Evaluation of anti-smoking television advertising on tobacco control among urban community population in Chongqing, China

Background China is the largest producer and consumer of tobacco in the world. Considering the constantly growing urban proportion, persuasive tobacco control measures are important in urban communities. Television, as one of the most pervasive mass media, can be used for this purpose. Methods The anti-smoking advertisement was carried out in five different time slots per day from 15 May to 15 June in 2011 across 12 channels of Chongqing TV. A cross-sectional study was conducted in the main municipal areas of Chongqing. A questionnaire was administered in late June to 1,342 native residents aged 18–45, who were selected via street intercept survey. Results Respondents who recognized the advertisement (32.77 %) were more likely to know or believe that smoking cigarettes caused impotence than those who did not recognize the advertisement (26.11 %). According to 25.5 % of smokers, the anti-smoking TV advertising made them consider quitting smoking. However, females (51.7 %) were less likely to be affected by the advertisement to stop and think about quitting smoking compared to males (65.6 %) (OR = 0.517, 95 % CI [0.281–0.950]). In addition, respondents aged 26–35 years (67.4 %) were more likely to try to persuade others to quit smoking than those aged 18–25 years (36.3 %) (OR = 0.457, 95 % CI [0.215–0.974]). Furthermore, non-smokers (87.4 %) were more likely to find the advertisement relevant than smokers (74.8 %) (OR = 2.34, 95 % CI [1.19–4.61]). Conclusions This study showed that this advertisement did not show significant differences on smoking-related knowledge and attitude between non-smokers who had seen the ad and those who had not. Thus, this form may not be the right tool to facilitate change in non-smokers. The ad should instead be focused on the smoking population. Gender, smoking status, and age influenced the effect of anti-smoking TV advertising on the general population in China

Enhanced Lipid Production in Chlamydomonas reinhardtii by Co-culturing With Azotobacter chroococcum

The green algae, Chlamydomonas reinhardtii, is one of the model species used to study lipid production, although research has focused on nitrogen-deficient cultures, that inhibit the development of biomass by C. reinhardtii and limit lipid production. In this study, Azotobacter chroococcum was added to the algal culture to improve lipid accumulation and productivity of C. reinhardtii. The maximum lipid content and production of C. reinhardtii in the co-culture were 65.85% and 387.76 mg/L, respectively, which were 2.3 and 5.9 times the control's levels of 29.11% and 65.99 mg/L, respectively. The maximum lipid productivity of C. reinhardtii in the co-culture was 141.86 mg/(L·day), which was 19.4 times the control's levels of 7.33 mg/(L·day). These increases were attributed to the enhanced growth and biomass and the change in the activity of enzymes related to lipid regulation (ACCase, DGAT, and PDAT). Compared to the conventional strategy of nitrogen deprivation, A. chroococcum added to the culture of C. reinhardtii resulted in higher lipid accumulation and activity, greater efficiency in the conversion of proteins to lipids, higher biomass, and increased growth of C. reinhardtii. Therefore, using A. chroococcum to improve the growth and biomass of C. reinhardtii is an efficient, rapid, and economically viable strategy for enhancing lipid production in C. reinhardtii

Distribution-sensitive Information Retention for Accurate Binary Neural Network

Model binarization is an effective method of compressing neural networks and accelerating their inference process. However, a significant performance gap still exists between the 1-bit model and the 32-bit one. The empirical study shows that binarization causes a great loss of information in the forward and backward propagation. We present a novel Distribution-sensitive Information Retention Network (DIR-Net) that retains the information in the forward and backward propagation by improving internal propagation and introducing external representations. The DIR-Net mainly relies on three technical contributions: (1) Information Maximized Binarization (IMB): minimizing the information loss and the binarization error of weights/activations simultaneously by weight balance and standardization; (2) Distribution-sensitive Two-stage Estimator (DTE): retaining the information of gradients by distribution-sensitive soft approximation by jointly considering the updating capability and accurate gradient; (3) Representation-align Binarization-aware Distillation (RBD): retaining the representation information by distilling the representations between full-precision and binarized networks. The DIR-Net investigates both forward and backward processes of BNNs from the unified information perspective, thereby providing new insight into the mechanism of network binarization. The three techniques in our DIR-Net are versatile and effective and can be applied in various structures to improve BNNs. Comprehensive experiments on the image classification and objective detection tasks show that our DIR-Net consistently outperforms the state-of-the-art binarization approaches under mainstream and compact architectures, such as ResNet, VGG, EfficientNet, DARTS, and MobileNet. Additionally, we conduct our DIR-Net on real-world resource-limited devices which achieves 11.1x storage saving and 5.4x speedup

Some Advice about the Water Strategy of China to Keep the Water Balance in 2025

Water resource is crucial for human survival. And fresh-water is the constraint for the development of China. In order to realize the sustainable development, we build three mathematical models for determining an effective, feasible and cost-efficient water strategy to meet the projected water demand of China in 2025. These models are as following: model of the cost of water transfer, model of the water price and the model of desalination plant construction cost. All the models are based on the forecast of the water demand and supply of China in 2025. Based on the result of these models, we propose some advice about the water strategy to meet the water demand of China in 2025, such as: building desalination plants in the coastal provinces which are lack of water, carrying out the inter-basin water transfer projects in the middle of China, setting a reasonable water price based on the market economy and et al

Improving Robust Fairness via Balance Adversarial Training

Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem. Previously proposed Fair Robust Learning (FRL) adaptively reweights different classes to improve fairness. However, the performance of the better-performed classes decreases, leading to a strong performance drop. In this paper, we observed two unfair phenomena during adversarial training: different difficulties in generating adversarial examples from each class (source-class fairness) and disparate target class tendencies when generating adversarial examples (target-class fairness). From the observations, we propose Balance Adversarial Training (BAT) to address the robust fairness problem. Regarding source-class fairness, we adjust the attack strength and difficulties of each class to generate samples near the decision boundary for easier and fairer model learning; considering target-class fairness, by introducing a uniform distribution constraint, we encourage the adversarial example generation process for each class with a fair tendency. Extensive experiments conducted on multiple datasets (CIFAR-10, CIFAR-100, and ImageNette) demonstrate that our method can significantly outperform other baselines in mitigating the robust fairness problem (+5-10\% on the worst class accuracy

MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization

Robust multi-agent reinforcement learning (MARL) necessitates resilience to uncertain or worst-case actions by unknown allies. Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios. Attempts to simplify this complexity often yield overly pessimistic policies, inadequate robustness across scenarios and high computational demands. Unlike these approaches, humans naturally learn adaptive and resilient behaviors without the necessity of preparing for every conceivable worst-case scenario. Motivated by this, we propose MIR2, which trains policy in routine scenarios and minimize Mutual Information as Robust Regularization. Theoretically, we frame robustness as an inference problem and prove that minimizing mutual information between histories and actions implicitly maximizes a lower bound on robustness under certain assumptions. Further analysis reveals that our proposed approach prevents agents from overreacting to others through an information bottleneck and aligns the policy with a robust action prior. Empirically, our MIR2 displays even greater resilience against worst-case adversaries than max-min optimization in StarCraft II, Multi-agent Mujoco and rendezvous. Our superiority is consistent when deployed in challenging real-world robot swarm control scenario. See code and demo videos in Supplementary Materials

Notoginseng root enhances healing in imiquimod-induced psoriasis mice model via anti-inflammatory and antiproliferative properties

Purpose: To evaluate the beneficial effect of Panax notoginseng (PN) gel against imiquimod-induced psoriasis in a mice model.Methods: Psoriasis was induced by topical application of imiquimod cream (5 %) on the shaved skin of mice for 7 days. PN group received PN gel (1 %) twice a day with imiquimod cream (5 %) once a day for one week. The effect of PN gel was estimated by scoring skin thickness, scaling and erythema. Reverse transcription polymerase chain reaction (RT-PCR) was used for the determination of the expressions of inflammatory mediators in skin tissues of mice. Moreover, the severity of inflammation was determined by histopathological and immunohistochemical assessment of skin tissues.Results: The severity of inflammation and the expressions of inflammatory mediators were significantly reduced in PN gel-treated group, relative to the negative control group. Treatment with PN gel attenuated the histopathology of skin tissue in the imiquimod-induced psoriatic mice, and significantly decreased the level of intercellular adhesion molecule (ICAM-1), when compared to the negative control group.Conclusion: These results show that PN gel attenuates psoriasis in imiquimod-induced psoriasis mice model by decreasing skin inflammation. Thus, PN gel may be suitable for the management of psoriasis.Keywords: Psoriasis, Panax notoginseng, Inflammatory mediators, Imiquimod, Intercellular Adhesion Molecule-