bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of digital transformation and earnings management on ESG performance: evidence from Chinese listed enterprises
Authors: Wang Lang , Hou Sheng 
Published: January 2024

AbstractThe improvement of enterprise ESG performance is one of the key driving forces to achieve the goal of economic and social green development. There is a gap between knowledge and practice in the ESG performance of Chinese enterprises, and digital transformation (DT) provides new ideas for ESG development. The research purpose of this paper is to explore the impact mechanism of DT on ESG and the specific path of DT to drive ESG. It provides a reference for listed enterprises to rely on DT to empower their sustainable development capability. This paper takes the panel data of A-share listed enterprises from 2011 to 2021 as the sample and measures the core indicators using the text mining method, modified Jones model, and Roy-Chowdhury model. On this basis, using a combination of econometric models and qualitative comparative analysis, we empirically analyze the impact mechanisms of DT on ESG as well as the specific grouping paths that drive ESG performance. The main conclusions are shown as follows. First, DT can significantly reduce ESG, with an impact coefficient of − 0.013, which is significant at the 5% level. It reflects that the DT of enterprises at this stage has certain deficiencies. There is a matching lag in the enterprise's internal organizational resources. The entry of digital technology will have a certain impact on traditional operations, and the high uncertainty of DT adds some hidden costs to the enterprise. Secondly, there is an indirect suppression effect of accrued earnings management (AEM) in the transmission mechanism of DT affecting ESG. It is verified that DT can reduce information asymmetry and inhibit EM activities, thus reducing the impact on ESG. Finally, a total of six configurations achieved high ESG valuations. High technology practice-low performance manipulation; digital intelligence-low performance manipulation; digital intelligence-digital resources; digital resources-inadequate digital infrastructure.; high technology practice-bottom technology deficiency; digital intelligence-high performance manipulation. Through configuration analysis, the suppression effect of EM is further verified. The lack of AEM is usually the core condition of the high-valuation group. Meanwhile, digital intelligence, digital resources, and digital technology practice can drive the improvement of enterprise ESG. The instrumental variables approach and robustness tests support these findings.
In vitro gastrointestinal gas monitoring with carbon nanotube sensors
Authors: Vasquez Sahira , Angeli Martina Aurora Costa , Polo Andrea , Costantini Alice , Petrelli Mattia , Avancini Enrico , Di Cagno Raffaella , Gobbetti Marco , Gaiardo Andrea , Valt Matteo , Lugli Paolo , Petti Luisa 
Published: January 2024

AbstractIn vitro simulators of the human gastrointestinal (GI) tract are remarkable technological platforms for studying the impact of food on the gut microbiota, enabling continuous and real-time monitoring of key biomarkers. However, comprehensive real-time monitoring of gaseous biomarkers in these systems is required with a cost-effective approach, which has been challenging to perform experimentally to date. In this work, we demonstrate the integration and in-line use of carbon nanotube (CNT)-based chemiresitive gas sensors coated with a thin polydimethylsiloxane (PDMS) membrane for the continuous monitoring of gases within the Simulator of the Human Microbial Ecosystem (SHIME). The findings demonstrate the ability of the gas sensor to continuously monitor the different phases of gas production in this harsh, anaerobic, highly humid, and acidic environment for a long exposure time (16 h) without saturation. This establishes our sensor platform as an effective tool for real-time monitoring of gaseous biomarkers in in vitro systems like SHIME.
Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach
Authors: Torresani Michele , Rocchini Duccio , Ceola Giada , de Vries Jan Peter Reinier , Feilhauer Hannes , Moudrý Vítězslav , Bartholomeus Harm , Perrone Michela , Anderle Matteo , Gamper Hannes Andres , Chieffallo Ludovico , Guatelli Enrico , Gatti Roberto Cazzolla , Kleijn David 
Published: January 2024

AbstractThe ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique “Structure from Motion” (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an $$R^2$$

R
2

of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao’s Q, Coefficient of Variation, Berger–Parker index, and Simpson’s D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao’s Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.
FEM simulations for double diffusive transport mechanism hybrid nano fluid flow in corrugated enclosure by installing uniformly heated and concentrated cylinder
Authors: Bilal S. , Shah Imtiaz Ali , Khan Ilyas , Al-Otaibi Shaha , Rahimzai Ariana Abdul 
Published: January 2024

AbstractGeneration of fluid flow due to simultaneous occurrence of heat and mass diffusions caused by buoyancy differences is termed as double diffusion. Pervasive applications of such diffusion arise in numerous natural and scientific systems. This article investigates double diffusion in naturally convective flow of water-based fluid saturated in corrugated enclosure and containing hybrid nano particles composed of Copper (Cu) and Alumina (Al2O3). Impact of uniformly applied magnetic field is also accounted. To produce thermosolutal convective potential circular cylinder of constant radius is also adjusted by providing uniform temperature and concentration distributions. Finite element approach is capitalized to provide solution of utilized governing equations by utilizing Multiphysics COMSOL software. Wide-range of physical parameters are incorporated to depict their influence on associated distributions (velocity, temperature and concentration). Interesting physical quantities like Nusselt number, Sherwood numbers are also calculated against involved sundry parameters. It is note worthily observed that maximum strength of stream lines $${(\psi }_{max})$$

(
ψ

max

)

is 3.3 at $$\phi =0$$

ϕ
=
0

and drops to 1.2 when $$\phi $$
ϕ
is increased to 0.04. Furthermore, in the hydrodynamic case (Ha = 0), it is observed that the velocity field exhibits an increasing trend compared to the hydromagnetic case $$\left(Ha\ne 0\right),$$

H
a
≠
0

,

which is proved from the attained values of stream-function i.e., $${|\psi |}_{max}=11$$

|
ψ
|

max

=
11

(in the absence of a magnetic field) and $${|\psi |}_{max}=3.5$$

|
ψ
|

max

=
3.5

(in the presence of a magnetic field). It is revealed from the statistics of Nusselt number that increase in volume fraction of nano particles from 0 to 0.4, heat flux coefficient upsurges up to 7% approximately. Since, present work includes novel physical aspects of thermosolutal diffusion generated due to induction of hybrid nanoparticles in water contained in corrugated enclosure, so this study will provide innovative thought to the researchers to conduct research in this direction.
Factor structure and psychometric properties of the Perceived Stress Scale in Russian adolescents
Authors: Marakshina Julia , Adamovich Timofey , Vasin Georgy , Ismatullina Victoria , Lobaskova Marina , Malykh Artem , Kolyasnikov Pavel , Tabueva Anna , Zakharov Ilya , Malykh Sergey 
Published: January 2024

AbstractThis study involved a psychometric analysis of the 10-item Perceived Stress Scale (PSS-10). To investigate the Russian version of the PSS-10 for adolescents, 3530 adolescents aged 13–17 years were recruited. Confirmatory factor analysis revealed that the data corresponded to the expected two-factor configuration. Psychometric properties and factor structure were evaluated. As expected, the PSS-10 included two factors: perceived helplessness and perceived self-efficacy. Internal consistency demonstrated acceptable values (Cronbach’s alpha was 0.82 for perceived helplessness, 0.77 for perceived self-efficacy, and 0.80 for the overall PSS score). Measurement invariance across sexes was assessed, and configural and metric invariance were confirmed. The developed diagnostic tool can be used both in the school system to alleviate the negative consequences of academic stress in adolescents and, in the future, in other areas, particularly in clinical practice.
An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection
Authors: Kantipudi M. V. V. Prasad , Kumar N. S. Pradeep , Aluvalu Rajanikanth , Selvarajan Shitharth , Kotecha K 
Published: January 2024

AbstractDetection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.
On chip synthesis of a pH sensitive gefitinib anticancer drug nanocarrier based on chitosan/alginate natural polymers
Authors: Alizadeh Hossein , Ahmadi Mazaher , Heydari Shayesteh Omid 
Published: January 2024

AbstractIn this research, using a microfluidic chip, a nanocarrier for the anticancer drug gefitinib was synthesized. Chitosan and alginate natural polymers were utilized for the synthesis of the nanocarrier. The synthesis of the nanocarrier comprises the interaction of secondary amine functional groups of gefitinib molecules with carboxylate functional groups of alginate polymer to form the primary nucleus followed by the formation of the nanocarrier through the self-assembly of chitosan and alginate polymers on a fabricated microfluidic chip. The chip was fabricated by laser engraving poly(methyl methacrylate) polymer sheets. The nanocarrier was characterized by FT-IR, DLS, SEM, and TEM techniques. The synthesized nanocarrier had a size distribution of 5.30 ± 2.60 nm and the encapsulation efficiency percent was 68.4% in the optimum conditions. The loading efficiency was calculated as 50.2 mg g−1 of nanocarrier. Drug release studies showed that the nanocarrier is sensitive to pH and releases more gefitinib in acidic environments. Cytotoxicity of the synthesized nanocarrier was studied on the A549 non-small cell lung cancer, and the MTT test showed that the synthesized nanocarrier has a lower IC50 value than the free drug. Also, the cytotoxicity studies showed that the materials used for the synthesis of nanocarrier do not show significant cytotoxicity. Compared to the previously reported method, the developed microfluidic-assisted method showed advantages such as a faster synthesis procedure and comparable encapsulation efficiency and loading capacity.
Angle modulated two-dimensional single cell pulsed-field gel electrophoresis for detecting early symptoms of DNA fragmentation in human sperm nuclei
Authors: Kaneko Satoru , Takamatsu Kiyoshi 
Published: January 2024

AbstractWe here developed a novel angle-modulated two-dimensional single cell pulsed-field gel electrophoresis (2D-SCPFGE). Variations in current-application-time and rotation angle generated different alignments of DNA fibers and segments. After the first run, the specimen was turned by 150° (2D-SCPFGE-0–150) to detect naturally occurring the earliest stage of DNA fragmentation or 75° (2D-SCPFGE-0–75) to analyze artificially induced cleavage. The former revealed that a part of long chain fibers remained at the origin and long segments were still tangled in the bundle of elongated fibers after the first run. The latter visualized the dose-dependent cleavage of DNA by EcoR1. Multicycle 2D-SCPFGE was useful for generating 2D-alignments of single nuclear DNA fibers, which is the first step for visualization of single-strand breaks on stretched fibers. To date, many articles have accepted the pathogenetic significances of DNA fragmentation in human sperm for male infertility and congenital anomaly. It is necessary to perform multivariate analyses of not only earliest-stage DNA fragmentation but also other types of damage, including single-strand breaks, in sequential DNA fibers. 2D-SCPFGE is the fundamental tool for understanding single nuclear DNA damages.
Cancer associated fibroblasts serve as an ovarian cancer stem cell niche through noncanonical Wnt5a signaling
Authors: Fang Yiming , Xiao Xue , Wang Ji , Dasari Subramanyam , Pepin David , Nephew Kenneth P. , Zamarin Dmitriy , Mitra Anirban K. 
Published: January 2024

AbstractFrequent relapse and chemoresistance cause poor outcome in ovarian cancer (OC) and cancer stem cells (CSCs) are important contributors. While most studies focus exclusively on CSCs, the role of the microenvironment in providing optimal conditions to maintain their tumor-initiating potential remains poorly understood. Cancer associated fibroblasts (CAFs) are a major constituent of the OC tumor microenvironment and we show that CAFs and CSCs are enriched following chemotherapy in patient tumors. CAFs significantly increase OC cell resistance to carboplatin. Using heterotypic CAF-OC cocultures and in vivo limiting dilution assay, we confirm that the CAFs act by enriching the CSC population. CAFs increase the symmetric division of CSCs as well as the dedifferentiation of bulk OC cells into CSCs. The effect of CAFs is limited to OC cells in their immediate neighborhood, which can be prevented by inhibiting Wnt. Analysis of single cell RNA-seq data from OC patients reveal Wnt5a as the highest expressed Wnt in CAFs and that certain subpopulations of CAFs express higher levels of Wnt5a. Our findings demonstrate that Wnt5a from CAFs activate a noncanonical Wnt signaling pathway involving the ROR2/PKC/CREB1 axis in the neighboring CSCs. While canonical Wnt signaling is found to be predominant in interactions between cancer cells in patients, non-canonical Wnt pathway is activated by the CAF-OC crosstalk. Treatment with a Wnt5a inhibitor sensitizes tumors to carboplatin in vivo. Together, our results demonstrate a novel mechanism of CSC maintenance by signals from the microenvironmental CAFs, which can be targeted to treat OC chemoresistance and relapse.
Measuring out quasi-local integrals of motion from entanglement
Authors: Lu Bohan , Bertoni Christian , Thomson Steven J. , Eisert Jens 
Published: January 2024

AbstractQuasi-local integrals of motion are a key concept underpinning the modern understanding of many-body localisation, a phenomenon in which interactions and disorder come together. Despite the existence of several numerical ways to compute them—and in the light of the observation that much of the phenomenology of many properties can be derived from them—it is not obvious how to directly measure aspects of them in real quantum simulations; in fact, hard experimental evidence is still missing. In this work, we propose a way to extract the real-space properties of such quasi-local integrals of motion based on a spatially-resolved entanglement probe able to distinguish Anderson from many-body localisation from non-equilibrium dynamics. We complement these findings with a rigorous entanglement bound and compute the relevant quantities using tensor networks. We demonstrate that the entanglement gives rise to a well-defined length scale that can be measured in experiments.
Multi-animal 3D social pose estimation, identification and behaviour embedding with a few-shot learning framework
Authors: Han Yaning , Chen Ke , Wang Yunke , Liu Wenhao , Wang Zhouwei , Wang Xiaojing , Han Chuanliang , Liao Jiahui , Huang Kang , Cai Shengyuan , Huang Yiting , Wang Nan , Li Jinxiu , Song Yangwangzi , Li Jing , Wang Guo-Dong , Wang Liping , Zhang Yaping , Wei Pengfei 
Published: January 2024

AbstractThe quantification of animal social behaviour is an essential step to reveal brain functions and psychiatric disorders during interaction phases. While deep learning-based approaches have enabled precise pose estimation, identification and behavioural classification of multi-animals, their application is challenged by the lack of well-annotated datasets. Here we show a computational framework, the Social Behavior Atlas (SBeA) used to overcome the problem caused by the limited datasets. SBeA uses a much smaller number of labelled frames for multi-animal three-dimensional pose estimation, achieves label-free identification recognition and successfully applies unsupervised dynamic learning to social behaviour classification. SBeA is validated to uncover previously overlooked social behaviour phenotypes of autism spectrum disorder knockout mice. Our results also demonstrate that the SBeA can achieve high performance across various species using existing customized datasets. These findings highlight the potential of SBeA for quantifying subtle social behaviours in the fields of neuroscience and ecology.
Wireless power and information dual transfer system via magnetically coupled resonators
Authors: He Xiangning , Liu Sheng , Wu Jiande , Feng Yue , Wang Ruichi , Li Wuhua , Weng Wanying 
Published: January 2024

AbstractHigh-efficiency medium-range wireless power transfer using magnetically coupled resonators requires a wireless data link between the contactless coils to regulate power. Multiplexing the power transfer channel as the information channel is a cost-effective solution for the communication. However, existing technologies cannot transmit data across the medium-range magnetically coupled resonators channel without substantially affecting power transfer. Here we show a power-electronics-converters based wireless power and information dual transfer system in which the information signals are modulated on one dc side of the inverter/rectifier, and transmitted through a conventional medium-range wireless power transfer system, and then demodulated on the other dc side. Using the frequency mixer characteristic of the inverter/rectifier, information is modulated onto the sideband of the power carrier and transmitted through the medium-range channel. Finally, we prototyped a 6.78 MHz system capable of transferring 45 W power across a one-meter distance with 62% efficiency and 60 kb/s bitrate for half-duplex communication.
Liver ACSM3 deficiency mediates metabolic syndrome via a lauric acid-HNF4α-p38 MAPK axis
Authors: Xiao Xiao , Li Ruofei , Cui Bing , Lv Cheng , Zhang Yu , Zheng Jun , Hui Rutai , Wang Yibo 
Published: January 2024

AbstractMetabolic syndrome combines major risk factors for cardiovascular disease, making deeper insight into its pathogenesis important. We here explore the mechanistic basis of metabolic syndrome by recruiting an essential patient cohort and performing extensive gene expression profiling. The mitochondrial fatty acid metabolism enzyme acyl-CoA synthetase medium-chain family member 3 (ACSM3) was identified to be significantly lower expressed in the peripheral blood of metabolic syndrome patients. In line, hepatic ACSM3 expression was decreased in mice with metabolic syndrome. Furthermore, Acsm3 knockout mice showed glucose and lipid metabolic abnormalities, and hepatic accumulation of the ACSM3 fatty acid substrate lauric acid. Acsm3 depletion markedly decreased mitochondrial function and stimulated signaling via the p38 MAPK pathway cascade. Consistently, Acsm3 knockout mouse exhibited abnormal mitochondrial morphology, decreased ATP contents, and enhanced ROS levels in their livers. Mechanistically, Acsm3 deficiency, and lauric acid accumulation activated nuclear receptor Hnf4α-p38 MAPK signaling. In line, the p38 inhibitor Adezmapimod effectively rescued the Acsm3 depletion phenotype. Together, these findings show that disease-associated loss of ACSM3 facilitates mitochondrial dysfunction via a lauric acid-HNF4a-p38 MAPK axis, suggesting a novel therapeutic vulnerability in systemic metabolic dysfunction.