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The purpose of this study isto synthesize recent empirical research investigating memory of stressful critical incidents (both simulated and occurring in the field) among law enforcement officers.

The study used the approach of systematic state-of-the-art review.

In total, 20 studies of police and military officers show reduced detail and accuracy of high- versus low-stress incidents, especially for peripheral versus target information. Decrements in memory performance were mediated by the extent of physiological stress responses. Delayed recall accuracy was improved among officers that engaged in immediate post-incident rehearsal, including independent debriefing or reviewing body-worn camera footage.

Most studies were not found through systematic database searches, highlighting a need for broader indexing and/or open access publishing to make research more accessible.

By understanding how stress physiology enhances or interferes with memory encoding, consolidation and recall, evidence-based practices surrounding post-incident evidence gathering are recommended.

The current review addresses common public misconceptions of enhanced cognitive performance among police relative to the average citizen.

The current work draws from scientific knowledge about the pervasive influence of stress physiology on memory to inform existing practices surrounding post-incident evidence gathering among police.

This study aims to analyse the scientific relationship between technological innovation and carbon emissions. Taking the Chinese electric power industry as the empirical research object, this study examined the effect of power technological innovation on carbon emissions and proposed policy recommendations for the development of technological innovation in China.

This study first calculated the energy consumption and carbon emission level of the Chinese electric power industry from 2005 to 2018. Secondly, this study built an evaluation index system for technological innovation of electric power with six indicators: average utilisation hours of power generation equipment; power consumption rate of power plant; line loss rate; standard coal consumption for power generation; standard coal consumption for power supply; and number of patent applications granted for generation, conversion or distribution of electric power in China. Finally, from a practical point of view, the relationship between technological innovation and carbon emissions of the Chinese electric power industry from 2005 to 2018 is evaluated and analysed.

Power technology innovation has been found to have a long-term and relatively large effect on carbon emissions, and carbon emissions have a short-term and insignificant impact on power technology innovation.

This study puts forward relevant suggestions for developing technological innovation and technology transfer, which is essential to establishing a low-carbon or zero-carbon power system in China.

This study provides empirical evidence for clarifying the relationship between technological innovation and carbon emissions in the power industry and further develops research theories on technological innovation and carbon emissions.

Relevant authorities will adopt measures to promote technological innovation and development in the power sector to reduce carbon emissions.

This study built an evaluation index system with six indicators for technological innovation of electric power. The evaluation method was used to measure the technological innovation level of the Chinese electric power industry. The causal link between technological innovation and carbon emissions in China was analysed.

This study examines the impact of China’s mixed-ownership reform on the innovation of non-state-owned acquirers, with a particular focus on the impact on firms with high financing constraints, low-quality accounting information or less tangible assets.

We use a proprietary dataset of firms listed on the Shanghai and Shenzhen Stock Exchanges to investigate the impact of mixed ownership reform on non-state-owned enterprise (non-SOE) innovation. We employ regression analysis to examine the association between mixed ownership reform and firm innovation.

The study finds that non-state-owned firms can improve innovation by acquiring equity in state-owned enterprises (SOEs) under the reform. Eased financing constraints, lowered financing costs, better access to tax incentives or government subsidies, lowered agency costs, better accounting information quality and more credit loans are underlying the impact. Additionally, cross-ownership connections amongst non-SOE executives and government intervention strengthen the impact, whilst regional marketisation weakens it.

This study adds to the literature on the association between mixed ownership reform and firm innovation by focussing on the conditions under which this impact is stronger. It also sheds light on the policy implications for SOE reforms in emerging economies.

The purpose of this study is to reveal the magnitude of empty container movements (ECM) arising from cargo seasonality by means of long-term datasets of Turkish terminals. Trade imbalance is one of the well-known major reasons of ECM. Cargo seasonality apart from some other operational drivers and market effect, i.e. commercial decisions of the ship operators, is the major operational driver in Turkish terminals effecting ECM. Furthermore, this study highlights the significance of market effect, leading to take measures for more effective empty container operations in terms of decision makers leading the ship operators.

Time series analysis of full container datasets was performed through X-13ARIMA-SEATS methodology, implementing seasonal adjustment.

The results indicate that 17 of 112 time series in hand, based on a terminal/hinterland, container type and “in and out” foreign trade, exhibit cargo seasonality. Roughly, the amount of ECM originating from cargo seasonality in Turkish terminals represents 10 per cent of total ECM except trade imbalance in those terminals where seasonality is present. This reveals that ECM arising from market effect should not be underestimated.

Reefer container traffic could not be sorted from the datasets.

This paper focuses on one of the major reasons of ECM, cargo seasonality. It brings a novel point of view and interpretations which were not suggested previously about ECM, motivating to overcome inefficiency in container operations.

By drawing on the research paradigm of collective action that occurs in physical space, the present study aims to explore the antecedent predictors of network social mobilization – feeling of injustice – and discuss the emotional mechanism of this prediction: mediating effect of anger and resentment.

Micro-blog postings about network social mobilization were collected to develop the dictionary of codes of fairness, anger and resentment. Then, according to the dictionary, postings on Sina Weibo were coded and analyzed.

The feeling of injustice predicted network social mobilization directly. The predictive value was 27% and 33%, respectively during two analyses. The feeling of injustice also predicted social mobilization indirectly via anger and resentment. In other words, anger and resentment account for the active mechanism in which the feeling of injustice predicts network social mobilization. Mediating effect value was 29.63% and 33.33% respectively.

This study is our first exploration to use python language to collect data from human natural language pointing on micro-blog, a large number of comments of netizen about certain topic were crawled, but a small portion of the comments could be coded into analyzable data, which results in a doubt of the reliability of the study. Therefore, we should put the established model under further testing.

In the cyberspace, this study confirms the mechanism of network social mobilization, expands and enriches the research on social mobilization and deepens the understanding of social mobilization.

This study provides an empirical evidence to understand the network social mobilization, and it gives us the clue to control the process of network social mobilization.

This study uses the Python language to write Web crawlers to obtain microblog data and analyze the microblog content for word segmentation and matching thesaurus. It has certain innovation.