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By reviewing the related literature on the Jiaoshi Pinren Zhi (JPZ) system, this paper seeks to indicate that the implementation of JPZ not only has led to domestic brain drain and the imbalance of teachers' qualities between different areas and schools, but also has violated children's equal rights to education guaranteed by the Constitution and educational laws in PR China.

By analysing national policies and laws and some cases, the paper points out that the reforms on teachers' employment causes the imbalance of teachers' qualities between different areas and schools and it is against the principle of children's equal rights to education guaranteed by the Constitution and some educational laws in China.

Since the late 1980s, many reform initiatives have been launched in the context of the transition from planned economy to market economy in China.

The reforms on the teachers' employment system from “Tongyi Fenpei” (TF, a system of unified placement for all graduates) and the life‐time employment system to JPZ (a free contract employment system) is one of the important reform initiatives.

This paper aims to introduce the efficiency improvement in the operating room (OR) of a local hospital using the integration of simulation and optimization.

Based on the simulation model, a Tabu search (TS) algorithm is developed as an optimizer for the meta-heuristic optimization method to find the optimum configuration of resources for the OR operation.

The computational efficiency is improved for the optimum search. Results show that 21 percent more patients can be processed compared to the existing operation. The average time stay of patients in the OR is reduced by 17 percent.

Limited resources considered in the model may limit the capacity of the proposed method, more resources including nurses, beds in post-operative units, and beds in inpatient wards will be included in the decision variables.

Long waiting lists in the OR lead to the low performance of healthcare systems. It is crucial to identify inefficiency and to improve the OR operation efficiently.

The TS-based heuristic optimizing method developed in this research shows the promise in time saving of the optimal solution search for the OR efficiency improvement.

Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods.

In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty.

The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods.

The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

Based on the growing interest devoted to knowledge management (KM) in inter-organizational contexts, the purpose of this paper is to systematize existing literature and understand how it developed over time, thus tracing its roots and evolution to unveil gaps and suggest new promising areas for future research.

This study used bibliographic techniques to analyze a sample of 85 studies along three main periods (1998-2010, 2011-2014 and 2015-2019). In particular, this study focused on co-occurrences of keywords to identify the most dominant themes, as well as connections among these themes.

Overall, the review shows the main outlets that have published papers on the topic of KM in inter-organizational contexts, as well as the theoretical background this research builds on. The temporal analysis exhibits the core topics that have persisted and grown consistently over time as the links between KM, innovation and networks. In addition, the review highlights new emerging themes, such as the human and social side of KM, and new interesting contexts of study (e.g. coopetition and open/user innovation), which opens exciting avenues for new research opportunities.

This study illustrates the conceptual structure of the field in three distinct periods and contributes to a more nuanced understanding of the key topics and their interrelatedness within the area of KM in inter-organizational contexts. Both researchers and practitioners can profit from the study because it reveals consolidated topics while identifying areas that still need to be investigated to foster KM in inter-organizational settings.

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).

This paper aims to check the presence of such relationship in the field. Certain values are at stake for the success of economic behavior. Since the genesis of modern capitalism, a set of beliefs proper of Calvinism (mainly Predestination but also Beruf, inner-worldly asceticism, role of Sects […] ) was said by Max Weber to cause an anxiety about salvation and generate a propensity to economic success as a sign of election. The author argues on the contrary that the Calvinist belief in the Perpetual Assurance of Salvation might cause a sense of self-efficacy able to favor economic success. To observe this in action today, it is crucial to consider the evolution that the Protestant ethic went through migrating first in North America and, finally, through the Protestant revival of China. Wenzhou is called “Jerusalem of China” for its large Protestant community that is also strongly involved in business. Some scholar already pointed out the presence among those entrepreneurs of this Protestant ethic (Yi Xiang, Boss-Christian […]).

The data presented in this comparative qualitative study pertain to ethnographic observations, job-shadowing and interviews done among Chinese Christian and non-Christian entrepreneurs from Wenzhou living in Milan, Italy.

The results show, with some adjustments, the presence of a Chinese version of the Protestant ethic overlapping with several values proper to the Chinese context (Confucianism, lineage, social network). The extension of the study to other cases must be done with caution considering the non-causal justificatory role of the belief.

Successful entrepreneurship involves specific social, cultural and even religious aspects that move beyond mere business strategies.

This paper aims to prepare an associative thickener base on two polyacrylate-based copolymers, which can be used for digital printing of nylon carpet with enhanced performance.

An associative thickener was prepared by compounding two polyacrylate-based copolymers, cationic starch and polyacrylic acid; and mediated by polyethylene glycol and polyacrylamide crosslinker. The formulation of the associative thickener was optimized by using the orthogonal array testing strategy. The stability of the associative thickener was investigated by measuring effects of temperature, electrolytes, storage time and auxiliaries on viscosity. The associative thickener was compared with a commercial thickener by evaluating their performance in digital printing of nylon carpet.

The associative thickener provided same color strength and fastness in the printing of nylon carpet as the commercial one, but was more easily washed off for a better hand feeling of the printed carpet.

The prepared associative thickener can be applied for digital printing of nylon carpet.

The associative thickener can be facilely prepared from commercially available chemicals and suitable for digital printing of nylon carpet.

This paper aims to guide the implementation of noise reduction measures in hoistway and reduce the aerodynamic noise generated by elevator operation, this paper aims to propose an aerodynamic noise analysis method that can solve the flow field in hoistway.

A turbulence-acoustic model solving the flow field in a hoistway and a numerical wind hoistway model of the ultra-high-speed elevator were established by using large eddy simulation (LES) and Curle acoustic theory.

The characteristics of pulsating flow field and aerodynamic noise around ultra-high-speed elevator are analyzed. The asymmetric characteristics of the flow field could be observed using the turbulent kinetic energy and the instantaneous vortexes in the wind hoistway model. Vortex shedding, air flow separation and recombination around the car were the key factors for aerodynamic noise generation. The sound pressure level was approximately linear to the logarithm of car speed. The increase of car deflection angle in a certain range would reduce the peak frequency of wake noise and increase the sound pressure level (SPL) value.

This paper provides important guidance for researches studying the aerodynamic noise in the hoistway and the technical personnel that look for the reduction measures, which greatly improves the shortcomings in the numerical simulation of the aerodynamic noise of the hoistway.

This paper aims to investigate whether the establishment of commodity futures can effectively hedge systemic risk in the spot network, given the context of financialization in the commodity futures market.

Utilizing industry association data from the Chinese commodity market, the authors identify systemically important commodities based on their importance in the production process using multiple graph analysis methods. Then the authors analyze the effect of listing futures on the systemic risk in the spot market with the staggered difference-in-differences (DID) method.

The findings suggest that futures contracts help reduce systemic risks in the underlying spot network. Systemic risk for a commodity will decrease by approximately 5.7% with the introduction of each corresponding futures contract, since the hedging function of futures reduces the timing behavior of firms in the spot market. Establishing futures contracts for upstream commodities lowers systemic risks for downstream commodities. Energy commodities, such as crude oil and coal, have higher systemic importance, with the energy sector dominating systemic importance, while some chemical commodities also have considerable systemic importance. Meanwhile, the shortest transmission path for risk propagation is composed of the energy industry, chemical industry, agriculture/metal industry and final products.

The paper provides the following policy insights: (1) The role of futures contracts is still positive, and future contracts should be established upstream and at more systemically important nodes in the spot production chain. (2) More attention should be paid to the chemical industry chain, as some chemical commodities are systemically important but do not have corresponding futures contracts. (3) The risk source of the commodity spot market network is the energy industry, and therefore, energy-related commodities should continue to be closely monitored.

New venture status is the most prominent feature of entrepreneurial startups, but its performance implications have remained under-studied. This study aims to bridge this knowledge void and offer precise guidelines for startup managers in boosting performance.

The study develops and tests a multi-perspective model on the linkage between new venture status and firm performance by integrating I/O economics, resource-based view and dynamic capability perspective. The arguments from the first two perspectives point to an adverse effect of new venture status, which is contingent, respectively, on business differentiation and resource endowments. The third perspective grounds a positive relationship between new venture status and performance, which is more pronounced for firms with weaker dynamic capabilities.

Quantitative evidence from a sample of new and established firms in China shows that the direct effect of new venture status is negative but insignificant. Neither business differentiation nor dynamic capabilities moderate the relationship. Low resource endowments, however, reinforce the negative influence of new venture status. New venture status, thus, shapes firm performance through resource scarcity from resource-based view rather than competitive vulnerability from I/O economics or strategic flexibility from dynamic capability perspective.

Newness and new venture performance have both been extensively examined in literature. But the relationship between them has remained largely omitted. The multi-perspective model and the findings in this study help clarify the confusion as to whether newness is good or bad in the context of an emerging market and reveals the subtle mechanism the effect of newness unfolds.