Assessing the Impact of the “Two-child Policy” in China: The Effects of the Second Child on the Growth of Teenagers

Purpose: China’s new universal two-child policy has brought a baby boom of the second child in 2016. With the changing family structures, elder children in adolescence may have more and more emotional and even psychological problems such as “two-child syndrome”. This paper aims to explore the “influence factors and mechanism of the second child on the growth of teenagers” and has great practical significance to promote the physical and mental health of teenagers and the harmony of two-child families. Design/methodology/approach: By means of literature analysis, in-depth interview, questionnaire survey and statistical analysis, the elder children aged 12 to 18 in the two-child family were taken as the main body of the survey, and an empirical research based on NLP understanding hierarchy theory was carried out. Major Findings: (1) The most significant influence on the growth of teenagers is the attitude of parents, and then the gender of the first child. (2) Parents’ favouritism towards the second child would lead to decline in the first child’s academic performance. (3) The higher the family income, the less the influence on the first child’s life. (4) When the age difference is large, the first child and the second child are not easy to have conflicts. Practical implications: Parents should try to treat any children equally. Parents should pay attention to the sibling relationship of their two children with small age gap. Parents who have two children with a large age gap should properly guide the elder child to help take care of the younger brother or sister. Originality/value: This study contributes to the growing literature focusing on the influence factors and mechanism of the second child on the growth of teenagers in the context of China’s new universal two-child policy. It adds some early empirical insights on the physical and mental health of teenagers in twochild families

Temporal Knowledge Sharing enable Spiking Neural Network Learning from Past and Future

Spiking neural networks have attracted extensive attention from researchers in many fields due to their brain-like information processing mechanism. The proposal of surrogate gradient enables the spiking neural networks to migrate to more complex tasks, and gradually close the gap with the conventional artificial neural networks. Current spiking neural networks utilize the output of all moments to produce the final prediction, which compromises their temporal characteristics and causes a reduction in performance and efficiency. We propose a temporal knowledge sharing approach (TKS) that enables the interaction of information between different moments, by selecting the output of specific moments to compose teacher signals to guide the training of the network along with the real labels. We have validated TKS on both static datasets CIFAR10, CIFAR100, ImageNet-1k and neuromorphic datasets DVS-CIFAR10, NCALTECH101. Our experimental results indicate that we have achieved the current optimal performance in comparison with other algorithms. Experiments on Fine-grained classification datasets further demonstrate our algorithm's superiority with CUB-200-2011, StanfordDogs, and StanfordCars. TKS algorithm helps the model to have stronger temporal generalization capability, allowing the network to guarantee performance with large time steps in the training phase and with small time steps in the testing phase. This greatly facilitates the deployment of SNNs on edge devices

Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition

The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment. To address the problem, we leverage Dynamic Vision Sensors (DVS) cameras to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains.Comment: Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark

Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks

The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our exploration into a new paradigm for Spiking Neural Networks (SNNs): a Plasticity-Driven Learning Framework (PDLF). This paradigm diverges from traditional neural network models that primarily focus on direct training of synaptic weights, leading to static connections that limit adaptability in dynamic environments. Instead, our approach delves into the heart of synaptic behavior, prioritizing the learning of plasticity rules themselves. This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt. Our PDLF does not merely adapt existing concepts of functional and Presynaptic-Dependent Plasticity but redefines them, aligning closely with the dynamic and adaptive nature of biological learning. This reorientation enhances key cognitive abilities in artificial intelligence systems, such as working memory and multitasking capabilities, and demonstrates superior adaptability in complex, real-world scenarios. Moreover, our framework sheds light on the intricate relationships between various forms of plasticity and cognitive functions, thereby contributing to a deeper understanding of the brain's learning mechanisms. Integrating this groundbreaking plasticity-centric approach in SNNs marks a significant advancement in the fusion of neuroscience and artificial intelligence. It paves the way for developing AI systems that not only learn but also adapt in an ever-changing world, much like the human brain

Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback

In this paper, an adaptive neural bounded control scheme is proposed for an n-link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme

propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans

**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors, and the inefficiency of existing methods. **Purpose:** We conducted a study to introduce a model, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. **Methods:** We developed the PropNet framework, propagating radiologists' knowledge from 2D annotations to the entire 3D space. This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency. **Results:** With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency. The performance is comparable in different scenarios and with various radiologists' annotations (Dice between 0.785 and 0.803). Moreover, the model shows improved prognostic prediction performance (C-index of 0.620 vs. 0.576) on an independent validation set of 42 patients with advanced gastric cancer. **Conclusions:** Our model generates accurate tumor segmentation efficiently and stably, improving prognostic performance and reducing high-throughput image reading workload. This model can accelerate the quantitative analysis of gastric tumors and enhance downstream task performance

The analysis of barriers to bim implementation for industrialized building construction: a China study

The emerging Building Information Modeling (BIM) can better promote the development of building industrialization, with data integration between information-rich building models and business processes. However, the practical implementation of BIM still faces barriers. Existing studies have discussed these barriers extensively, but the research on the barriers to the implementation of BIM amid building industrialization in China is inadequate. In this study, 23 barriers were identified through literature review. A questionnaire survey approach was used to collect data from various parties. Factor analysis methods were used to process and rank barrier factors for BIM applications in the context of industrialized building. Based on the analysis of each factor, analytic hierarchy process was adopted to identify the key barriers to the implementation of BIM for industrialized building construction. The study concluded that the main barriers for BIM implementation for industrialized building were capital-related factors and the lack of support from owners. This study proposes that in addition to governmental policy support for BIM and multi-stakeholder engagement, companies should also organize experts to effectively evaluate the risks of applying BIM. Overall, this study provides suggestions on construction organizational transformations in the roadmap of moving towards digital-driven building industrialization

The comparison between effects of Taichi and conventional exercise on functional mobility and balance in healthy older adults: a systematic literature review and meta-analysis

BackgroundTaichi is beneficial for functional mobility and balance in older adults. However, such benefits of Taichi when comparing to conventional exercise (CE) are not well understood due to large variance in study protocols and observations.MethodsWe reviewed publications in five databases. Eligible studies that examined the effects of Taichi on the outcomes of functional mobility and balance in healthy older adults as compared to CE were included. Subgroup analyses compared the effects of different types of CE (e.g., single and multiple-type exercise) and different intervention designs (e.g., Taichi types) on those outcomes (Registration number: CRD42022331956).ResultsTwelve studies consisting of 2,901 participants were included. Generally, compared to CE, Taichi induced greater improvements in the performance of Timed-Up-and-Go (SMD = −0.18, [−0.33 to −0.03], p = 0.040, I2 = 59.57%), 50-foot walking (MD = −1.84 s, [−2.62 to −1.07], p < 0.001, I2 = 0%), one-leg stance with eyes open (MD = 6.00s, [2.97 to 9.02], p < 0.001, I2 = 83.19%), one-leg stance with eyes closed (MD = 1.65 s, [1.35 to 1.96], p < 0.001, I2 = 36.2%), and functional reach (SMD = 0.7, [0.32 to 1.08], p < 0.001, I2 = 86.79%) tests. Subgroup analyses revealed that Taichi with relatively short duration (<20 weeks), low total time (≤24 h), and/or using Yang-style, can induce significantly greater benefits for functional mobility and balance as compared to CE. Uniquely, Taichi only induced significantly greater improvements in Timed-Up-and-Go compared to single- (SMD = −0.40, [−0.55 to −0.24], p < 0.001, I2 = 6.14%), but not multiple-type exercise. A significant difference between the effects of Taichi was observed on the performance of one-leg stance with eyes open when compared to CE without balance (MD = 3.63 s, [1.02 to 6.24], p = 0.006, I2 = 74.93%) and CE with balance (MD = 13.90s, [10.32 to 17.48], p < 0.001, I2 = 6.1%). No other significant difference was shown between the influences of different CE types on the observations.ConclusionTaichi can induce greater improvement in functional mobility and balance in older adults compared to CE in a more efficient fashion, especially compared to single-type CE. Future studies with more rigorous design are needed to confirm the observations here

Dewatering performance of aerobic granular sludge under centrifugal with different sludge conditioning agent

The aerobic granular sludge(AGS) technology draw scientific researchers attention, and more and more scientific research focuses on it, due to its superior advantages, such as good settling performance, high biological phase, high toxicity resistance and multiple biological effects. With the rapid development of AGS technology, a considerable amount of residual AGS will be produced, and dehydration is the biggest bottleneck of sludge reduction. This study investigated the dewatering process and method of residual AGS cultured by continuous flow experiment. Experiments were conducted using centrifugal dewatering technology with a dosing scheme to analyze the granular sludge dewatering process, and investigate the release process of EPS component in AGS dewatering. Our results implied the specific resistance of AGS has a very low value ((1.82 ± 0.03) × 109 m/kg) and it was not obvious for the conditioning effect of chemical conditioner on AGS dewatering. However, the moisture content can be reduced to 63.5% after dewatering with the presence of inorganic substances. The addition of drinking water treatment plant sludge (Alum sludge) can improve the efficiency of the dewatering of AGS. A possible dewatering process of AGS dewatering was proposed which was divided into two stages: First, a considerable amount of free water in the sludge was quickly removed under the action of gravity without pressure filtration. Second, the bound water release required cooperation between applying centrifugal or pressing force to grind granular cells and separate protein-like substances with the inorganic matter inside the granular sludge. The possible mechanism of AGS dewatering and hypothesis dewatering process are useful to optimize the AGS dewatering process

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

IntroductionThe identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.MethodsTo address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.Results and discussionThe MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking