Wang Xi-feng, one of the most outstanding characters in the famous Chinese classical fiction Hong Lou Meng （The Story of the Stone）. The theoretical basis for this paper is Interpersonal Function propounded by M.A.K...Wang Xi-feng, one of the most outstanding characters in the famous Chinese classical fiction Hong Lou Meng （The Story of the Stone）. The theoretical basis for this paper is Interpersonal Function propounded by M.A.K.Halliday. The paper tries to make a tentative study on the translation of Wang Xi-feng＇s speech in Hong Lou Meng by analyzing Hawkes＇version. It has demonstrated that： to reappear a vivid image of Wang Xi-feng, the translated version reflects Wang＇s personality which properly corresponds to the original. The translator is obeyed by ＂What Wang says should conform to her personality＂.展开更多
Wang Xi-feng, one of the most outstanding characters in the famous Chinese classical fiction Hong Lou Meng （The Story of the Stone）. The theoretical basis for this paper is Interpersonal Function propounded by M.A.K...Wang Xi-feng, one of the most outstanding characters in the famous Chinese classical fiction Hong Lou Meng （The Story of the Stone）. The theoretical basis for this paper is Interpersonal Function propounded by M.A.K.Halliday. The paper tries to make a tentative study on the translation of Wang Xi-feng＇s speech in Hung Lou Meng by analyzing Hawkes＇version. It has demonstrated that： to reappear a vivid image of Wang Xi-feng, the translated version reflects Wang＇s personality speech which properly corresponds to the original by coincide with her social status and identity. The translator is obeyed by ＂What Wang says should coincide with her social status and identity＂.展开更多
Recognizing translatability instead of untranslatability is of significance, since a wide-spread recognition of untranslatability may daunt the efforts of translators. The paper approaches the question of untranslatab...Recognizing translatability instead of untranslatability is of significance, since a wide-spread recognition of untranslatability may daunt the efforts of translators. The paper approaches the question of untranslatability thorough categorizing untranslatability into different groups and then examining untranslatability in each group by analyzing some typical examples from David Hawkes' translation of Hong Lou Meng. Through the analysis of how David Hawkes translated the untranslatables, the paper argues that real untranslatability is rare, while translatability rules.展开更多
The culture-loaded word is a symbol of national culture,since national culture with distinctive features is directly or indirectly reflected in its vocabulary.This paper is driven both by qualitative research and by q...The culture-loaded word is a symbol of national culture,since national culture with distinctive features is directly or indirectly reflected in its vocabulary.This paper is driven both by qualitative research and by quantitative method through the comparison and analysis of translation strategies involved in the five types of material culture-loaded-words,namely,apparel,diet,equipment,architecture and medicine,in The Story of Stone by David Hawkes.With the final quantitative statistics of the proportion of each translation method,it aims to scrutinize either the broad spectrum or the specific characteristics of those translation strategies so as to provide a perspective for the study of cultural translations.展开更多
In the study of Terrestrial Gamma-ray Flashes (TGFs) and Sonoluminescence, we observe parallels with larger cosmic events. Specifically, sonoluminescence involves the rapid collapse of bubbles, which closely resembles...In the study of Terrestrial Gamma-ray Flashes (TGFs) and Sonoluminescence, we observe parallels with larger cosmic events. Specifically, sonoluminescence involves the rapid collapse of bubbles, which closely resembles gravitational collapse in space. This observation suggests the potential formation of low-density quantum black holes. These entities, which might be related to dark matter, are thought to experience a kind of transient evaporation similar to Hawking radiation seen in cosmic black holes. Consequently, sonoluminescence could be a valuable tool for investigating phenomena typically linked to cosmic scale events. Furthermore, the role of the Higgs boson is considered in this context, possibly connecting it to both TGFs and sonoluminescence. This research could enhance our understanding of the quantum mechanics of black holes and their relation to dark matter on Earth.展开更多
Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been...Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.展开更多
Using a rigorous mathematical approach, we demonstrate how the Cosmic Microwave Background (CMB) temperature could simply be a form of geometric mean temperature between the minimum time-dependent Hawking Hubble tempe...Using a rigorous mathematical approach, we demonstrate how the Cosmic Microwave Background (CMB) temperature could simply be a form of geometric mean temperature between the minimum time-dependent Hawking Hubble temperature and the maximum Planck temperature of the expanding universe over the course of cosmic time. This mathematical discovery suggests a re-consideration of Rh=ctcosmological models, including black hole cosmological models, even if it possibly could also be consistent with the Λ-CDM model. Most importantly, this paper contributes to the growing literature in the past year asserting a tightly constrained mathematical relationship between the CMB temperature, the Hubble constant, and other global parameters of the Hubble sphere. Our approach suggests a solid theoretical framework for predicting and understanding the CMB temperature rather than solely observing it.1.展开更多
In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate pr...In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.展开更多
Aiming at the problems that the original Harris Hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum,this paper proposes an improved Harris Hawk optimization algorithm(GHHO).F...Aiming at the problems that the original Harris Hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum,this paper proposes an improved Harris Hawk optimization algorithm(GHHO).Firstly,we used a Gaussian chaotic mapping strategy to initialize the positions of individuals in the population,which enriches the initial individual species characteristics.Secondly,by optimizing the energy parameter and introducing the cosine strategy,the algorithm's ability to jump out of the local optimum is enhanced,which improves the performance of the algorithm.Finally,comparison experiments with other intelligent algorithms were conducted on 13 classical test function sets.The results show that GHHO has better performance in all aspects compared to other optimization algorithms.The improved algorithm is more suitable for generalization to real optimization problems.展开更多
文摘Wang Xi-feng, one of the most outstanding characters in the famous Chinese classical fiction Hong Lou Meng （The Story of the Stone）. The theoretical basis for this paper is Interpersonal Function propounded by M.A.K.Halliday. The paper tries to make a tentative study on the translation of Wang Xi-feng＇s speech in Hong Lou Meng by analyzing Hawkes＇version. It has demonstrated that： to reappear a vivid image of Wang Xi-feng, the translated version reflects Wang＇s personality which properly corresponds to the original. The translator is obeyed by ＂What Wang says should conform to her personality＂.
文摘Wang Xi-feng, one of the most outstanding characters in the famous Chinese classical fiction Hong Lou Meng （The Story of the Stone）. The theoretical basis for this paper is Interpersonal Function propounded by M.A.K.Halliday. The paper tries to make a tentative study on the translation of Wang Xi-feng＇s speech in Hung Lou Meng by analyzing Hawkes＇version. It has demonstrated that： to reappear a vivid image of Wang Xi-feng, the translated version reflects Wang＇s personality speech which properly corresponds to the original by coincide with her social status and identity. The translator is obeyed by ＂What Wang says should coincide with her social status and identity＂.
文摘Recognizing translatability instead of untranslatability is of significance, since a wide-spread recognition of untranslatability may daunt the efforts of translators. The paper approaches the question of untranslatability thorough categorizing untranslatability into different groups and then examining untranslatability in each group by analyzing some typical examples from David Hawkes' translation of Hong Lou Meng. Through the analysis of how David Hawkes translated the untranslatables, the paper argues that real untranslatability is rare, while translatability rules.
文摘The culture-loaded word is a symbol of national culture,since national culture with distinctive features is directly or indirectly reflected in its vocabulary.This paper is driven both by qualitative research and by quantitative method through the comparison and analysis of translation strategies involved in the five types of material culture-loaded-words,namely,apparel,diet,equipment,architecture and medicine,in The Story of Stone by David Hawkes.With the final quantitative statistics of the proportion of each translation method,it aims to scrutinize either the broad spectrum or the specific characteristics of those translation strategies so as to provide a perspective for the study of cultural translations.
文摘In the study of Terrestrial Gamma-ray Flashes (TGFs) and Sonoluminescence, we observe parallels with larger cosmic events. Specifically, sonoluminescence involves the rapid collapse of bubbles, which closely resembles gravitational collapse in space. This observation suggests the potential formation of low-density quantum black holes. These entities, which might be related to dark matter, are thought to experience a kind of transient evaporation similar to Hawking radiation seen in cosmic black holes. Consequently, sonoluminescence could be a valuable tool for investigating phenomena typically linked to cosmic scale events. Furthermore, the role of the Higgs boson is considered in this context, possibly connecting it to both TGFs and sonoluminescence. This research could enhance our understanding of the quantum mechanics of black holes and their relation to dark matter on Earth.
文摘Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.
文摘Using a rigorous mathematical approach, we demonstrate how the Cosmic Microwave Background (CMB) temperature could simply be a form of geometric mean temperature between the minimum time-dependent Hawking Hubble temperature and the maximum Planck temperature of the expanding universe over the course of cosmic time. This mathematical discovery suggests a re-consideration of Rh=ctcosmological models, including black hole cosmological models, even if it possibly could also be consistent with the Λ-CDM model. Most importantly, this paper contributes to the growing literature in the past year asserting a tightly constrained mathematical relationship between the CMB temperature, the Hubble constant, and other global parameters of the Hubble sphere. Our approach suggests a solid theoretical framework for predicting and understanding the CMB temperature rather than solely observing it.1.
文摘In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.
文摘Aiming at the problems that the original Harris Hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum,this paper proposes an improved Harris Hawk optimization algorithm(GHHO).Firstly,we used a Gaussian chaotic mapping strategy to initialize the positions of individuals in the population,which enriches the initial individual species characteristics.Secondly,by optimizing the energy parameter and introducing the cosine strategy,the algorithm's ability to jump out of the local optimum is enhanced,which improves the performance of the algorithm.Finally,comparison experiments with other intelligent algorithms were conducted on 13 classical test function sets.The results show that GHHO has better performance in all aspects compared to other optimization algorithms.The improved algorithm is more suitable for generalization to real optimization problems.
