A P2P Networking Simulation Framework For Blockchain Studies

Recently, blockchain becomes a disruptive technology of building distributed applications (DApps). Many researchers and institutions have devoted their resources to the development of more effective blockchain technologies and innovative applications. However, with the limitation of computing power and financial resources, it is hard for researchers to deploy and test their blockchain innovations in a large-scape physical network. Hence, in this dissertation, we proposed a peer-to-peer (P2P) networking simulation framework, which allows to deploy and test (simulate) a large-scale blockchain system with thousands of nodes in one single computer. We systematically reviewed existing research and techniques of blockchain simulator and evaluated their advantages and disadvantages. To achieve generality and flexibility, our simulation framework lays the foundation for simulating blockchain network with different scales and protocols. We verified our simulation framework by deploying the most famous three blockchain systems (Bitcoin, Ethereum and IOTA) in our simulation framework. We demonstrated the effectiveness of our simulation framework with the following three case studies: (a) Improve the performance of blockchain by changing key parameters or deploying new directed acyclic graph (DAG) structure protocol; (b) Test and analyze the attack response of Tangle-based blockchain (IOTA) (c) Establish and deploy a new smart grid bidding system for demand side in our simulation framework. This dissertation also points out a series of open issues for future research

崇拜科學文明之一瞥

近世文明底演進，是以人類為中心的。這也不過是人在過去的時期，已經親自受了許多盲目的權威底轄制；到了現在人就不能自禁的要求自由底伸張了。我們知道轄制和自由的相偶，自然會起衝突；所以人間呼喊……解放，解放……的聲浪，不絕於耳。從這種普遍要求獨立底現象，我們就看見兒女不服從父母，妻子打倒丈夫，媳婦控制婆婆，勞工反抗僱主，市民謀叛政府等等顛倒乾坤的事實；不能說是不多了！若我們想救濟這紊亂無緒底狀況，只有將宇宙一切事物之佈置，都依着人類中心觀念底一條路上走去。因此便可認定人之智識是創造社會和歷史底工具。社會為適應人類需要而創造底器具。歷史一方面為人類知識活動底結果；他方面為人類建築將來世界的根基。進一步說，假使我們期望去發見人是一個什麼東西，同時又想知道他的本領能做出些什麼驚天動地的事情，以及怎樣去滿足他的慾望的話；我們不妨引俄托教授（Prof. M. C. Otto）解釋「人是什麼？」底一段文字來研究一番。俄托說：「人，就是那從現今奮鬥掙扎中所演出種種複雜和矛盾底事實而產生的。他的過去時代已經獲着人生長所成熟底果子。他的將來是必需依着他的種族底繼續性而規定了。假若他能綿延下去，那他就可以尋去此方法來改善他的本身。他表現着垂頭喪氣的樣兒，是因為他有時否認他自己所發出的底言論；然而同時他又贊成他自己曾經否認了的意見。他真似乎是反復無常的。若從不能決定的嚴重方面推察，他每時代和每時刻都要估定他自己本身。他雖想要逃脫那明白底公式；然而他很恐懼那不可思議的東西把他捉住。他拒絕那計劃他前程底圖表。沒有甚麼理性和美術底工作，能夠把他降入深淵或昇於雲端；或將他清楚的描寫和推察出來。環境的機遇與他沒甚助益。兇暴的壓迫，也沒有法使他挫志喪膽。反之，就是那適意的境況，也不能引誘他苟且偷安。他的終局，我們難以判斷；或者他是歸於空虛，也未可知。不過這地球似乎已經允許他有能力去成就他的志願。正如華提明（Whiteman）講的『茫茫希望的瀑布，滔滔不絕。』他將來或者成為愛末尚（Emerson）的『黃金不可能』（Golden Lmpossibility）。或者成為佈浪林（Browning）的『尚未成就。』（Not yet formed.）又或者成為哈地（Hardy）的『宇宙底不可描寫的焦點。』（Indescrible Focus of the Universe.）這些事情怎樣變遷和結局，都是難以預料的。」換句話說，人是有這樣底精神和氣概，更是他具有獨特驚奇底天性；所以他就能勇往突進而建設今日的科學文明。其機械底花樣翻新，均已畢露，因此，人就自己驚奇他功業底偉大。一般人莫不同聲的說「科學之神」足可揭破世界底迷夢，而超度眾生往彼安全底福地呢

Research on Personalized Learning Resource Recommendation Based on Knowledge Graph Technology

In the face of the dilemma of learners\u27 learning loss and information overload in information resources, a personalized learning resource recommendation algorithm is proposed by conducting in-depth and extensive research on the knowledge graph. This algorithm relies on the similarity or correlation between learners\u27 characteristics and course knowledge (learning resources) for recommendation. It analyzes learners\u27 characteristics in depth from four aspects: data collection and processing, model construction, resource and path recommendation, and model application, and establishes a multi layered dynamic feature model for learners; Analyze the core elements of the curriculum knowledge graph, decompose the curriculum knowledge into nanoscale knowledge granularity, and construct a curriculum knowledge graph model. The experimental results indicate that this algorithm improves learners\u27 learning efficiency and promotes their personalized development

Research on Accurate Recommendation of Learning Resources based on Graph Neural Networks and Convolutional Algorithms

In response to the challenges of learning confusion and information overload in online learning, a personalized learning resource recommendation algorithm based on graph neural networks and convolution is proposed to address the cold start and data scarcity issues of existing traditional recommendation algorithms. Analyze the characteristics of the Knowledge graph of learners and curriculum resources in depth, use the graph Auto encoder to extract the auxiliary information and features in the Knowledge graph and establish the corresponding feature matrix, and use Convolutional neural network for classification and prediction. The experimental results show that this algorithm improves the performance of recommendation systems, improves learners\u27 learning efficiency, and promotes personalized development

The Practice and Innovation of Energizing the Competitiveness of Brand of County by the IP of Culture and Tourism at Zigui

The integration of culture and tourism makes the interaction between culture and tourism deeper and closer. After years of vigorous development, the tourism of county is no longer like before building infrastructure in the entire scenic area, and the economy of county no longer relies on hardware construction and a large investment. And now a new focus is needed to promote the economy and brand competitiveness of the county. Combining the IP (intellectual property) construction method in the Internet era with regional brands with local cultural characteristics, an innovative form of IP for county cultural and tourism brands at present is created, the Zigui County of Yichang City is the practical example of the innovative form. Combine with the unique culture of Qu Yuan, the Dragon Boat Festival, and navel orange specialty of Zigui, the IPs of brand of the county that are called “one da three xiao”, which are Qudafu, Chengxiaozi, Zongxiaogui, and Zhouxiaolong, were created. The IPs are deeply loved by tourists, and quickly stand out in the competition of tourism spread in the surrounding counties and cities. By energizing the competitiveness of brand of the county through IP, the new appearance of county brand of the Zigui, which effectively attracts traffic and drives the economic promotion of Zigui County, is displayed with affinity, sustainability and influence

PointAS: an attention based sampling neural network for visual perception

Harnessing the remarkable ability of the human brain to recognize and process complex data is a significant challenge for researchers, particularly in the domain of point cloud classification—a technology that aims to replicate the neural structure of the brain for spatial recognition. The initial 3D point cloud data often suffers from noise, sparsity, and disorder, making accurate classification a formidable task, especially when extracting local information features. Therefore, in this study, we propose a novel attention-based end-to-end point cloud downsampling classification method, termed as PointAS, which is an experimental algorithm designed to be adaptable to various downstream tasks. PointAS consists of two primary modules: the adaptive sampling module and the attention module. Specifically, the attention module aggregates global features with the input point cloud data, while the adaptive module extracts local features. In the point cloud classification task, our method surpasses existing downsampling methods by a significant margin, allowing for more precise extraction of edge data points to capture overall contour features accurately. The classification accuracy of PointAS consistently exceeds 80% across various sampling ratios, with a remarkable accuracy of 75.37% even at ultra-high sampling ratios. Moreover, our method exhibits robustness in experiments, maintaining classification accuracies of 72.50% or higher under different noise disturbances. Both qualitative and quantitative experiments affirm the efficacy of our approach in the sampling classification task, providing researchers with a more accurate method to identify and classify neurons, synapses, and other structures, thereby promoting a deeper understanding of the nervous system

Mechanism of dissolution and oxidation of stibnite mediated by the coupling of iron and typical antimony oxidizing bacteria

Antimony oxidizing bacteria (SbOB) and iron oxides are the main driving factors to the weathering dissolution and oxidation of stibnite (Sb2S3) waste ore. The characteristics of the dissolution and oxidation process of stibnite in the absence of strain AO-1 and iron oxides, Pseudomonas sp. AO-1-mediated (AO-1-mediated), Fe (Fe, Fe2(SO4)3, and FeS2) -mediated, and coupled-mediated groups (Fe+AO-1, Fe2(SO4)3+AO-1, FeS2+AO-1) under various pH values were examined through sequential batch experiments. The results showed that all the AO-1-mediated, Fe-mediated and coupled-mediated can promote the dissolution and oxidation of stibnite, and the promotion effect increased with the rise of pH. The order of contribution to the dissolution of stibnite under the coupling mediation is as follows: coupling effect (42.4-78.2%) > chemical effect (19.4-56.6%) > biological effect (0.9-2.4%). In addition, the dissolution and oxidation mechanisms of stibnite were further investigated and analyzed in combination with scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). This study has important implications for elucidating the source control and geochemical behavior of antimony pollution in antimony mining areas

Gemini surfactant-modified activated carbon for remediation of hexavalent chromium from water

Gemini surfactants, with double hydrophilic and hydrophobic groups, offer potentially orders of magnitude greater surface activity compared to similar single unit molecules. A cationic Gemini surfactant (Propyl didodecyldimethylammonium Bromide, PDDDAB) and a conventional cationic surfactant (Dodecyltrimethylammonium Bromide, DTAB) were used to pre-treat and generate activated carbon. The removal efficiency of the surfactant-modified activated carbon through adsorption of chromium(VI) was investigated under controlled laboratory conditions. Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM) were used to investigate the surface changes of surfactant-modified activated carbon. The effect of important parameters such as adsorbent dosage, pH, ionic strength and contact time were also investigated. The chromium(VI) was adsorbed more significantly on the Gemini surfactant-modified activated carbon than on the conventional surfactant-modified activated carbon. The correlation coefficients show the data best fit the Freundlich model, which confirms the monolayer adsorption of chromium(VI) onto Gemini surfactant-modified activated carbon. From this assessment, the surfactant-modified (especially Gemini surfactant-modified) activated carbon in this study showed promise for practical applications to treat water pollution