Can senior executive characteristics improve carbon emission performance? Evidence from China

In recent years, the backbone of China's market economy has involved controlling corporate carbon emissions and reducing environmental pollution. This study aimed to investigate the relationship between the characteristics of senior managers and the carbon emission performance of enterprises. The empirical study used panel data on high-emission industries operating in the China A-shares market from 2014 to 2017. The results showed that the natural age, education level, and professional background of senior managers in high-emission enterprises were negatively correlated with the carbon emission performance of enterprises. However, in the case of large enterprises, there is no strong relationship between the natural age of executives and carbon emission performance, while professional background and education level are negatively correlated with carbon emission performance. However, natural age is significantly related to carbon emission performance in small and medium-sized enterprises. In the regional classification of enterprises, the natural age and professional background of the core executives of high-emitting enterprises in the eastern region are negatively correlated with carbon emission performance, while the professional background of the core executives of high-emitting enterprises in the central and western regions is positively correlated with carbon emission performance. Moreover, the natural age and educational level of executives are negatively correlated with carbon emission performance. To measure the carbon emission performance of an enterprise, the study used the balanced scorecard evaluation system (CEP). Meanwhile, it innovatively classifies and sorts the sample companies based on their overall size and distribution area, and analyzes the carbon performance and executive characteristics from two perspectives. The study provides suggestions and countermeasures for companies in China, especially those in high-emission industries, to help reduce pollution

Emotion regulation strategies and mental wellbeing among Chinese college students during COVID-19: the moderating roles of confinement and attentional bias

BackgroundDuring COVID-19, confinement measures were implemented to curb the epidemic spread. While effective in reducing infections, these measures likely deteriorated the psychological wellbeing of students due to school closures and isolation.MethodsThis study analyzed 13,109 valid questionnaires from individuals aged 18–24 years (mean 20.28 ± 1.518) to explore how emotion regulation strategies (cognitive reappraisal and expression suppression) impact mental wellbeing through attentional biases (positive and negative), with confinement as three moderators.ResultsCognitive reappraisal was positively associated with mental wellbeing, whereas expression suppression showed a negative association. Positive attentional bias was associated with higher wellbeing, whereas negative bias was linked to lower levels of it. Negative attentional bias was linked to a stronger positive association between cognitive reappraisal and mental wellbeing, as well as a more pronounced negative association? with expression suppression. Confinement was associated with a stronger positive relation between cognitive reappraisal and mental wellbeing, while corresponding to a more negative relation with expression suppression.ConclusionOur findings highlight the complex interplay between emotion regulation strategies and mental wellbeing during confinement. Cognitive reappraisal and positive attentional bias are associated with protective effects, while expression suppression and negative bias are linked to detrimental effects. Confinement measures, despite their positive impact on physical health, significantly modulate these effects. Tailored interventions considering individual differences and contexts are needed to support mental wellbeing in similar crises

Biomaterials-based anti-inflammatory treatment strategies for Alzheimer’s disease

The current therapeutic drugs for Alzheimer's disease only improve symptoms, they do not delay disease progression. Therefore, there is an urgent need for new effective drugs. The underlying pathogenic factors of Alzheimer's disease are not clear, but neuroinflammation can link various hypotheses of Alzheimer's disease; hence, targeting neuroinflammation may be a new hope for Alzheimer's disease treatment. Inhibiting inflammation can restore neuronal function, promote neuroregeneration, reduce the pathological burden of Alzheimer's disease, and improve or even reverse symptoms of Alzheimer's disease. This review focuses on the relationship between inflammation and various pathological hypotheses of Alzheimer's disease; reports the mechanisms and characteristics of small-molecule drugs (e.g., nonsteroidal anti-inflammatory drugs, neurosteroids, and plant extracts); macromolecule drugs (e.g., peptides, proteins, and gene therapeutics); and nanocarriers (e.g., lipid-based nanoparticles, polymeric nanoparticles, nanoemulsions, and inorganic nanoparticles) in the treatment of Alzheimer's disease. The review also makes recommendations for the prospective development of anti-inflammatory strategies based on nanocarriers for the treatment of Alzheimer's disease

Oxidative esterification of acetol with methanol to methyl pyruvate over hydroxyapatite supported gold catalyst: Essential roles of acid-base properties

ABSTRACT(#br)Acetol is a major light oxygenate and readily produced from staged or fast pyrolysis of lignocellulose biomass. Herein we report that acetol can be selectively converted to methyl pyruvate, an important fine chemical, through oxidative esterification over Au-based catalysts. Detailed experimental studies showed that Au on amphoteric supports with appropriate strength and balanced ratio of acid and base sites can facilitate the desired oxidative-esterification pathway without accelerating undesired aldol-condensation or Cannizzaro reactions. In particular, hydroxyapatite (with a Ca/P ratio of 1.62) supported Au achieved 87% selectivity to methyl pyruvate at an acetol conversion of 62%

Structural basis for the ligand-dependent activation of heterodimeric AHR-ARNT complex

The aryl hydrocarbon receptor (AHR) possesses an extraordinary capacity to sense and respond to a wide range of small-molecule ligands, ranging from polycyclic aromatic hydrocarbons to endogenous compounds. Upon ligand binding, AHR translocates from the cytoplasm to nucleus, forming a transcriptionally active complex with aryl hydrocarbon receptor nuclear translocator (ARNT), for DNA binding and initiation of gene expression programs that include cellular detoxification pathways and immune responses. Here, we examine the molecular mechanisms governing AHR’s high-affinity binding and activation by a diverse group of ligands. Crystal structures of the AHR-ARNT-DNA complexes, bound with each of six established AHR ligands, including Tapinarof, 6-formylindolo[3,2-b]carbazole (FICZ), benzo[a]pyrene (BaP), β-naphthoflavone (BNF), Indigo and Indirubin, reveal an unconventional mode of subunit assembly with intimate association between the PAS-B domains of AHR and ARNT. AHR’s PAS-B domain utilizes eight conserved residues whose dynamic rearrangements account for the ability to bind to ligands through hydrophobic and π-π interactions. Our findings further reveal the structural underpinnings of a ligand-driven activation mechanism, whereby a segment of the AHR protein undergoes a structural transition from chaperone engagement to ARNT heterodimer stabilization, to generate the transcriptionally competent assembly. Our results provide key information for the future development of AHR-targeting drugs

Construction and Application of Energy Footprint Model for Digital Twin Workshop Oriented to Low-Carbon Operation

To address the difficulty of accurately characterizing the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in low-carbon workshops, a low-carbon-operation-oriented construction method of the energy footprint model (EFM) for a digital twin workshop (DTW) is proposed. With a focus on considering the fluctuations in equipment energy consumption and the correlation between multiple pieces of equipment at the workshop production process level (CBMEatWPPL), the EFM of a DTW is obtained to characterize the dynamic evolution of whole energy consumption in the workshop. Taking a production unit as a case, on the one hand, an EFM of the production unit is constructed, which achieved the characterization and visualization of the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in the production unit; on the other hand, based on the EFM, an objective function of workshop energy consumption is established, which is combined with the tool life, robot motion stability, and production time to formulate a multi-objective optimization function. The bee colony algorithm is adopted to solve the multi-objective optimization function, achieving collaborative optimization of cross-equipment process parameters and effectively reducing energy consumption in the production unit. The effectiveness of the proposed method and constructed EFM is demonstrated from the above two aspects

Predicting Tunneling-Induced Ground Collapse Based on TBM Operational Data and Geological Data

Collapses are the most sensational types of events and frequently the ones that cause the most serious consequences in tunneling operations. It often occurs because of insufficient geological studies and the limitations of experience-based decision-making. To cope with those problems, we proposed a data-driven model based on the tunnel boring machine operational data and the in situ geological information to forecast tunneling-induced ground collapse. In the proposed model, we offered a general data process flow diagram to process engineering data. Three machine learning classifiers, k-nearest neighbors, support vector classifier, and random forests were adopted for collapse prediction. The performance of the three classifiers was verified based on the data from the Yinsong water conveyance tunnel. The results illustrated that the proposed data-driven model was sufficient for the studying task with 90% of the collapsed zones being identified on average. The contributions of this paper are to provide a reliable data process flow diagram to process engineering data and offer an accurate and robust model for identifying collapses. </jats:p