Development Power and Its Power Model: The Analytic Approach for Continuous Motivity of Economic Growth

Based on the Partial Distribution [F. Dai, 2001] and the theory of Development Power [F. Dai, 2004], this paper discusses the power model of relation between development power (DP) and productivity. The power model also supports the hypothesis [F. Dai, 2005] that there are three kinds of energy states in economy, i.e. normal state, strong state and super state, and DP is the continuous motivity to economic growth. By the power model of DP, we could interpret in analytic way that the diffusion of DP and the diversifications of economic development also might be occurred after the super state. Finally, the conclusions in this paper are researched in the empirical way, the results indicate the power model is better than the exponential model of DP in many cases, and we could get the inimitable outcomes in describing the macroeconomic process by the power model of DP.Development Power (DP), Partial Distribution, power model, macroeconomic analysis

Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels

IoU losses are surrogates that directly optimize the Jaccard index. In semantic segmentation, leveraging IoU losses as part of the loss function is shown to perform better with respect to the Jaccard index measure than optimizing pixel-wise losses such as the cross-entropy loss alone. The most notable IoU losses are the soft Jaccard loss and the Lovasz-Softmax loss. However, these losses are incompatible with soft labels which are ubiquitous in machine learning. In this paper, we propose Jaccard metric losses (JMLs), which are identical to the soft Jaccard loss in a standard setting with hard labels, but are compatible with soft labels. With JMLs, we study two of the most popular use cases of soft labels: label smoothing and knowledge distillation. With a variety of architectures, our experiments show significant improvements over the cross-entropy loss on three semantic segmentation datasets (Cityscapes, PASCAL VOC and DeepGlobe Land), and our simple approach outperforms state-of-the-art knowledge distillation methods by a large margin. Code is available at: \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: Submitted to ICML2023. Code is available at https://github.com/zifuwanggg/JDTLosse

Research of the Classification Model Based on Dominance Rough Set Approach for China Emergency Communication

Ensuring smooth communication and recovering damaged communication system quickly and efficiently are the key to the entire emergency response, command, control, and rescue during the whole accident. The classification of emergency communication level is the premise of emergency communication guarantee. So, we use dominance rough set approach (DRSA) to construct the classification model for the judgment of emergency communication in this paper. In this model, we propose a classification index system of emergency communication using the method of expert interview firstly and then use DRSA to complete data sample, reduct attribute, and extract the preference decision rules of the emergency communication classification. Finally, the recognition accuracy of this model is verified; the testing result proves the model proposed in this paper is valid

Heat-stable enterotoxins of enterotoxigenic Escherichia coli and their impact on host immunity

Enterotoxigenic Escherichia coli (ETEC) are an important diarrhea-causing pathogen and are regarded as a global threat for humans and farm animals. ETEC possess several virulence factors to infect its host, including colonization factors and enterotoxins. Production of heat-stable enterotoxins (STs) by most ETEC plays an essential role in triggering diarrhea and ETEC pathogenesis. In this review, we summarize the heat-stable enterotoxins of ETEC strains from different species as well as the molecular mechanisms used by these heat-stable enterotoxins to trigger diarrhea. As recently described, intestinal epithelial cells are important modulators of the intestinal immune system. Thus, we also discuss the impact of the heat-stable enterotoxins on this role of the intestinal epithelium and how these enterotoxins might affect intestinal immune cells. Finally, the latest developments in vaccination strategies to protect against infections with ST secreting ETEC strains are discussed. This review might inform and guide future research on heat-stable enterotoxins to further unravel their molecular pathogenesis, as well as to accelerate vaccine design

Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels

The soft Dice loss (SDL) has taken a pivotal role in many automated segmentation pipelines in the medical imaging community. Over the last years, some reasons behind its superior functioning have been uncovered and further optimizations have been explored. However, there is currently no implementation that supports its direct use in settings with soft labels. Hence, a synergy between the use of SDL and research leveraging the use of soft labels, also in the context of model calibration, is still missing. In this work, we introduce Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a standard setting with hard labels, but (ii) can be used in settings with soft labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm the potential synergy of DMLs with soft labels (e.g. averaging, label smoothing, and knowledge distillation) over hard labels (e.g. majority voting and random selection). As a result, we obtain superior Dice scores and model calibration, which supports the wider adoption of DMLs in practice. Code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: Submitted to MICCAI2023. Code is available at https://github.com/zifuwanggg/JDTLosse

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union

Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, where some objects occupy more pixels than others. This causes traditional evaluation metrics to be biased towards \textit{majority classes} (e.g. overall pixel-wise accuracy) and \textit{large objects} (e.g. mean pixel-wise accuracy and per-dataset mean intersection over union). To address these shortcomings, we propose the use of fine-grained mIoUs along with corresponding worst-case metrics, thereby offering a more holistic evaluation of segmentation techniques. These fine-grained metrics offer less bias towards large objects, richer statistical information, and valuable insights into model and dataset auditing. Furthermore, we undertake an extensive benchmark study, where we train and evaluate 15 modern neural networks with the proposed metrics on 12 diverse natural and aerial segmentation datasets. Our benchmark study highlights the necessity of not basing evaluations on a single metric and confirms that fine-grained mIoUs reduce the bias towards large objects. Moreover, we identify the crucial role played by architecture designs and loss functions, which lead to best practices in optimizing fine-grained metrics. The code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: NeurIPS 202

The role of IGF-1 in exercise to improve obesity-related cognitive dysfunction

Obesity is an important factor that threatens human health. The occurrence of many chronic diseases is related to obesity, and cognitive function decline often occurs with the onset of obesity. With the further prevalence of obesity, it is bound to lead to a wider range of cognitive dysfunction (ORCD). Therefore, it is crucial to suppress ORCD through intervention. In this regard, exercise has been shown to be effective in preventing obesity and improving cognitive function as a non-drug treatment. There is sufficient evidence that exercise has a regulatory effect on a growth factor closely related to cognitive function—insulin-like growth factor 1 (IGF-1). IGF-1 may be an important mediator in improving ORCD through exercise. This article reviews the effects of obesity and IGF-1 on cognitive function and the regulation of exercise on IGF-1. It analyzes the mechanism by which exercise can improve ORCD by regulating IGF-1. Overall, this review provides evidence from relevant animal studies and human studies, showing that exercise plays a role in improving ORCD. It emphasizes the importance of IGF-1, which helps to understand the health effects of exercise and promotes research on the treatment of ORCD

Nutrient recovery technologies for management of blackwater: A review

Nutrient recovery and recycling are of great importance in sustainable development. Blackwater (BW) refers to wastewater from toilets, which contains feces, urine, water, and toilet paper from flush toilets. The highly concentrated nutrients of blackwater could be collected through source separation and treated adequately to recover nutrients efficiently and economically. The review intends to give an overview of the characteristics of BW and different techniques to recover nutrients and other valuable products. A number of these technologies are currently under development or being tested at laboratory or pilot scale. The perspective for blackwater nutrient recovery technologies is very positive due to their great potential. For application of source-oriented sanitation infrastructure and systems, there is still a long way to go for development of commercial technologies and valuable products

Combination of Walnut Peptide and Casein Peptide alleviates anxiety and improves memory in anxiety mices

IntroductionAnxiety disorders continue to prevail as the most prevalent cluster of mental disorders following the COVID-19 pandemic, exhibiting substantial detrimental effects on individuals’ overall well-being and functioning. Even after a search spanning over a decade for novel anxiolytic compounds, none have been approved, resulting in the current anxiolytic medications being effective only for a specific subset of patients. Consequently, researchers are investigating everyday nutrients as potential alternatives to conventional medicines. Our prior study analyzed the antianxiety and memory-enhancing properties of the combination of Walnut Peptide (WP) and Casein Peptide (CP) in zebrafish.Methods and ResultsBased on this work, our current research further validates their effects in mice models exhibiting elevated anxiety levels through a combination of gavage oral administration. Our results demonstrated that at 170 + 300 mg human dose, the WP + CP combination significantly improved performances in relevant behavioral assessments related to anxiety and memory. Furthermore, our analysis revealed that the combination restores neurotransmitter dysfunction observed while monitoring Serotonin, gamma-aminobutyric acid (GABA), dopamine (DA), and acetylcholine (ACh) levels. This supplementation also elevated the expression of brain-derived neurotrophic factor mRNA, indicating protective effects against the neurological stresses of anxiety. Additionally, there were strong correlations among behavioral indicators, BDNF (brain-derived neurotrophic factor), and numerous neurotransmitters.ConclusionHence, our findings propose that the WP + CP combination holds promise as a treatment for anxiety disorder. Besides, supplementary applications are feasible when produced as powdered dietary supplements or added to common foods like powder, yogurt, or milk