Chongqing Technology and Business University

Green innovation is essential for sustainable development, especially in China’s Specialized-Refined-Differentiated-Innovative (SRDI) enterprises. Family-owned SRDI firms, in particular, have attracted attention due to their de-familization strategies and their influence on green innovation. Our study analyzes panel data from 2016 to 2021 for listed SRDI family firms to investigate how de-familization in management rights and ownership impacts green innovation. Using socio-emotional wealth (SEW) theory and a fixed-effects model, we find that de-familization significantly negatively affects green innovation, with corporate governance serving as a mediating factor. Digital transformation moderates these negative effects, while market concentration exacerbates them. These impacts are more pronounced in firms before being designated as "Little Giants," those receiving higher government subsidies, those located in eastern regions, or those not classified as major polluters. This research provides actionable insights for SRDI family firms to strategically manage de-familization, optimize resource allocation, implement customized governance strategies, and promote sustainable growth.

In recent years, it has become widely acknowledged that heavy metals are often present in oil-contaminated sites. This study utilized three specific types of microorganisms with different functions to construct a composite bacterial consortium for treating lubricant-Cr(VI) composite pollutants. The selected strains were Lysinbacillus fusiformis and Bacillus tropicus. The Back Propagation Neural Network-genetic algorithm was employed to optimize the secondary bacterial addition time to 67 h and the strain ratio to 2:1. The optimized process involved the use of 4.6 g/L glucose and ammonium oxalate as electron donors. After 6 days of treatment with the composite consortium, the removal rates of 1500 mg/L lubricating oil and 50 mg/L chromium reached 90.3% and 84.2%, respectively. Initial analysis using three-dimensional fluorescence to examine the changes in extracellular polymers in the bacteria when exposed to chromium-lubricating oil, showed that 30 mg/L Cr(VI) could induce the secretion of extracellular protein-like substances. These substances may be directly or indirectly involved in the biological detoxification mechanism of chromium. The synergistic removal of complex pollutants has the potential to transform previous “unilateral” removal studies and enhance bioremediation efficiency.

A systematic numerical investigation is carried out to understand the effect of variation of the equilibrium pressure pedestal structure on the H-mode plasma response to the applied resonant magnetic perturbation (RMP) field in tokamaks. The plasma response to the n = 1–4 (n is the toroidal mode number) RMP fields is computed and analyzed utilizing the toroidal MHD code MARS-F (Liu et al. in Phys. Plasmas 7:3681, 2000). The edge safety factor is fixed while varying the pressure pedestal height or width in the single-null (SN) and double-null (DN) divertor-like configurations. The key results are: (i) the optimal coil current phasing for ELM control, between two rows of RMP coils, is insensitive to the assumed pedestal height or width, (ii) with the same coil current and optimal coil phasing and assuming the Spitzer model for the plasma resistivity, a higher pedestal height generally leads to less edge-peeling response of the plasma and thus more challenge to control the edge localized mode (ELM), and (iii) a wider pressure pedestal generally increases the edge-peeling response and hence favors ELM control. These results hold particularly well for n > 1 RMPs, for both the SN and DN plasmas.

Phototheranostic nanoplatforms that perform simultaneous optical imaging and phototherapy through light activation are considered a promising approach for early diagnosis, surgical guidance, and precision treatment of cancer. In this work, we develop a novel flower-like gap-enhanced Raman tags (for brief, PMF-GERTs) wrapped with polydopamine (PDA)-functionalized molybdenum disulfide (MoS2) nanosheets. In PMF-GERTs, 4,4′-biphenyldithiol (BPDT) Raman reporter molecules are embedded in the nanogap between the gold core and the flower-like shell, and PDA-functionalized MoS2 nanosheets (PDA/MoS2) were wrapped on the surface of the flower-shaped shell. Photothermal and photodynamic experiments show that PDA/MoS2 nanosheets significantly improve the photothermal performance and photodynamic response ability of Raman tags in the NIR-II region. Under the irradiation of 1064 nm laser (1 W/cm²), the PMF-GERTs solution can heat up to 66 °C within 300 s, and the photothermal conversion efficiency reaches 43.6%. Moreover, PMF-GERT also has excellent photothermal stability and photodynamic properties and can perform effective phototherapy on 4T1 tumor cells. In Raman spectra and mapping imaging experiments, PMF-GERTs have strong enhanced Raman signals, lower detection thresholds and long-time physiological environment stability (72 h). In addition, PMF-GETRs also have excellent performance in simulating biological tissues and biological Raman mapping imaging. This novel Raman tag is expected to be used to develop phototheranostic nanoplatform that integrates Raman imaging diagnosis and photothermal and photodynamic therapy. Graphical abstract

Acute rejection (AR) is a significant complication in liver transplantation, impacting graft function and patient survival. Kupffer cells (KCs), liver-specific macrophages, can polarize into pro-inflammatory M1 or anti-inflammatory M2 phenotypes, both of which critically influence AR outcomes. Angiopoietin-like 4 (ANGPTL4), a secretory protein, is recognized for its function in regulating inflammation and macrophage polarization. This study investigates the effects of ANGPTL4 on KC polarization through cellular interactions between hepatocytes (HCs) and KCs. Using a rat orthotopic liver transplantation model, we observed reduced ANGPTL4 expression during AR, whereas increased ANGPTL4 levels were linked to immune tolerance. Administration of ANGPTL4 recombinant protein improved liver function, suppressed inflammation, and promoted M2 polarization of KCs. Co-culture experiments demonstrated that hepatocyte-derived ANGPTL4 significantly modulates KC polarization and inflammatory responses, mainly by inhibiting the NF-κB signaling pathway. The results emphasize the promise of ANGPTL4 as a therapeutic target to reduce AR and improve liver transplant outcomes by influencing hepatocyte-KC interactions.

Objective Depression among older adults is increasingly becoming a global public health issue. Along with the rapid development of digital information technology, the Internet has profoundly changed the lifestyle of older adults. However, few studies have focused on the mental health of rural middle-aged and older adult populations, and this study aims to explore the impact of Internet use on depressive symptoms among rural middle-aged and older adults. Methods Our study is based on 10,946 Chinese rural participants aged 45 and above in the 2018 China Health and Retirement Longitudinal Study (CHARLS). Depression is measured by a 10-item Centre for Epidemiologic Studies (CES-D10), and multiple linear regression and the propensity score matching (PSM) method are used to examine the effect of Internet use on depression in Chinese rural middle-aged and older adults. Results Internet use significantly reduced depression in rural middle-aged and older adults. The mechanism was that Internet use improved mental health by improving social interaction and enhancing social support. Furthermore, desk computer, laptop computer, and cellphone use were all significantly associated with lower depression scores compared to non-Internet users. And the more the content of Internet use, the significantly lower the level of depression in rural middle-aged and older adults. Heterogeneity analysis showed that Internet use reduced depression more pronounced in the groups of males, those in elementary and secondary education, low-medium income, and aged under 75. Conclusion The paper confirms that Internet use significantly reduces depression, with social interaction and social support playing a mediating role. The results of the study show that strengthening rural Internet infrastructure can promote healthy aging in rural areas.

Existing semi-supervised medical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch. However, current copy-paste methods have three limitations: (1) training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information; (2) low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data; (3) the segmentation performance in low-contrast and local regions is less than optimal. We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy (SADT), which enhances feature diversity and learns high-quality features to overcome these problems. To be more precise, SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data, which prevents the loss of rare labeled data. We introduce a bi-directional copy-paste mask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision. For the mixed images, Deep-Shallow Spatial Contrastive Learning (DSSCL) is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas. In this procedure, the features retrieved by the Student Network are subjected to a random feature perturbation technique. On two openly available datasets, extensive trials show that our proposed SADT performs much better than the state-of-the-art semi-supervised medical segmentation techniques. Using only 10% of the labeled data for training, SADT was able to acquire a Dice score of 90.10% on the ACDC (Automatic Cardiac Diagnosis Challenge) dataset.

In recent years, mathematical programs with complementarity constraints (MPCC) and a non-Lipschitz objective function have been introduced and are now more prevalent than locally Lipschitz MPCC. This paper proposes a smoothing partial augmented Lagrangian (SPAL) method to tackle this problem. However, due to the disruption of the complementary structure’s integrity by this method, proving its convergence becomes exceptionally challenging. We have achieved global convergence of the SPAL method. Specifically, we demonstrate that the accumulation point of the sequence generated by the SPAL method can be a strongly stationary point under the Mangasarian-Fromovitz qualification (MPCC-MFQ) and the boundedness of the multiplier corresponding to the orthogonal constraint. Moreover, if the aforementioned multiplier is unbounded, the accumulation point can be a Clarke stationary point under MPCC-MFQ and a suitable assumption. Numerical experiments indicate that the SPAL method surpasses existing methods in terms of the quality of accumulation points and running times.

The coupled development of new-type urbanization (NTU) and rural revitalization (RR) represents a critical proposition put forth by China for forging a novel paradigm of urban-rural relationship. Initially, this study employs the entropy method to quantify NTU and RR. Subsequently, it carries out a comprehensive analysis concerning their coupled relationship with the relative development degree model (RDDM), coupled coordination degree model (CCDM), Dagum Gini coefficient, kernel density estimation, and Tobit model. The findings drawn from the study indicate from 2011 to 2022, NTU and RR in the Yangtze River Economic Belt (YREB) have exhibited a consistent upward trajectory, but lagging NTU disorders are widely distributed and numerous. The coupled coordination degree (CCD) of NTU and RR constantly improves, transitioning from moderate imbalance to primary coordination, exhibiting a spatial distribution of a "high in the east and low in the west". The relative disparity between the coupled development of NTU and RR demonstrates a slowly narrowing trend, whereas the absolute disparity indicates an expanding trend. Among the influencing factors, the development of the agricultural industry exerts the most significant positive impact on the coupled development, whereas the level of financial support for agriculture exerts a dampening effect, which is heterogeneous in nature.

Social media users often fail to control their social media use when it disturbs with other daily goals (e.g., study, work, chores). Previous studies have demonstrated the negative impact of social media self-control failure on wellbeing, but the mechanism underlying the negative impact remains unclear. Based on the kindling model, this study examined the chain mediating effect of daily hassle and subjective vitality on the association between social media self-control failure and affective wellbeing (i.e., positive affect and negative affect). A sample of 490 Chinese social media users (Mage = 24.2, SDage = 8.3, 72.4% female) completed an online survey. Structural equation modeling results showed that: (1) Social media self-control failure was associated with lower positive affect and higher negative affect directly. (2) Their direct associations were serially mediated by daily hassle and subjective vitality. The results indicated that intermittent social media self-control failure might have durable effects on affective wellbeing through increasing perceptions of daily hassle and depleting one’s subjective vitality.

It is valuable to explore those hidden patterns from imbalanced data. In imbalanced data, skewed distribution of the classes makes minority classes to be hardly noticed. Existing classifiers easily suffer the perturbation caused by the skewed distribution so that they obtain unstable prediction and poor performance. Accordingly, to mitigate the perturbation, we utilize a score mechanism to employ a classifier being concerned about minority classes. Through calculating the conformity of the observed data, the score of the data conformity is obtained. And using the obtained score to classify highly imbalanced data. Following that, our classifier explores minority classes on the learning regions yielded by the score, instead of exploring them on the original data regions. Experimental results show that our classifier outperformed the competitors in classification performance and efficiency, moreover, it learned more compact boundaries separating minority classes from majority classes than the competitors did. Results also show that our classifier does not exhibit an exponential classification time at classifying large volume data with highly imbalanced ratio. We do not impose any restrictive classifier assumptions on both imbalance data and the calculation regarding the score of the data conformity. Additionally, we propose to sufficiently utilize the learning regions yielded by the score for better classification boundaries, instead of using the original data regions, since those hard-to-observe minority classes can be well perceived on the learning regions, meanwhile, there can tighten majority classes and minority classes so that the margins between them are significantly displayed.

In response to the impacts of climate change and the intensity of human activities in the alpine meadow region, there is an urgent need to determine the ecological quality and its drivers in alpine meadow areas. In this paper, Shangri-La was adopted as an example, the spatial and temporal evolution patterns of the ecological quality in Shangri-La were determined in both natural and social dimensions, and the contributions of various driving factors were analyzed. The conclusions are as follows: (1) the natural status index of Shangri-La from 2000 to 2020 generally showed a spatial distribution pattern that decreased from the central townships toward the north and south, and the social pressure index was irregularly distributed in high-value areas and continuously distributed in low-value areas. (2) From 2000 to 2020, the areas with high values of the ecological quality index were mainly distributed in central Shangri-La, with a maximum value of 0.91, while the low values were largely distributed in some townships in the north and south, with a minimum value of 0.26. (3) In the driving factors, the influences of the normalized difference vegetation index (NDVI) and net primary productivity (NPP) were greater than those of the other factors, among which the NDVI attained the largest mean value of 0.452, while the relative humidity (RHU) attained the lowest value of 0.036. (4) In terms of relative contributions, evapotranspiration (EVP) and precipitation (TEM) shifted from a positive drive to a negative drive from south to north. The contribution of the temperature to the ecological quality was the highest, at 64%. The spatial heterogeneity in the contributions of human disturbance activity factors to the ecological quality varied significantly, with the largest negative driving contribution of the NPP, at − 42.36%. The results could provide a basis for regional ecological quality protection and restoration.

A rational design of water-stable and high-efficiency MOFs-based electrocatalysts thus achieving durable sensitive electrochemical sensors remains a great challenge. Herein, water-stable Co2+ doped-Cu2+ and 1,3,5-benzene tricarboxylic coordination polymers (Cu-BTC@Co)...

Distinguishing vibration signals associated with different levels of gear damage, combined with the challenging operating environment of wind turbines, complicates the collection of sufficient data for effective fault diagnosis. This paper proposes a few-shot learning (FSL) based graph neural network (GNN) for evaluating gear tooth fracture levels within small datasets.The short-time fourier transform (STFT) is used to convert the original signal into two-dimensional data for preprocessing. Fault severity features, extracted by a convolutional neural network (CNN), are then input into the GNN for severity classification. A natural wind turbine experiment platform was developed to simulate various operating conditions. To further validate the proposed method, comparative experiments were conducted using Siamese Networks, Matching Networks, and Relation Networks. The results demonstrate that the proposed method outperforms these alternatives in evaluating gear damage severity.

Multi-unmanned aerial vehicle (UAV) collaborative task planning and distribution path planning are the core content of agricultural UAV logistics distribution. In this study, the multi-UAV collaborative task planning and the distribution path planning were discussed, and such constraint conditions as UAV load capacity, battery capacity and flight time were comprehensively considered, aiming to reduce the number of UAVs and their power consumption. To ensure the safe and efficient completion of multi-UAV logistics distribution tasks, 3D agricultural ultralow space was subjected to environment modeling, and a bilevel planning model for collaborative planning of UAV distribution route and flight path was constructed. Then, an improved particle swarm optimization (PSO) algorithm with the improved learning factor and inertia coefficient was designed on the basis of PSO framework, and the global optimal solution in the current iteration was improved using variable neighborhood descent search. The feasibility of the proposed algorithm was verified by analyzing a practical case. With the central city area of XX City as the study area, 1 logistics & freight transportation center was taken as the central warehouse (coordinates: 50, 50, unit: km) and 50 intelligent express cabinets as the express cabinets of UAVs. The obtained results were comparatively analyzed with those acquired through the basic PSO algorithm. The results manifest that the proposed algorithm performs better than the compared algorithms. The improved PSO algorithm is superior to the basic PSO algorithm in aspects of total UAV flight distance, number of UAVs used and algorithm convergence time, indicating that the model and algorithm established in this study are feasible and effective.

Based on the sample data of 63 higher education institutions in Chongqing from 2013 to 2022, this paper adopts the entropy value method and the coupling coordination degree evaluation model to measure the coupling coordination degree and coupling mechanism between higher education and industrial economy in Chongqing. The results of the study found that: (1) the level of higher education in Chongqing shows a trend of rapid growth followed by basic stability and slight decline during the sample period, with the public undergraduate > the public specialized > the private undergraduate > the private specialized, and the central urban area > the new area of the city proper > the two clusters. Meanwhile, the level of industrial economy and its three subsystems in Chongqing also showed an upward trend, in which industrial efficiency > industrial scale > industrial structure. (2) The coupling coordination degree between higher education and industrial economy in Chongqing is at a medium level, with a phenomenon of high constant high and low constant low. It is characterized by an olive-shaped structure with fewer areas at the two ends, more areas in the middle for the quantity distribution, and one area higher than two clusters for the geospatial distribution. (3) There is a mutual promoting effect between higher education and industrial economy in Chongqing. The development of higher education, industrial economy, industrial efficiency, industrial scale, and industrial structure can improve the coupling coordination degree. Except for the industrial scale, other variables have a positive moderating effect on the coupling coordination degree. In addition, the heterogeneity analysis shows that the positive effect of higher education on the coupling coordination degree is higher in private institutions, undergraduate institutions, two clusters, and new area of the city proper.

Unconfined Compressive Strength (UCS) is one of the most important mechanical properties in geomechanics and is crucial for reliable geo-mechanical modeling of geological formations. Traditional methods for determining UCS involve a great deal of laboratory testing on core samples, which can be very expensive, or collecting a large amount of data from well logs, which could be equally burdensome. Recently, with increasing demands on real-time, efficient UCS predictions in industries dealing with construction, mining, civil engineering, and even petroleum exploration, the need to realize innovative ways has grown. This paper, therefore, embarks on the introduction of a state-of-the-art machine learning framework that embeds the Multi-Layer Perceptron architecture with advanced meta-heuristic optimization techniques, namely Beluga Whale Optimization and Black Widow Optimization, to predict unconfined compressive strength with high accuracy. That is, unlike ANNs, which on occasions easily get bogged down in difficulties of parameter optimizations and slow convergence, it can enable fast and sure predictions, hence its eminently suitable use in real-time decision-making. Employing an extensive dataset compiled from previous scholarly studies, outstanding predictive performance was realized for this model. Therefore, the best-optimized model of MLBO2 was the superior predictor that gave a maximum ${R}^{2}$ of 0.998 and minimum RMSE of 1.309 in the test phase. These results confirm that the model is efficient in providing highly accurate UCS predictions with immense added advantages over the existing techniques and can contribute much to the geomechanical domains.