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The purpose of this study is to evaluate the potential risks of copyright infringement in digital library based on the extension theory.

At first, the analytic hierarchy process (AHP) is used to determine the weights of the existing indicator system for early warning. Second, a model is built to evaluate the potential risks of copyright infringement based on the extension theory in digital library. Finally, a real-world application is presented to show the effectiveness and usefulness of this approach.

The main findings of this paper are as follows: the early warning extension theory model is effective in distinguishing the degree of the potential risks of copyright infringement in digital library; the ranges of the value and the values of the indicators can directly affect the results while using this approach, so the accuracy of these two aspects is a crucial question.

The social impact is that copyright infringement risks of digital library is reduced; the lawsuit rate and economic loss due to copyright infringement are thereby decreased as well.

This paper introduces the evaluation of the potential risks of copyright infringement based on the extension theory in digital library. The results provide support for the decision-makers in handling the potential risks of copyright infringement in digital library.

The purpose of this paper is to improve the conceptual understanding of the process of participation in early warning systems (EWS) through a review of participatory EWS examples in the academic literature. Specifically, this paper asks: who is involved, what responsibilities do participants hold, what activities are they involved in, and what are the associated successes, issues and outcomes?

A total of 30 cases of participation in EWS documented in the academic literature were identified through online searches. Existing concepts in participation (power and responsibility, communication) and people-centred early warning (risk knowledge, monitoring and warning, communication and dissemination and response capability) were used to examine each paper.

Participation was found to take place through a range of activities across all elements of the EWS. Participation also varied in breadth of inclusion, ranging from the general public to selected volunteers. The majority of cases received support and facilitation from other actors, such as government and NGOs, but the extent of power and responsibility held by participants varied greatly within this. Common successes and issues associated with participatory EWS and the potential outcomes are presented, and the opportunities, challenges and gaps in knowledge are discussed.

This paper links participation and EWS literature to form a clearer conceptualisation of participation in EWS in support of future research in the field. It provides unique insights into who participates, their roles and relations with other actors and the outcomes of participation.

To survive and grow in an era of uncertainty, companies should strive not only for a single visionary view, which most likely corresponds with their expectations, but instead they should try to acquire multiple views that describe the whole ”window of opportunities”. The development of external market scenarios to assess current strategies is the usual way of coping with these uncertainties. Today this traditional scenario approach has to be extended in four directions: the use of market scenarios to systematically develop future‐robust strategies, the use of alternative strategy scenarios to address uncertainties within an organization, the use of scenarios as a basis for strategic early warning processes and the combination of performance measurement and strategic early warning in a scenario‐based future scorecard. In conclusion it is shown that scenarios could significantly help to bridge the gap between strategy implementation and early‐warning processes.

Introduction Within the framework of an empirical study the structure and the developmental stage of planning and control systems in 355 companies of different branches and sizes in German industry were investigated. In so doing planning and control, with the help of data processing, were analysed throughout industry as a whole as well as in sectional classes.

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

Small- and medium-sized enterprises (SMEs) often lack adequate accounting systems and may even fail because of accounting inefficiencies. Indeed, accounting can mitigate the course of a crisis and support a troubled SME’s turnaround. Its impact on reorganization success, however, has scarcely been researched so far. Therefore, this paper aims to examine the effects of several accounting parameters, namely, the quality of accounting systems, quality of early warning systems, formal planning, the standard of financial accounting and reorganization planning on the short- and long-term success of court-supervised reorganization.

The impact of accounting on reorganization success is investigated in a sample of all SME bankruptcy cases with ten or more employees (n = 117) in Upper Austria in 2012 including data for short-term survival (in 2016) and long-term survival (in 2019).

This study found evidence that the general quality of accounting systems, the quality of early warning systems and written reorganization plans positively influence the outcomes of the analyzed court-supervised reorganizations of SMEs. In particular, the existence of a reorganization plan significantly increases the short- and long-term reorganization success by ensuring the efficient and effective use of resources in the reorganization process.

This study should increase the awareness of SMEs’ owner managers, consultants, creditors and legislators for the importance of accounting in the context of reorganization. The fact that the effect of accounting on reorganization success is less pronounced in the long-term view indicates the necessity of increasing the strategic focus in SMEs’ accounting instruments.

This study provides new evidence on the impact of specific accounting parameters on the short- and long-term success of the court-supervised reorganization of SMEs. Furthermore, this study points out the high relevance of reorganization plans for SMEs.

The pace of urbanization in the developing world is led by Asia. Over the next 25 years, Asia's urban population will grow by around 70% to more than 2.6 billion people. An additional billion people will have urban habitats (ADB, 2006).

The “Hyogo Framework for Action 2005–2015: Building the Resilience of Nations and communities to disasters” (HFA) was adopted at the UN World Conference on Disaster Reduction (January 2005, Kobe, Japan). The HFA specifies that disaster risk is compounded by increasing vulnerabilities related to various elements including unplanned urbanization. Across the HFA, important elements on urban risk reduction are mentioned as one of crucial areas of work to implement the HFA. In particular incorporating disaster risk reduction into urban planning is specified to reduce the underlying risk factors (Priority 4).

Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.

In the range of 0.5–10.0 s after the P-wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM.

The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τ_c method and P_d method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P-wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P-wave arrival, respectively, which is better than the realization rate norm of multiple stations.

At the latest, 1.5 s after the P-wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

This paper seeks to illustrate designation of a real‐time oil spill monitoring and management system using computer system, GIS models, internet and a variety of other technologies.

Appropriate models in GIS, together with monitoring technologies and internet‐based communication infrastructure, facilitate oil spill early warning, situational analysis, risk analysis and damage analysis in addition to management and disaster response in real‐time. The system architect includes command and control module, designed for managing and coordinating oil spill accidents response. The structure also includes an accident data dissemination scheme, through an internet portal which distributes disaster thematic products and facilitates communication between oil spill disaster players and the administration office.

The functionality of such a system through its components including database, central repository, disaster models, command and control and communication schemes covers all the stages of spill management before, during, and after an accident.

The system acts as a single umbrella of control and administration for efficient and effective oil spill accident management and enhances oil spill accident early warning and alert mechanisms. The system will also enhance decision supports for quick emergency responses and improve real‐time communication and information sharing between responsible agencies.

As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements.

Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures.

The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR.

The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners.