Suchergebnisse

Purpose
There has been a lack of meaningful information systems architecture, which comprehensively conceptualise the essential components and functionality of an information system for fire emergency response addressing needs of different job roles. The purpose of this paper is to propose a comprehensive information systems architecture which would best support four of the key firefighter job roles.


Design/methodology/approach
The study has built on the outcomes of two previous preliminary studies on information and human-computer interaction needs of core firefighter job roles. Scenario-based action research was conducted with firefighters in a range of roles, to evaluate human-computer interaction needs while using various technology platforms.


Findings
Several key themes were identified and led us to propose several layers of an integrated architecture, their composition and interactions.


Research limitations/implications
The selected fire scenarios may not represent every type of fire expected in high-risk built environments.


Practical implications
The current paper represents a shared discussion between end users, system architects and designers, to understand and improve essential components. It therefore provides a reference point for the development of information system architecture for fire emergency response.


Originality/value
The proposed information system architecture is novel because it outlines specific architectural elements required to meet the specific situation awareness needs of different firefighters job roles.

The proposed carbon peak and carbon neutralization goals have ushered China into an era of emissions reduction and a climate-oriented economy. With the proposed double carbon goal, China has formulated many environmental protection and green credit policies. This paper aims to assess the impact of corporate environmental performance (CEP) on financing costs, using a panel dataset of companies in China's heavily polluting industries from 2010 to 2019. We employed fixed-effect models, moderating-effect models, and panel quantile regression (PQR) to analyze the impact, underlying mechanisms, and asymmetric features of CEP on financing costs. Our results indicate that CEP has an inhibitory effect on financing costs, with political connections strengthening this effect and GEA weakening it. Moreover, the impact exhibits asymmetry at different levels of financing costs, wherein lower financing costs see a greater weakening effect from CEP. Improved CEP helps to optimize the financing performance of companies and reduce financing costs. Therefore, policy makers and regulatory authorities should work to unblock financing channels for companies, encourage environmental investment, and remain flexible in implementing environmental policies.

Aim: To study the sleep and mental health of chronic insomnia patients in China during coronavirus disease in 2019 (COVID-19) epidemic. Methods: A total of 764 patients with chronic insomnia were included in this study. From 17 January 2020 to 24 January 2020, insomnia, anxiety and physical symptoms were evaluated online, and they were followed up for 4 and 8 weeks. Main outcomes and indicators were assessed using the Pittsburgh Sleep Quality Index (PSQI) and each factor score, the General Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-15 (PHQ-15), respectively. In addition, insomnia, anxiety and physical symptoms were assessed at baseline and at the end of fourth and eighth weeks. Wilcoxon signed rank test was used to compare the changes in patients' scale scores at different time points. Results: Among the 764 participants, there were 755 and 738 evaluators who completed the fourth and eighth weeks, respectively, and the questionnaire completion rates were 98.82% and 96.60%, respectively. Among them, there are 459 (60.0%) aged 41–60 years old, 546 (71.5%) women, 218 (28.5%) men and 313 (41%) college degrees. After 8 weeks of follow-up, the differences in sleep status, anxiety symptoms and physical symptoms were statistically significant. Among the factors of PSQI, there were differences in subjective sleep quality, sleep latency, sleep duration, sleep disturbance (disorder), sleep efficiency and daytime function. At 4 weeks of follow-up, there was a statistically significant difference in the use of hypnotic drugs; at 8 weeks of follow-up, there was no statistically significant difference in the use of hypnotic drugs. Conclusion: Under the influence of the COVID-19, the sleep status and anxiety of patients with chronic insomnia are affected by the epidemic.

Although investment and R&D activities can exert significant effects on energy-related industrial CO2 emissions (EICE), related factors have not been fairly uncovered in the existing index decomposition studies. This paper extends the previous logarithmic mean Divisia index (LMDI) decomposition model by introducing three novel factors (R&D intensity, investment intensity, and R&D efficiency). The extended model not only considers the conventional drivers of EICE, but also reflects the microeconomic effects of investment and R&D behaviors on EICE. Furthermore, taking Shanghai as an example, which is the economic center and leading CO2 emitter in China, we use the extended model to decompose and explain EICE changes. Also, we incorporate renewable energy sources into the proposed model to carry out an alternative decomposition analysis at Shanghais entire industrial level. The results show that among conventional (macroeconomic) factors, expanding output scale is mainly responsible for the increase in EICE, and industrial structure adjustment is the most significant factor in mitigating EICE. Regardless of renewable energy sources, the emission-reduction effect of energy intensity focused on by the Chinese government is less than the expected due to the rebound effect, but the introduction of renewable energy sources intensifies its mitigating effect, partly resulting from the transmission from the abating effect of industrial structure adjustment. The effect of energy structure is the weakest. Although all the three novel factors exert significant effects on EICE, they are more sensitive to policy interventions than conventional factors. R&D intensity presents an obvious mitigating effect, while investment intensity and R&D efficiency display an overall promotion effect with some volatility. The introduction of renewable energy sources intensifies the promotion effect of R&D efficiency as a result of the "green paradox" effect. Finally, we propose that CO2 mitigation efforts should be made by considering both macroeconomic and microeconomic factors in order to achieve a desirable emission-reduction effect.

As the water resource is becoming scarce, conservation of water has a high priority around the globe, study on water management and conservation becomes an important research problem. People are increasingly becoming more individual households, which tend to be less efficient, requiring more resources per capita than larger households. In order to address these challenges, this paper presents the achievements of monitoring domestic water consumption at the appliance level and intervening people's water usage behavior which have been made in ISS-EWATUS (http://www.issewatus.eu), an European Commission funded FP7 project. The water amount consumed by every household appliance is wirelessly recorded with the exact consumption time and stored in a central database. People's water consumption behavior is likely affected by the real-time water consumption awareness, instant practical advices regarding water-saving activities and classification of water consumption behavior for individuals, all of which are provided by a decision support system deployed as a mobile application in a tablet or any other mobile devices. Only the enhanced water consumption awareness is presented in this paper due to the space limitation. The integrated monitoring and decision support system has been deployed and in use in Sosnowiec in Poland and Skiathos in Greece since March 2015. The domestic water consumption monitoring system at appliance level and the local DSS for affecting people's water consumption behavior are innovative and have little seen before according to the knowledge of the authors. ; This work is part of the ISS-EWATUS project (www.issewatus.eu) and has been funded by the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no (619228). Appreciation also goes to our former research associates Dr Xi Chen, Dr Xiaomin Chen, Dr Kim Perren, and Dr Yixing Shan who have worked in Loughborough University on the project at various stages.

AbstractThis article investigates the economic impacts of a multi‐disaster mix comprising extreme weather, such as flooding, pandemic control, and export restrictions, dubbed a "perfect storm." We develop a compound‐hazard impact model that improves on the ARIO model by considering the economic interplay between different types of hazardous events. The model considers simultaneously cross‐regional substitution and production specialization, which can influence the resilience of the economy to multiple shocks. We build scenarios to investigate economic impacts when a flood and a pandemic lockdown collide and how these are affected by the timing, duration, and intensity/strictness of each shock. In addition, we examine how export restrictions during a pandemic impact the economic losses and recovery, especially when there is the specialization of production of key sectors. The results suggest that an immediate, stricter but shorter pandemic control policy would help to reduce the economic costs inflicted by a perfect storm, and regional or global cooperation is needed to address the spillover effects of such compound events, especially in the context of the risks from deglobalization.

Abstract. Flooding is one of the most disruptive natural disasters, causing substantial loss of life and property damage. Coastal cities in Asia face floods almost every year due to monsoon influences. Early notification of flooding events enables governments to implement focused preventive
actions. Specifically, short-term forecasts can buy time for evacuation and emergency rescue, giving flood victims timely relief. This paper proposes a novel multi-strategy-mode waterlogging-prediction (MSMWP) framework for forecasting waterlogging depth based on time series prediction and a machine learning regression method. The framework integrates historical rainfall and waterlogging depth to predict near-future waterlogging in time under future meteorological circumstances. An expanded rainfall model is proposed to consider the positive correlation of future rainfall with waterlogging. By selecting a suitable prediction strategy, adjusting the optimal model parameters, and then comparing the different algorithms, the optimal configuration of prediction is selected. In the actual-value testing, the selected model has high computational efficiency, and the accuracy of predicting the waterlogging depth after 30 min can reach 86.1 %, which is superior to many data-driven prediction models for waterlogging depth. The framework is useful for accurately predicting the depth of a target point promptly. The prompt dissemination of early warning information is crucial to preventing casualties and property damage.