Beijing Technology and Business University

The spent black tea extract was utilized in order to synthesize the spent black tea silver nanoparticles (SBT-AgNPs). Various parameters were tested to yield the best production of SBT-AgNPs. The characterization was conducted by X-Ray diffraction, Scanning electron microscopy, Zeta potential and energy dispersive X-ray (EDX). The XRD analysis showed hkl planes corresponding to (111), (200), (220), (311) planes at 2θ theta deg 38.3°, 40.8°, 64.5°, and 74.2°. The scanning electron microscopy reported the plate like round shaped morphology of the AgNPs. The zeta potential was examined to be-17.5 mV and a size distribution by intensity of 157.6 d. nm was observed. The EDX was employed to determine the purity of samples by reporting a strong peak of silver (Ag). The degradation activity was examined by photocatalytic removal of methylene blue and malachite green dyes from textile wastewater. The textile wastewater showed a decrease of methylene blue by 25% and 58.3%. The malachite green was also reduced by 33.3% and 60%, which was remarkably significant owing to the presence of the complex factor in the natural environment. The study sets a promising record to harbor the full potential of available food waste resource, such as spent black tea to form SBT-AgNPs and its application in the dye removal from textile waste. The multifaceted outcomes of this study resulted in an eco-friendly procedure, thereby reusing the waste material for environmental cleanup.

With the advent of the digital economy and the post-epidemic era, China’s retail enterprises face significant crises and challenges. Enhancing both the in-role and extra-role service performance of sales staff in physical stores is crucial for preserving competitive advantages and sustainable development of retail enterprises. Based on Conservation of Resources theory, this study proposes a moderated mediation model that elucidates how the perceived service climate impacts employee in-role and extra-role service performance through work engagement, as well as the moderating effect of perceived overqualification. Data were collected from 598 physical store sales staff and 117 direct supervisors across 19 retail enterprises using a multi-wave, multi-source survey. Hierarchical regression analysis confirmed our hypotheses, demonstrating that perceived service climate enhances employee in-role and extra-role service performance via the partial mediating effect of work engagement. Moreover, perceived overqualification positively moderates not only the link between perceived service climate and work engagement but also the mediating effects of work engagement between perceived service climate and in-role and extra-role service performance, separately. The theoretical and practical implications of these findings are discussed in detail.

Different from administrative law enforcement that yields immediate effects on social governance, environmental justice is often considered to have limited impact on enterprises’ green behavior due to its passive and neutral nature. This paper employs propensity score matching-difference in differences method (PSM-DID) to analysis how environmental justice affects the green total factor productivity (GTFP) of enterprises based on quasi natural experiment of the establishment of environmental courts (ECs) in China. The results confirm that the establishment of ECs promotes enterprise’s GTFP, and this conclusion remains robust to multiple scenarios. Our mechanism tests show that ECs enhance enterprise’s GTFP by boosting green innovation and weakening political connections. In addition, the effect of ECs is prominent with greater public supervision and environmental enforcement intensity. Further inspections exhibit that the positive influence of ECs is more significant in capital-intensive industries, eastern regions, and regions with better legal environments. This paper provides new empirical evidence for revealing the connection between environmental justice and corporate green transition, and presents pertinent policy implications on the advancement of environmental justice and the acceleration of corporate green development.

Oily sensitive skin is complex and requires accurate identification and personalized care. However, the current classification method relies on subjective assessment. This study aimed to classify skin type and subtype using objective biophysical parameters to investigate differences in skin characteristics across anatomical and morphological regions. This study involved 200 Chinese women aged 17–34 years. Noninvasive capture of biophysical measures and image analysis yielded 104 parameters. Key classification parameters were identified through mechanisms and characteristics, with thresholds set via statistical methods. This study identified the optimal ternary value classification method for dividing skin types into dry, neutral, and oily types based on tertiles of biophysical parameters and, further, into barrier-sensitive, neurosensitive, and inflammatory-sensitive types. Oily sensitive skin shows increased sebum, follicular orifices, redness, dullness, wrinkles, and porphyrins, along with a tendency for oiliness and early acne. Subtypes exhibited specific characteristics: barrier-sensitive skin was rough with a high pH and prone to acne; neurosensitive skin had increased TEWL (Transepidermal Water Loss) and sensitivity; and inflammatory-sensitive skin exhibited a darker tone, with low elasticity and uneven redness. This study established an objective classification system for skin types and subtypes using noninvasive parameters, clarifying the need for care for oily sensitive skin and supporting personalized skincare.

With the popularization of social networks, fake news is also widely and rapidly spreading, which poses a great threat to the Internet. Therefore, how to detect fake news automatically and efficiently has become an urgent problem to be solved. However, the existing approaches mostly focus on the explicit features (images and text) and deep fusions, without considering potential features such as text emotion and image category. To find a solution to this issue, we propose a Potential Features Fusion Network (PFFN), which models the explicit and potential features at the same time. To exploit the potential image features, we introduce a mixture of experts structure to process the news image separately, which can best use the relationships between the news image category and fake news detection. Besides, we also extract emotion features as potential text features and fuse them with explicit text features. Finally, we establish an attention-based feature fusion network to fuse the potential features with the explicit features, which can obtain a multi-modal fusion feature of a piece of news and thus further improve the performance. We make experiments on four public datasets (Weibo16, Weibo19, Twitter, and PolitiFact), the results compared with the baseline approaches demonstrate that our PFFN has a better performance. Our code is available at https://github.com/Wang-bupt/PFFN

This paper addresses the critical challenge of enhancing robotic real-time sensing and navigation capabilities in complex environments through advanced 3D modeling and semantic mapping technologies. The research integrates RGBD camera-based 3D modeling with synchronous positioning techniques to achieve precise environmental surface classification and monitoring. A novel approach combining multi-view recognition with improved segmentation quality is presented, along with a delay fusion method to address positioning errors in real-time visual-aided inertial navigation. The methodology demonstrates particular effectiveness in constructing semantic maps within dynamic complex environments, though success relies heavily on high-quality image processing and accurate synchronous positioning. Two experimental validations were conducted to investigate object perception and recognition mechanisms for robot-environment interaction. The study further explores adaptive imaging technology in robotic vision sensors and examines its future applications. This research contributes significantly to the field of autonomous robotics by enhancing environmental understanding and interaction capabilities, while acknowledging the technical constraints in real-world implementations. The findings suggest promising directions for improving robotic perception systems in complex operational scenarios.

Cardiovascular diseases (CVD) represent a primary global health challenge. Poor dietary choices and lifestyle factors significantly increase the risk of developing CVD. Legumes, recognized as functional foods, contain various bioactive components such as active peptides, protease inhibitors, saponins, isoflavones, lectins, phytates, and tannins. Studies have demonstrated that several of these compounds are associated with the prevention and treatment of cardiovascular diseases, notably active peptides, saponins, isoflavones, and tannins. This review aims to analyze and summarize the relationship between bioactive compounds in legumes and cardiovascular health. It elaborates on the mechanisms through which active ingredients in legumes interact with risk factors for cardiovascular diseases, such as hypertension, hypercholesterolemia, endothelial dysfunction, and atherosclerosis. These mechanisms include, but are not limited to, lowering blood pressure, regulating lipid levels, promoting anticoagulation, enhancing endothelial function, and modulating TLR4 and NF-κB signaling pathways. Together, these mechanisms emphasize the potential of legumes in improving cardiovascular health. Additionally, the limitations of bioactive components in legumes and their practical applications, with the goal of fostering further advancements in this area were discussed.

In an intensive kinship society, individuals naturally have a sense of trust in their relatives and remain wary of strangers (a sense of distrust). However, an individual’s perception of collective norms, kinship intensity, and group affiliations may change with the diverse social interactions in his own interpersonal circle. The current research examines whether occupational diversity can reduce the gap between trust in relatives and trust in strangers. With a large national sample (Study 1), the findings indicated that direct diverse social networks have an inhibiting effect on trust in relatives and a facilitating effect on trust in strangers. Experimental studies found that focusing on the network diversity of one’s occupational ties can affect trust in relatives negatively and strangers positively (Study 2), and the positive perception of occupational diversity acted as a moderating variable for diversity to function (Study 3). The findings underline the “kinship estrangement effect” of occupational diversity in shaping an individual’s trust in relatives and nonrelatives, especially in family-oriented culture.

A pyrene-derived fluorescent probe (P4CG) was designed and synthesized for the purpose of detecting protamine and trypsin activity. The anionic probe self-assembled with protamine, driven by electrostatic and hydrophobic interactions, exhibiting a sensing behavior towards protamine in a fluorescence ratiometric manner. The assay demonstrated high sensitivity, with a limit of detection (LOD) of 13.8 ng/mL, and exhibited selectivity in the HEPES buffer solution. Moreover, the P4CG-protamine complex enables the monitoring of trypsin activity with satisfactory sensitivity and selectivity. The presence of the trypsin inhibitor resulted in the inhibition of the hydrolysis of protamine, which in turn led to a diminished fluorescence recovery. Consequently, this assay can be employed for the screening of trypsin inhibitors.

Formulating tailored emission reduction policies for each Chinese province is crucial due to regional differences in carbon emission evolution patterns. This paper proposes a novel and comprehensive research framework that integrates data envelopment analysis (DEA), Tobit regression, and system dynamics (SD) model to analyze the influence factors and evaluate provincial emission reduction policies while considering regional differences. The DEA method assesses each province's development resource allocation and carbon emission efficiency. Based on the DEA results, each provinces’ key emission influencing factors can be derived combining with Tobit regression and sensitivity analysis of SD. Policies are then selected based on these factors to gauge their effectiveness. SD method is used to simulate carbon emissions under different policy scenarios in the future. The analysis results present obvious differences in resource allocation and regional characteristics among provinces. Qinghai's emission reduction potential has been preliminarily explored as an example. Energy structure, industry structure, energy intensity, forest coverage, and R&D input intensity are its main influencing factors for carbon emission. The forest carbon sink plays a significant role. The emission reduction of the integrated scenario is not a linear sum of all other scenarios. To ensure the completion of the neutralization goal, further adjustments to the long-term policy and extra measures are needed.

Carbon emission research based on input-output tables (IOTs) has received attention, but data quality issues persist due to inconsistencies between the sectoral scopes of energy statistics and IOTs. Specifically, China’s official energy data are reported at the industry level, whereas IOTs are organized by product sectors. Valid IOT-based environmental models require consistent transformation from industry-level to product-level emissions. However, most existing studies overlook this necessary transformation, leading to substantial estimation errors. This study addresses this issue by developing a high-quality, product-level emissions dataset for China, grounded in robust product technology identification derived from IOTs. Our new emissions dataset, aligned with Chinese national IOTs, covers 29 to 34 product sectors across 7 benchmark years from 1997 to 2020. It includes data from 4 to 5 energy sectors and detailed emissions for 18 types of fossil fuels, using both IPCC-default and two China-specific emission factors. This inventory improves product-sector emission accounting and can be integrated into IOT-based climate and energy models, serving as a fundamental database for energy and emission analysis.