common for BLB and BLS to occur together in fields, which highlights the need for broad‐spectrum phages capable of infecting both Xoo and Xoc. In this study, two lytic broad‐spectrum phages (pXoo2106 and pXoo2107) that can infect various strains of Xoo and Xoc were assessed. Both phages belong to the class Caudoviricetes and one of them to the family Autographiviridae, while the other belongs to an unclassified family. Two phages alone or combined in a phage cocktail could effectively inhibit Xoo and Xoc growth in vitro. In an in vivo biocontrol experiment, the phage cocktail reduced the total CFU and significantly eased the symptoms caused by Xoo or Xoc. Our results suggest that pXoo2106 and pXoo2107 have a broad‐spectrum host range targeting different X. oryzae strains, and have strong biocontrol potential in field applications against both BLB and BLS.
Belgium: Political Developments and Data in 2022
Authors: BAUDEWYNS PIERRE , VANDELEENE AUDREY , WINTER LIEVEN DE , DERUETTE SERGE 
Published: June 2023

AbstractThe De Croo I Cabinet had to deal in 2022 with major issues related to international (war in Ukraine), European (energy supply) and national (inflation, social crisis and community problems) issues events. Given the coalition of parties that support the government, the policies developed by the government have sometimes taken a long time to be negotiated, decided, and implemented. In addition, the composition of the government had to be changed for various reasons, among others the vigorous opposition of the two main parties in Parliament (VB—Vlaams Belang—and N‐VA—Nieuw‐Vlaamse Alliantie).
Explainable machine learning framework to predict personalized physiological aging
Authors: Bernard David , Doumard Emmanuel , Ader Isabelle , Kemoun Philippe , Pagès Jean‐Christophe , Galinier Anne , Cussat‐Blanc Sylvain , Furger Felix , Ferrucci Luigi , Aligon Julien , Delpierre Cyrille , Pénicaud Luc , Monsarrat Paul , Casteilla Louis 
Published: June 2023

AbstractAttaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter‐parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty‐six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age‐specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow‐up. These data show that PPA is a robust, quantitative and explainable ML‐based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.
Recuperación temprana de la vegetación tras un incendio de mediana a alta severidad en los bosques andino‐patagónicos
Authors: Rago Maria Melisa , Urretavizcaya María Florencia , Morelli Pablo , Mohr Bell Diego , Guzmán Mario 
Published: June 2023

ResumenEn todo el mundo se está produciendo un cambio en los regímenes de incendios que afecta a la sucesión post‐fuego. En este contexto, cada vez es más necesario priorizar las zonas de restauración y centrarse en sus objetivos particulares. Evaluar el sotobosque emergente representa la primera aproximación a una revegetación post‐fuego, necesaria para establecer las directrices de restauración adecuadas. En este estudio, nos propusimos evaluar la respuesta inicial post‐fuego en un incendio de mediana a alta severidad en los bosques Andino‐Patagónicos. Para ello (1) se comparó la estructura de la comunidad vegetal entre las distintas severidades de incendio, (2) se evaluó la respuesta de la estructura de la comunidad vegetal a la interacción entre severidad del incendio y tipo de bosque, (3) se comparó la composición vegetal entre las categorías de severidad del incendio‐tipo de bosque y (4) se evaluó la regeneración de especies leñosas tras el incendio. Encontramos que la cobertura vegetal del estrato inferior comenzó a recuperarse rápidamente tras el incendio y en mayor medida en la severidad media que en la alta, mientras que la cobertura del estrato superior fue incipiente en la severidad media y nula en la alta. Las especies nativas predominaron en las parcelas quemadas y no quemadas, aunque fueron menores en las quemadas. Entre las formas de crecimiento, arbustos y árboles se vieron afectados de forma similar por el fuego, independientemente del tipo de bosque. La composición de la comunidad vegetal varió entre la mayoría de las categorías de severidad del incendio‐tipo de bosque. La frecuencia de especies leñosas que se regeneran por rebrote y por semillas fue menor en las parcelas quemadas que en las no quemadas. Estos resultados sugieren que aunque existe una pronta recuperación de la vegetación, la elevada cobertura de suelo expuesto y la pérdida del estrato superior de vegetación pueden favorecer la erosión y dificultar el establecimiento de árboles. Además, las especies arbóreas que sólo se regeneran a partir de semillas probablemente no se recuperarán de forma natural. Además, la presencia de especies exóticas con alto riesgo de invasión puede requerir control. Por lo tanto, dado que la severidad del incendio y el tipo de bosque pueden conducir a diferentes escenarios de recuperación natural post‐fuego, se deberían tomar diferentes acciones de restauración para promover sistemas resilientes para el futuro.
Abiotic and biotic determinants of West African termite mounds in altered land
Authors: Padonou Elie A. , Avocevou‐Ayisso Carolle M. A. , Akakpo Bokon Alexis , Sinsin Brice 
Published: June 2023

AbstractTermites contribute to the spatial heterogeneity of the ecological processes of the highly degraded lands called the bowé in West Africa, but the underlying determinants of the distribution of the mounds remain poorly understood. Thus, the study aims to determine the principal abiotic and biotic factors shaping mound densities on bowé in Benin, West Africa. A number of 64 plots were randomly sampled on sites with mounds and adjacent off‐mounds sites on bowé. Data were collected on abiotic (soil variables (pH, bulk density, particle sizes and soil organic carbon) and altitude) and biotic (the number of live termite mounds, plant richness and cover) factors. Correspondence analysis (CA) was applied to assess the pattern of vegetation and termite mounds on bowé; while principal component analysis (PCA) was used to assess the relationship between soil variables (pH, bulk density, particle size and soil organic carbon), altitude, number of mounds, plant richness and cover. Generalised mixed effects and linear mixed effects models with Poisson error distribution were used to analyse the variation in soil variables, number of mounds, species richness and cover between termite species. The findings revealed that the pattern of termite mounds on bowé was driven by altitude (topography) and soil clay content that contribute to termite activities as well as improving soil bulk density, plant species richness and cover. These factors were positively associated with all termite species considered, except for those belonging to the genus of Macrotermes.
Evaluation of a modified emergency surgical acuity score in predicting operative and non‐operative mortality and morbidity in an acute surgical unit
Authors: Wang Hogan , Luu Veronica , Jiang Eric , Kirkland Olivia , Kabir Shahrir , Davis Sean S. , Hugh Thomas J. 
Published: June 2023

AbstractBackgroundEmergency general surgery (EGS) patients have an increased risk of mortality and morbidity compared to other surgical patients. Limited risk assessment tools exist for use in both operative and non‐operative EGS patients. We assessed the accuracy of a modified Emergency Surgical Acuity Score (mESAS) in EGS patients at our institution.MethodsA retrospective cohort study from an acute surgical unit at a tertiary referral hospital was performed. Primary endpoints assessed included death before discharge, length of stay (LOS) >5 days and unplanned readmission within 28 days. Operative and non‐operative patients were analysed separately. Validation was performed using the area under the receiver operating characteristic (AUROC), Brier score and Hosmer‐Lemeshow test.ResultsA total of 1763 admissions between March 2018 and June 2021 were included for analysis. The mESAS was an accurate predictor of both death before discharge (AUROC 0.979, Brier score 0.007, Hosmer–Lemeshow P = 0.981) and LOS >5 days (0.787, 0.104, and 0.253, respectively). The mESAS was less accurate in predicting readmission within 28 days (0.639, 0.040, and 0.887, respectively). The mESAS retained its predictive ability for death before discharge and LOS >5 days in the split cohort analysis.ConclusionThis study is the first to validate a modified ESAS in a non‐operatively managed EGS population internationally and the first to validate the mESAS in Australia. The mESAS accurately predicts death before discharge and prolonged LOS for all EGS patients, providing a highly useful tool for surgeons and EGS units worldwide.
The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach
Authors: Montolío Alberto , Cegoñino José , Garcia‐Martin Elena , Pérez del Palomar Amaya 
Published: June 2023

AbstractPurposeThe macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer‐aided method to facilitate diagnosis and prognosis in MS.MethodsThis paper combines a cross‐sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10‐year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier.ResultsFor MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs.ConclusionWe present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non‐invasive, low‐cost, easy‐to‐implement and effective method.
Comparison of multiple sclerosis patients with or without rebound activity after fingolimod cessation: Five‐year clinical outcomes
Authors: Gulec Bade , Everest Elif , Gorkey Ogeday Derin , Koc Metehan , Tutuncu Melih , Saip Sabahattin , Siva Aksel , Uygunoglu Ugur 
Published: June 2023

AbstractBackgroundPatients with multiple sclerosis (MS) who discontinue fingolimod might present with rebound activity. The reasons for the development of rebound have been identified, but there are limited data on the long‐term clinical outcomes of these patients. This study aimed to compare the long‐term outcomes of patients with MS with and without rebound activity after fingolimod discontinuation.MethodsA total of 31 patients who discontinued fingolimod for various reasons with a minimum follow‐up of 5 years were included in the study. Of these, 10 were assigned to the rebound group and 21 to the non‐rebound group. Clinical and demographic data and 5‐year clinical outcomes of both groups were prospectively examined.ResultsAt fingolimod initiation, there were no significant differences in age, disease duration, and Expanded Disability Status Scale (EDSS) score. The annualized relapse rate (ARR) was significantly higher in the rebound group than in the non‐rebound group before the fingolimod treatment (p = 0.005). In the rebound group, EDSS scores 2 months after rebound treatment and at the 5‐year follow‐up were not significantly different than before fingolimod initiation (p = 0.14 and p = 0.46, respectively). The last recorded EDSS was significantly higher in the non‐rebound group than in the rebound group (3.6 ± 2.3 vs. 2.15 ± 1.4, p = 0.045). At the last follow‐up, one patient was diagnosed with secondary progressive multiple sclerosis in the rebound group (10%), and 11 patients were in the non‐rebound group (52.4%, p = 0.05).ConclusionWhen rebound activity is well‐monitored and treated after fingolimod discontinuation, no overall EDSS change is expected in the long‐term follow‐up.