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As a core model for promoting technological industrialization, school‐enterprise collaborative innovation faces multi‐dimensional risks from technology maturity fluctuations, market policy changes, and multi‐party interest games. Traditional risk assessment methods like AHP and SVM rely on static indicators and single‐objective optimization, struggling with dynamic constraints. While multi‐objective evolutionary algorithms handle objective conflicts, they suffer from low convergence efficiency in high‐dimensional spaces and poor real‐time performance. Data sparsity and limited emergency scenario generation further restrict industrial applicability. This study proposes a technology transfer risk assessment framework integrating deep adversarial reinforcement learning with multi‐objective optimization. We develop a physically‐constrained adversarial generation network to simulate technology failure distributions and market fluctuation patterns, generating high‐fidelity risk scenarios. Combined with the Proximal Policy Optimization algorithm, we design a dynamic decision‐making mechanism that simultaneously optimizes risk control costs, technology transfer efficiency, and patent revenue. Experiments in semiconductor manufacturing and new energy batteries demonstrate significantly improved assessment accuracy and decision‐making speed. The framework achieves a 92% decision correction rate with 1.2‐h response delay in emergencies, overcoming traditional methods' limitations in dynamic multi‐objective collaboration. Adaptive reference point strategy and adversarial training effectively address data distribution bias and noise interference, providing practical intelligent decision support for school‐enterprise innovation. This study develops a deep adversarial reinforcement learning framework for dynamic technology transfer risk assessment. The research outcome DAGAN‐MORL has a misjudgment rate 6.3%, a delay of 1.8 h, and a profit error of 7.9%, leading at the industrial level, providing intelligent support for university‐enterprise collaborative innovation.

Smart manufacturing systems are undergoing deep digitalization and networking. However, two core bottlenecks persist: (1) Heterogeneous multimodal data integration (vision, RF, vibration), and (2) Virtual-real system collaboration barriers. These challenges directly restrict the evolution towards high-level intelligence. Current multimodal learning technologies face key challenges such as insufficient modeling of physical constraints in industrial data, low reliability of generative adversarial network (GAN) cross-modal mapping, and limited efficiency of Transformer architecture deployment at the edge. Existing virtual-real alignment methods, lacking the ability to model dynamic spatiotemporal correlations, struggle to adapt to flexible manufacturing requirements. This study proposes a cross-modal virtual-real alignment training framework based on a Transformer-GAN hybrid architecture. It adaptively integrates features from multiple sources such as vision, radio frequency, and vibration through a dynamic weight allocation mechanism. It combines a progressive training strategy to balance the collaborative optimization of virtual simulation and physical data, and designs a lightweight model compression scheme to achieve efficient deployment on edge devices. Experiments show that this framework significantly improves the quality of multimodal generation and the accuracy of virtual-real trajectory alignment. It breaks through the limitations of traditional methods in terms of industrial-level accuracy, real-time performance, and economic indicators, significantly improving multimodal generation quality (FID = 18.9 vs. CycleGAN’s 42.7) and virtual-real alignment accuracy (VAM-IoU = 88.3%). Edge deployment achieves 32 ms latency on Jetson AGX with 65.2% model compression, enabling real-time control in welding and assembly scenarios. The framework’s industrial efficacy is validated through a 23.8% OEE improvement and 40% annual cost reduction in automotive production lines. SNR driven dynamic fusion: the real-time modal weight assignment (0.2–0.8) reduces the virtual real alignment fluctuation to ±0.9%, which is superior to the static fusion method of 12.7%. Edge optimization deployment: 65.2% model compression is achieved by structured pruning, and the Jetson AgX is delayed by 32 ms to meet the real-time control requirements of the production line. Sustainable manufacturing benefits: 23.8% improvement in overall equipment efficiency (OEE) and 40% reduction in annual cost were verified in the FAW Volkswagen pilot project, which are in line with SDG 9 and 13 goals.

Suicide rate among rural elderly is the highest among all age groups in China, yet little is known about the suicide risks in this rapidly growing vulnerable population. This matched case-control psychological autopsy study was conducted during June 2014 to September 2015. Consecutive samples of suicides aged 60 or above were identified in three provinces (Shandong, Hunan, and Guangxi) in China. Living comparisons were 1:1 matched with the suicides in age (±3 years old), gender, and living location. Risk factors included demographic characteristics, being left-behind, mental disorder, depressive symptoms, stressful life events, and social support. A total of 242 suicides and 242 comparisons were enrolled: 135 (55.8%) were male, mean (s.d.) age was 74 (8) years. The most frequently used suicide means were pesticides (125, 51.7%) and hanging (95, 39.3%). Independent risks of suicide included unstable marital status [odds ratio (OR) 4.19, 95% confidence interval (CI) 1.61-10.92], unemployed (compared with employed, OR 4.43, 95% CI 1.09-17.95), depressive symptoms (OR 1.34, 95% CI 1.21-1.48), and mental disorder (OR 6.28, 95% CI 1.75-22.54). Structural equation model indicated that the association between being left-behind and suicide was mediated by mental disorder, depressive symptoms, stressful life events, and social support. Unstable marital status, unemployed, depressive symptoms, and mental disorder are independent risk factors for suicide in rural elderly. Being left-behind can elevate the suicide risk through increasing life stresses, depressive symptoms, mental disorder, and decreasing social support. Elderly suicide may be prevented by restricting pesticides, training rural physicians, treating mental disorders, mitigating life stress, and enhancing social connection.

In order to improve the security and efficiency of image encryption systems comprehensively, a novel chaotic S-box based image encryption scheme is proposed. Firstly, a new compound chaotic system, Sine-Tent map, is proposed to widen the chaotic range and improve the chaotic performance of 1D discrete chaotic maps. As a result, the new compound chaotic system is more suitable for cryptosystem. Secondly, an efficient and simple method for generating S-boxes is proposed, which can greatly improve the efficiency of S-box production. Thirdly, a novel double S-box based image encryption algorithm is proposed. By introducing equivalent key sequences r, t related with image ciphertext, the proposed cryptosystem can resist the four classical types of attacks, which is an advantage over other S-box based encryption schemes. Furthermore, it enhanced the resistance of the system to differential analysis attack by two rounds of forward and backward confusion-diffusion operation with double S-boxes. The simulation results and security analysis verify the effectiveness of the proposed scheme. The new scheme has obvious efficiency advantages, which means that it has better application potential in real-time image encryption.

Substitution-boxes (S-Boxes) are important non-linear components in block cryptosystem, which play an important role in the security of cryptosystems. Constructing S-Boxes with a strong cryptographic feature is an important step in designing block cipher systems. In this paper, a novel algorithm for constructing S-Boxes based on a new compound chaotic system is presented. Firstly, the new chaotic system, tent–logistic system, is proposed, which has better chaotic performance and wider chaotic range than the tent and logistic system, and can not only increase the randomness of the chaotic sequences but also expand the key space of cryptosystems. Secondly, a novel linear mapping is employed to construct the initial S-Box. Then, the permutation operation on the initial S-Box is performed by using chaotic sequence generated with the tent–logistic system, which improves the cryptographic features of the S-Box. The idea behind the proposed work is to make supplementary safe S-box. Detail tests for cryptographic strength of the proposed S-Box are performed by using different standard benchmarks. The test results and performance analysis show that our proposed S-Box has very smaller values of linear probability (LP) and differential probability (DP) and a satisfactory average value of nonlinearity compared with other S-Boxes, showing its excellent application potential in block cipher system.

Objective MicroRNAs (miRNAs) from mesenchymal stem cells (MSC)-derived extracellular vesicles (MSCs-EVs), including exosomes, are known to participate in different diseases. However, the function of miR-301b-3p from MSCs-EVs on the chemoresistance of gastric cancer (GC) cells remains poorly characterized. Thus, we aim to explore the role of MSCs-EVs-derived miR-301b-3p in multidrug resistance of GC cells. Methods Cisplatin (DDP)/vincristine (VCR)-resistant and sensitive GC clinical samples were harvested to detect expression of miR-301b-3p and thioredoxin interacting protein (TXNIP). MSCs were respectively transfected with miR-301b-3p oligonucleotides and/or TXNIP plasmids to extract the EVs, which were then co-cultured with multidrug-resistant GC cells. Then, P-glycoprotein (P-gp) and multidrug resistance-associated protein (MRP), IC50, proliferation, migration, and apoptosis of resistant GC cells were determined. The tumor growth was observed in nude mice. Targeting relationship between miR-301b-3p and TXNIP was confirmed. Results miR-301b-3p was upregulated, and TXNIP was downregulated in DDP/VCR-resistant GC tissues and cells. MSC-EVs induced drug resistance, proliferation, and migration and inhibited apoptosis of DDP/VCR-resistant GC cells in vitro, as well as facilitated tumor growth in vivo. Inhibition of miR-301b-3p or upregulation of TXNIP reversed the promoting effect of MSC-EVs on DDP/VCR resistant GC cells to DDP/VCR resistance and malignant behaviors. The effects of MSC-EVs carrying miR-301b-3p inhibition on DDP/VCR-resistant GC cells were reversed by TXNIP downregulation. TXNIP was confirmed as a target gene of miR-301b-3p. Conclusion miR-301b-3p from MSCs-EVs inhibits TXNIP to promote multidrug resistance of GC cells, providing a novel insight for chemotherapy in GC.

The frequency characteristics of wavelets and the vanishing moments of wavelet filters are both important parameters of wavelets. Clarifying the relationship between the wavelet frequency characteristics and the vanishing moments of the wavelet filter can provide a theoretical basis for selecting the best wavelet. In this paper, the frequency characteristics of wavelets were analyzed by mathematical modeling, the mathematical relationship between wavelet frequency characteristics and vanishing moments was clarified, the optimal wavelet base function was selected hierarchically according to the amplitude frequency characteristics of ECG signal, and an accurate notch filter was realized according to the frequency characteristics of the noise. The experimental results showed that the optimal orthogonal wavelet analysis for the ECG signals with different frequency characteristics could make the high frequency energy distribution sparser, and the method proposed in this paper could effectively preserve the singularity of the signal and reduce the signal distortion.

This paper presents an improved cryptanalysis of a chaos-based image encryption scheme, which integrated permutation, diffusion, and linear transformation process. It was found that the equivalent key streams and all the unknown parameters of the cryptosystem can be recovered by our chosen-plaintext attack algorithm. Both a theoretical analysis and an experimental validation are given in detail. Based on the analysis of the defects in the original cryptosystem, an improved color image encryption scheme was further developed. By using an image content–related approach in generating diffusion arrays and the process of interweaving diffusion and confusion, the security of the cryptosystem was enhanced. The experimental results and security analysis demonstrate the security superiority of the improved cryptosystem.

Tumor drug resistance remains a significant barrier to effective cancer treatment, undermining the clinical efficacy of current therapies through adaptive and evolving mechanisms. Cuproptosis, a recently discovered form of programmed cell death, emerges as a promising tumor suppressor by targeting mitochondrial metabolic pathways, offering a novel strategy to combat drug resistance. However, targeting mitochondrial metabolism presents challenges due to the essential physiological roles of mitochondria in normal tissues, raising concerns about potential on-target toxicity. Recent studies have revealed the dynamic role of non-coding RNAs (ncRNAs) in modulating susceptibility to cuproptosis via epigenetic alterations, potentially serving as key factors in therapeutic response. In this review, we first summarize the complex interplay between chemotherapeutic resistance and cancer progression/relapse. Then, we outline the key molecular mechanisms underlying cuproptosis, its upstream epigenetic regulators, and the downstream effects on drug resistance. Finally, this review discusses the latest findings on how ncRNAs influence resistance to chemotherapy, immunotherapy, and targeted therapy by modulating cuproptosis. This review aims to untangle the regulatory mechanisms of ncRNA-mediated epigenetic modifications on cuproptosis, highlighting the critical role of the ncRNA-cuproptosis interaction network in tumor drug resistance progression. These insights provide a conceptual foundation for developing ncRNA-targeted therapies in conjunction with cuproptosis-inducing approaches.

As digital finance ushers into a new era, carbon emissions in China have been peaking, highlighting the necessity of carbon neutrality. This work uses a dynamic spatial Durbin model, combined with a mediating effect model of the data from 30 provinces from 2011 to 2019, to explore the impact, transmission paths, and spatio-temporal heterogeneity of digital finance (Df) on carbon emission intensity (Cg). Meanwhile, the validation explores the mediating role of technological innovation (Rd), industrial restructuring (Is), and entrepreneurial effects (Es) in the process of digital finance influencing green low-carbon development. The empirical results show that: first, digital finance (Df) has a promoting effect on regional CO 2 reduction capacity (Cg), and this conclusion still holds under multiple robustness tests; second, digital finance (Df) can promote the regional CO 2 reduction capacity (Cg) through two paths, namely, promoting technological progress (Rd) and optimizing industrial structure (Is); third, the impact of inclusive digital finance on CO 2 emission intensity is heterogeneous. By analyzing regions with different economic development levels, we found that digital inclusive finance in the eastern region can enhance CO 2 reduction capacity, while in the central and western regions, the impact is not significant. Given this situation, China, to achieve carbon neutrality, should boost financial development’s ability to reduce carbon emission, promote technological progress, and optimize the industrial structure, thus forming a green and low-carbon economic cycle. This paper fills the research gap on how digital finance can effectively promote green development while exerting economic effects, and at the same time, enriches the literature on factors influencing green and low-carbon development.