Time Reversal Aided Bidirectional OFDM Underwater Cooperative Communication Algorithm with the Same Frequency Transmission

In underwater acoustic channel, signal transmission may experience significant latency and attenuation that would degrade the performance of underwater communication. The cooperative communication technique can solve it but the spectrum efficiency is lower than traditional underwater communication. So we proposed a time reversal aided bidirectional OFDM underwater cooperative communication algorithm. The algorithm allows all underwater sensor nodes to share the same uplink and downlink frequency simultaneously to improve the spectrum efficiency. Since the same frequency transmission would produce larger intersymbol interference, we adopted the time reversal method to degrade the multipath interference at first; then we utilized the self-information cancelation module to remove the self-signal of OFDM block because it is known for sensor nodes. In the simulation part, we compare our proposed algorithm with the existing underwater cooperative transmission algorithms in respect of bit error ratio, transmission rate, and computation. The results show that our proposed algorithm has double spectrum efficiency under the same bit error ratio and has the higher transmission rate than the other underwater communication methods

Study on transformation of cowpea trypsin inhibitor gene into cauliflower (Brassica oleracea L. var. botrytis)

Cowpea Trypsin Inhibitor (CpTI) gene was transferred into cauliflower by agrobacterium-mediated transformation method, and 14 transgenic cauliflower plants were obtained. Cotyledons and hypocotyls were used as explants. The putative transformants were assayed by PCR and Southern blotting analysis. The results indicated that CpTI gene was transferred into cauliflower successfully. The result of preliminary insect-resistant assay showed that the transgenic plants were more resistant to Pieris rapae than non-transgenic plants. Key Words: African Journal of Biotechnology Vol.4(1) 2005: 45-4

Height Information Aided 3D Real-Time Large-Scale Underground User Positioning

Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large‐scale underground mining environment. To solve this problem, this study proposes large‐scale underground 3D real‐time positioning method with seam height assistance. This method uses the ultrawide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise3D positioning. The simulation results show that the proposed real‐time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large‐scale underground users

La educación en la China comunista (1949-1976)

Amartya Kumar Sen, premio en Ciencias Económicas en memoria de Alfred Nobel (1998), afirmó que la educación de la era de Mao Zedong sentó las bases para el crecimiento económico de China. A lo largo de las tres décadas siguientes a la fundación de la República Popular China se aprecia un cambio de ruta y una transformación de la situación política. Las políticas de educación en la era de Mao supusieron, por un lado, el apoyo a talentos para el desarrollo y la industrialización del país, y por otro, garantizaron la educación universal. Tras la reforma y la apertura del sistema educativo, sin embargo, quedó constatada la necesidad de cambios drásticos con los que subsanar defectos. Modificaciones que las generaciones futuras deberán revisar y reforzar constantemente. El objetivo de este proyecto es describir y conocer la educación en China bajo las directrices de Mao y su evolución en un panorama de complejidad política

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings