Special Issue:Questions&Data for Better Science and Innovation Call for submissions

Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?

Effect of distribution of fines on evolution of cooperation in spatial public goods game

In the realm of public goods game,punishment,as a potent tool,stands out for fostering cooperation.While it effectively addresses the first-order free-rider problem,the associated costs can be substantial.Punishers incur expenses in imposing sanctions,while defectors face fines.Unfortunately,these monetary elements seemingly vanish into thin air,representing a loss to the system itself.However,by virtue of the redistribution of fines to cooperators and punishers,not only can we mitigate this loss,but the rewards for these cooperative individuals can be enhanced.Based upon this premise,this paper introduces a fine distribution mechanism to the traditional pool punishment model.Under identical parameter settings,by conducting a comparative experiment with the conventional punishment model,the paper aims to investigate the impact of fine distribution on the evolution of cooperation in spatial public goods game.The experimental results clearly demonstrate that,in instances where the punishment cost is prohibitively high,the cooperative strategies of the traditional pool punishment model may completely collapse.However,the model enriched with fine distribution manages to sustain a considerable number of cooperative strategies,thus highlighting its effectiveness in promoting and preserving cooperation,even in the face of substantial punishment cost.

Discreet investors exert greater influence on cooperation in the public goods game

This paper studies the evolutionary process of cooperative behavior in a public goods game model with heterogeneous investment strategies in square lattices.In the proposed model,players are divided into defectors,cooperators and discreet investors.Among these,defectors do not participate in investing,discreet investors make heterogeneous investments based on the investment behavior and cooperation value of their neighbors,and cooperators invest equally in each neighbor.In real life,heterogeneous investment is often accompanied by time or economic costs.The discreet investors in this paper pay a certain price to obtain their neighbors'investment behavior and cooperation value,which quantifies the time and economic costs of the heterogeneous investment process.The results of Monte Carlo simulation experiments in this study show that discreet investors can effectively resist the invasion of the defectors,form a stable cooperative group and expand the cooperative advantage in evolution.However,when discreet investors pay too high a price,they lose their strategic advantage.The results in this paper help us understand the role of heterogeneous investment in promoting and maintaining human social cooperation.

MKEAH:Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

PAL-BERT:An Improved Question Answering Model

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.

Operational requirements analysis method based on question answering of WEKG

The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

曹伟云教授治疗Good’s综合征合并支气管扩张验案1例

Good’s综合征是一种免疫缺陷性疾病,属于少见病,关于本病的报道并不多,尤其是采用中药治疗的报道少之又少。Good’s综合征患者因免疫力欠佳,易合并感染,尤以合并支气管扩张伴感染偏多。目前,西医治疗多为对症治疗,急性期予抗生素控制感染,但患者病情易反复。本文主要从曹伟云教授对Good’s综合征合并支气管扩张病的中医病因病机和辨证论治等方面展开论述,认为Good’s综合征合并支气管扩张或因初感外邪、侵犯肺系,或因痰热日久、消耗阴液,或因久病体虚、伤及阴阳引起,在治疗上或予以清热散邪,或予以养阴清热、化痰排脓,或予以补阳养阴,同时根据伴随症进行药物加减,通过整体观念辨证论治,扶正与祛邪动态调整,尤以扶正为要,增强患者的免疫力,改善患者体质,迅速缓解患者临床症状,减少病情发作次数和患者住院次数。

The Violent Redemption Theme in A Good Man is Hard to Find

Flannery O’Connor is one of the representative figures of American Southern writers.Being recognized as the most outstanding writer of the American South after Faulkner,she has great influence in the literary world.Her works are always shrouded in a strange and grotesque atmosphere and full of death as well as religious metaphors.The protagonists are a series of American Southern freaks struggling with the crisis of spiritual belief.The protagonists of her novel A Good Man is Hard to Find are a hypocritical believer in the world of sinners and a lost man suffering in the midst of real sin,thus O’Connor uses the highest form of violence-death to bring ultimate redemption to them.In people’s conception,violence and redemption are often opposite to each other,but O’Connor uses bloody and violent plots to explore the theme of religious salvation,integrating and unifying the two contrary concepts to form her unique and profound violent redemption writing.

Convenience on Three Wheels China-made motor tricycles are transforming the way goods and people are transported in Uganda

In most cities,towns and villages in Uganda,Chinamade motor tricycles have become a game-changer for commuters and entrepreneurs alike.

Changes in the Supply of Public Intermediate Goods,Fixed Wage,and the Relative Price in the Harris-Todaro Model

Since the Harris-Todaro model was proposed in 1970,it has played a crucial role in analyzing various environmental and trade issues in developing countries.This paper analyzes the effects of the amount of public intermediate goods provided by the government,the increase in the fixed wage rate in the urban sector,and the changes in the relative international prices of agricultural and manufacturing goods on labor employment,unemployment,and the economic welfare in the context of a small open economy.It also proposes relevant policies to reduce the unemployment rate while improving national welfare.

“Good”Boussinesq方程的多辛算法

考虑非线性“Good”Boussinesq方程的多辛形式 ,对于多辛形式 ,提出了一个新的等价于中心Preissman积分的 15点多辛格式· 数值试验结果表明 :多辛格式具有良好的长时间数值行为·

基于“Good”测试套件的GUI回归测试优化

回归测试在软件测试过程中是非常重要的,同时也是非常费时费力的。为了适应软件测试的需求,提高回归测试的效率,降低测试成本,针对目前GUI回归测试的困难,依据Atif M.Memon等人提出的新的回归测试方法,根据一个"Good"的测试套件对测试成本和"fault-detection effectiveness"的影响,突出一个"Good"的测试套件的特点,给出一个新的回归测试方法。该方法根据测试套件的特点,有针对性地选择合适的测试用例来构建回归测试套件。这样不仅有针对性,同时也优化了测试套件,提高了测试效率。

“钱学森之问”:以“Good Idea”重塑学术文化范型

梳理“钱学森之问”的时代背景和复杂意蕴,指出其诘问的核心就是呼唤“Good Idea”即卓越学术新思想、新观念之诞生。真正的“Good Idea”,应该是人类精神和物质文明发展史上具有里程碑意义的集中象征,也是作为“新思想孵化高地”的大学学术文化软实力的本质外显。凡是大学学术文化的建设,必须紧紧地围绕着催生和创造“Good Idea”这根精神主轴而予以深度的渐次展开,即要建设弘扬真理、学术本位、原创至尊的大学学术文化之信仰,营造人格自立、个性自由与思想自主的和谐宽松的学术文化环境,建设科学公平、多元开放、珍视创新的学术文化之生产制度(包括学术评价制度)。

Good综合征合并肺毛霉菌病1例

患者，女，72岁。因咳嗽、咯痰伴发热5d于2009年7月2日收入院。既往史：2005年因广泛黏膜念珠菌病发现免疫缺陷、胸腺瘤，并发菌群失调、全身多系统真菌感染，经补充丙种球蛋白、抗真菌治疗在我科治愈后出院。

以顽固性腹泻为首诊的Good综合征一例

Good综合征（ Good syndrome, GS）是一种罕见的合并胸腺瘤的成人原发性免疫缺陷病，典型特征为低丙种球蛋白血症、免疫缺陷合并胸腺瘤，该病老年人高发，无性别差异，占胸腺瘤患者的10％，其发病机制尚不明确。主要临床表现为自身免疫性疾病合并反复机会性感染，特别是含荚膜细菌的感染，肺部为最常受累部位。郑州大学第一附属医院收治以顽固性腹泻为首诊的典型GS一例，现报道如下。

基于Question Point的合作虚拟咨询服务

介绍了Question Point合作虚拟咨询服务产生的背景、功能、系统工作流程以及我国开展合作虚拟参考咨询的情况。

小学英语绘本教学中提问设计的研究——基于Good Thinker’s Tool Kit框架

基于“有效的问题设计可以促进学生自主思考”这一理论假设，该课题组教师在瑞士一所小学进行英文绘本教学时开展了实证研究。通过对比实验组与对照组的教学效果及问卷调查，证明以GoodThinker’s ToolKit为指导的问题设计框架可以有效促进学生的心智发展，培养他们自主思考的能力。

Good综合征2例

Good综合征（Good’s syndrome,GS）是一种胸腺瘤相关免疫缺陷综合征,表现为胸腺瘤合并外周血B细胞、T细胞缺陷及低丙种球蛋白血症,易反复继发细菌、真菌或病毒的感染.笔者在此报道2例GS患者并文献复习.

应用Good Point!培养大学生的英语在线学习能力

Good Point!是香港理工大学开发的一个全英在线交流平台,其教学目标是培养学习者的英语在线学习能力,因此将其应用于大学英语教学。通过对比分析前期和后期的在线交流记录、写作记录以及学生反馈探讨该平台的教学效果。结果发现:该平台能有效提高受试的英语在线交流能力、写作能力和英语思辨能力。可见该平台有利于培养大学生的英语在线探究能力,是一个值得推广的教学平台。