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By considering the nervous system as a type of communication system with reliable transmission of signals an attempt is made to use in neurophysiology the basic information theory theses developed by Shannon for technical communication systems. Some results of different information indices calculations (those of capacity, redundancy, degree of reliability and so on) of the pulses incorporated in their functional community (neuron ensembles) are presented. A comparison is made between peripheral and central neurons information indices. The leading role of spike trains redundancy is stressed in the mechanisms of securing a reliable signals transmission in the nervous system. The relation between redundancy and randomization process of neuron spike trains structures is shown.

This investigation is designed to quantify and appraise the efficiency of resource distribution in the provision of public digital cultural services in China. By acknowledging and incorporating the realities of China's social development, the authors offer recommendations for enhancement derived from the study’s data analysis results. The research zeroes in on the dissection and analysis of the integral elements that structure the provision of public digital cultural services, and it concentrates on the associated data computation. The conclusions drawn herein are expected to serve as a significant point of reference for ongoing academic investigations and practical explorations in affiliated domains.

In this research, the authors utilize a hybrid methodology to meticulously evaluate the efficiency of the components that underpin the provision of public digital cultural services (PDCS) in China. The authors embark on deconstructing the various constituents within the PDCS supply framework, conducting in-depth analyses and providing cogent interpretations of each integral element. Subsequently, the authors deploy the well-regarded SBM super-efficiency model to ascertain the operational efficiency of these components. Ultimately, through a comprehensive interpretation of the measured data and the integration of extant societal development conditions, the authors put forth relevant recommendations.

The provision of PDCS in China as of 2021 had been characterized by overall good efficiency, significant regional disparity and a disconnect between inputs and outputs with weak correlations to economic and demographic data.

In this study, the authors provide an exhaustive deconstruction and interpretation of the public digital cultural services supply system, thereby proposing a framework for evaluating the efficiency of supply element allocation. Additionally, the authors have determined a set of distinct measurable indicators that are readily accessible for open collection. Notably, this analytical and evaluative framework designed for element analysis and measurement may also find application in efficiency evaluation research of the supply systems of other related cultural endeavors.

Merger approving focuses on both market power and welfare gains. In general, the approval process does not include a comparative efficiency analysis. This paper aims to introduce this dimension and show its potential.

Based on the analysis of past bank mergers, the authors examine expected and actual efficiency gains. This paper measures the potential (ex ante) and ex post efficiency gains of bank mergers by using data envelopment analysis (DEA).

The authors find some (approved) mergers were promised and yielded efficiency gains while others did not.

DEA does not allow testing statistically the significance of the presumed relationship between variables.

The authors conclude that some mergers that took place would not have been approved had an efficiency analysis been made.

Regulators and/or competition authorities could approve mergers which do not increase efficiency.

To date, efficiency frontier analysis has not been performed for merger approval. It implies that the regulator or competition authority could allow mergers with no clear social gains.

Efficiency of a commercial bank affects both its competitiveness and the role it plays in the process of economic development. Although great efforts have been exerted in developing the various aspects of banking efficiency, there seems to be a lack of research on examining the impact of the bank efficiency from the employee wage perspective. The mechanism of how employee wage affects commercial bank efficiency and the relationship between the two were analyzed in this paper. Based on the growing body of research on efficiency in banking, the aim of this paper is to examine if competitiveness of employee wages at any commercial bank has any impact on the bank efficiency score.

The method used was quantitative analysis, which was based on comparing the evaluated efficiencies of the banks with employee wages published in the bank reports. The empirical data in this paper were based on 16 Chinese listed commercial banks from 2004 to 2012. The per capita wage of commercial banks was selected as the wage indicator, and the efficiency value obtained by the slack-based measure (SBM) model was selected as the efficiency indicator. According to the calculated data, the Tobit regression model was built to analyze the relationship between employee wage and commercial bank efficiency.

The research results show that employee wage is the key variable that influences the efficiency of Chinese commercial banks, and the inverted U-shaped relationship between employee wage and commercial banks efficiency shows up.

The wage structure data of the composition of basic pay and bonus were not available at the time of conducting the research. Per capita wages were used instead to reflect the employee wage levels of Chinese banks.

This study can provide some help for the banking industry by analyzing the wage levels from the perspective of efficiency and also further enriches the theoretical system of the relationship between employee wage and bank efficiency.

The purpose of this paper is to analyze agricultural total factor productivity (TFP) and input redundancies in different regions of China, and to bring out the policy implications for improving efficiency in agricultural production as well as environment protection.

Based on the provincial panel data during 1995-2014, the agricultural productivity of China and its regional disparity are analyzed. First, the agricultural TFP and its decomposition are dynamically evaluated by means of data envelopment analysis-Malmquist productivity index. Second, the agricultural radial production efficiency in year 2014 and the input redundancy changes from 1995 to 2014 are measured based on the BCC-slacks-based measure model.

The results showed that the overall agricultural TFP of China grew 4.3 percent annually during 1995-2014, mainly as a result of technical progress. However, the declines of technical efficiency and scale efficiency slowed down the agricultural TFP growth. The TFP growth in the Western region and Central region far exceeded the Eastern region in last few years. In 2014, most effective decision-making units were in the Western region. The input redundancies in the agricultural production increased substantially after 2006, especially for the pesticide use amount, reservoir capacity and agricultural machinery power.

Combining the dynamic and static analyses, the paper fulfilled the study of China’s agricultural productivity and the input redundancies in recent years, and also presented the regional disparities.

The purpose of this paper is to present a novel algorithm to handle space environment induced errors in the space‐robot software.

The radiations in outer space may induce transient errors in micro‐processors, this phenomena will make software behavior unpredictable, and the existing software fault tolerance methods have been restricted in non‐multi‐threaded operation systems, non‐component‐based frameworks, non‐cacheable micro‐processors, non‐distributed environments, etc. A software model for space‐robot software, based on adaptive redundancy, is developed and a corresponding run‐time error detection algorithm is presented in this paper. Software was monitored and run‐time transient error would be detected and processed.

Experiments indicate that this method introduces about 30‐35 percent time overhead and about 200‐230 percent memory overhead. It also increases the fault detection rate to 84‐92.5 percent. Moreover, the model and algorithm is effective in a realistic space robot environment.

A redundancy model is developed and an error detection algorithm is introduced in this paper. Experiments demonstrate it can provide space‐robot software with good protection against the radiation induced transient errors.

This study aims to the development of the scale of supply chain performance measures (SCPMs), food supply chain resilience (FSCS) and sustainable corporate performance (SCP) in small- and medium-sized enterprises (SMEs) in an emerging market. Based on this purpose, the study examines the relationships between SCPMs and SCP by exploring the mediating role of FSCS in emerging markets.

Based on a comprehensive literature review on the SCPMs, FSCS and SCP, the author evaluates the nexus of these constructs on disruptions during the COVID-19 pandemic emergency in an emerging market. The article follows a quantitative approach. A total of 567 valid responses from managers at senior and middle levels were received and used for data analysis. The Smart PLS version 3.3.2 was employed to analyse Structural Equation Modelling (SEM) to investigate the relationships between constructs and latent variables.

This study provides some theoretical contributions to expand the extant literature on the domain of SCPMs. First, the findings determine that multidimensional measures of flexibility, diversity, agility, inventory efficiency, redundancy and robustness are appropriate for measuring food SC performance in disruptions during the COVID-19 emergency. Besides, this study enriches the existing literature on SC disruption by providing extensive empirical evidence on SCPMs in disruptions during the COVID-19 emergency. Finally, this research provides an integrated empirical model that explores the link between the identified food SCPMs to FSCS and SCP.

The contributions may be of interest to business practitioners, business leaders and academics. In addition, this study provides empirical evidence to demonstrate that food SC performance, as measured by these measures, is strongly related to the firm's food supply chain resilience. This is the novel contribution of this study to the current literature on food SC management. Furthermore, this study provides further empirical evidence demonstrating the partial mediating role of the firm's food supply chain resilience in the nexus between food SC performance and SCP. The unique contribution of this study is an extension of the body of knowledge of SC management literature from a comprehensive approach by providing a proven set of performance measures of SC management to which it can drive SC resilience and SCP for food manufacturing SMEs in an emerging economy that hardly found in the current literature.

Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not.

A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity.

By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms.

The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.

A novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.

The result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”

A novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).