Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

What do we mean, ’tipping cascade’?

Based on suggested interactions of potential tipping elements in the Earth’s climate and in ecological systems, tipping cascades as possible dynamics are increasingly discussed and studied as their activation would impose a considerable risk for human societies and biosphere integrity. However, there are ambiguities in the description of tipping cascades within the literature so far. Here we illustrate how different patterns of multiple tipping dynamics emerge from a very simple coupling of two previously studied idealized tipping elements. In particular, we distinguish between a two phase cascade, a domino cascade and a joint cascade. While a mitigation of an unfolding two phase cascade may be possible and common early warning indicators are sensitive to upcoming critical transitions to a certain degree, the domino cascade may hardly be stopped once initiated and critical slowing down–based indicators fail to indicate tipping of the following element. These different potentials for intervention and anticipation across the distinct patterns of multiple tipping dynamics should be seen as a call to be more precise in future analyses on cascading dynamics arising from tipping element interactions in the Earth system.

HỆ THỐNG CHỈ SỐ CẢNH BÁO SỚM KHỦNG HOẢNG TIỀN TỆ VÀ KHỦNG HOẢNG NGÂN HÀNG TẠI VIỆT NAM

Dựa trên quan điểm thận trọng, nghiên cứu này tích hợp bốn cách tiếp cận Signal, Logit/Probit, BMA (Bayesian Model Averaging) và 2SLS (Two Stage Least Squares) để phát triển hệ thống các chỉ số cảnh báo sớm (Early Warning Indicators – EWI) về khủng hoảng tiền tệ (KHTT) và khủng hoảng ngân hàng (KHNH) tại Việt Nam. Kết quả nghiên cứu cho thấy vai trò quan trọng của các chỉ số kinh tế vĩ mô trong cảnh báo sớm KHTT và KHNH tại Việt Nam, đặc biệt là 8 chỉ số, bao gồm chỉ số giá chứng khoán, tỷ giá thực đa phương, xuất khẩu, M2/dự trữ ngoại hối, tiền gửi ngân hàng, dự trữ ngoại hối, số nhân cung tiền M2 và tác động của khủng hoảng tài chính (KHTC) toàn cầu. Thêm vào đó, nghiên cứu cũng tìm thấy bằng chứng về mối quan hệ nhân quả hai chiều giữa KHTT và KHNH tại Việt Nam, đồng thời cung cấp bằng chứng thực nghiệm về tác động của hiện tượng đô la hóa đến khả năng xảy ra KHTT và tác động mạnh mẽ của KHTC toàn cầu đến khả năng xảy ra KHTT và KHNH tại các nền kinh tế mới nổi nhỏ và mở cửa như Việt Nam.

Early Warning Indicators of Landslides Based on Deep Displacements: Applications on Jinping Landslide and Wendong Landslide, China

Real-time monitoring and early warning have great significance in reducing/avoiding the consequences caused by landslides. The deep displacement-based monitoring method has been proven to be a suitable solution for landslide risk management. However, the early warning indicators based on the deep displacement method need to be fully understood. This paper reports on an investigation into early warning indicators and deformation monitoring of several natural landslides. A series of indicators using the profiles of the accumulative displacement, kinetic energy, and their rates against time for early warning are developed and calibrated by monitoring and analyzing a natural landslide. The early warning indicators are then applied to monitor and identify the different deformation stages of the Jinping County North Landslide and the Wendong Town Landslide.

Checking for ‘Checkpoints’: Using course design to redefine engagement in Early-warning system learner analytics

In order to maintain pace with rising expectations to provide an ‘excellent learning environment’, higher education institutions across the world are turning to learner analytics to help allocate resources efficiently. However, the exponential increase of digital learning technologies has resulted in learner analytics sharing the same practical and ethical concerns as ‘big data’ in the wider context. This study provides an important ‘proof-of-concept’ that learner analytics is better served by data from theory driven course design, and not more data. We explore the potential of learner analytics combined with course design that incorporates regular, automated, low-stakes assignments to provide ‘checkpoints’ of student engagement. We show for a cohort of 424 foundation year students that attainment is best predicted by ‘checkpoint’ submission and not by a host of demographic and behavioural variables that have previously been identified as ‘early-warning indicators’. To conclude, we identify how the practice of integrating learner analytics and course design can help us better align our practice with ethical use of data guidelines for learner analytics and the recommendations from the literature.

Morphological and Molecular Alterations Induced by Lead in Embryos and Larvae of Danio rerio

Lead (Pb) is one of the most toxic and persistent elements and may adversely affect both humans and wildlife. Given the risks posed to humans, lead is listed among priority substances of public health importance worldwide. In fish, available studies deal with high doses, and the potential hazard of Pb at low concentrations is largely unknown. Given its well-demonstrated translational value for human toxicity research, we used zebrafish as a model species. Embryos were exposed to two environmentally relevant concentrations of lead (2.5 and 5 µg/L) from 6 h post-fertilization and analyzed after 48, 96, and 144 h. The morphological abnormality arose after 48 h, and the incidence and intensity were dose and time dependent. Spinal and tail deformities were the most frequently detected alterations. Pb also modulated the expression of genes involved in the toxicological responses (sod and mt), thus demonstrating that zebrafish’s early stages are able to mount an adaptive response. Moreover, ldh and β-catenin were significantly upregulated in all groups, whereas wnt3 expression was increased in the high concentration group. Our results confirm that zebrafish embryos and larvae are valuable early warning indicators of pollution and may play a major role in ecosystems and human health monitoring.

PHM Features for Large Circuit Boards to Be Implemented Into Electric Drivetrain Applications

Canary structures being used as early warning indicators represent an important tool for condition and health monitoring of electronic components and systems. In this paper, printed circuit boards with canary structures based on SMD 2512 ceramic chip resistors with reduced solder pad sizes were studied. Focus of these investigations was set on thermo-mechanical and mechanical stresses caused by passive thermal cycling as well as by vibrational loads. For this purpose, experimental methods such as deformation analysis and accelerated ageing tests as well as finite element based methods were applied. In addition, an outlook on the implementation of these canary structures into dual inverter electronic control boards for electrical powertrain applications will be given.

Overlapping Time Scales Obscure Early Warning Signals for the Second COVID-19 Wave

Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple time scales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of time scale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.