Institutional mapping and causal analysis of avalanche vulnerable areas based on multi-source data

Avalanche disaster is a major natural disaster that seriously threatens the national infrastructure and personnel's life safety. For a long time, the research of avalanche disaster prediction in the world is insufficient, there are only some basic models and basic conditions of occurrence, and there is no long series and wide range of avalanche disaster prediction products. Based on 7 different bands and different types of multi-source remote sensing data,this study combined with existing avalanche occurrence models, field investigation and statistical data to analyze the causes of avalanche. The U-net convolutional neural network and threshold analysis were used to extract the distribution of long time series avalanch-prone areas in two study areas, Heiluogou in Sichuan Province and along the Zangpo River in Palong, Tibet Autonomous Region. In addition, the relationship between earthquake magnitude and spatial distribution and avalanche occurrence is also analyzed in this study. This study will also continue to build a prior knowledge base of avalanche occurrence conditions, improve the prediction accuracy of the two methods, and produce products in long time series interannual avalanch-prone areas in southwest China, including Sichuan Province, Yunnan Province, and Tibet Autonomous Region. The resulting products will provide high-precision avalanche prediction and safety assurance for engineering construction and mountaineering activities in Southwest China.Comment: 19 pages, 13 figure

Hybrid of memory andprediction strategies for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment; otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme

BACKGROUND: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. RESULTS: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. CONCLUSION: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks

A dynamic multi-objective evolutionary algorithm based on decision variable classification

The file attached to this record is the author's final peer reviewed version.In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms

A fast pruned‐extreme learning machine for classification problem

Agency for Science, Technology and Research (A*STAR) Science and Engineering Research Counci

Visualisation of the digital twin data in manufacturing by using augmented reality

With the wave of Industry 4.0, Digital Twin is attracting more and more attention world-wide. The term might have been coined some time ago, today the concept is increasingly being used in the field of smart manufacturing. Digital Twin provides advantages in different fields of manufacturing, such as production and design, remote diagnostics and service. Digital Twin relies on the continuously accumulated data and real-time presentation of the collected data to simultaneously update and modify with its physical counterpart. However, presenting a huge amount of collected data and information in a Digital Twin in an intuitive manner remains a challenge. Currently, augmented reality (AR) has been widely implemented in the manufacturing environment, such as product design, data management, assembly instructions, and equipment maintenance. By integrating graphics, audios and real-world objects, AR allows the users to visualise and interact with Digital Twin data at a new level. It gives the opportunity to provide intuitive and continual visualisation of the Digital Twin data. In this paper, an AR application that uses Microsoft HoloLens to visualise the Digital Twin data of a CNC milling machine in a real manufacturing environment is presented. The developed application allows the operator to monitor and control the machine tool at the same time, but also enables to interact and manage the Digital Twin data simultaneously, which provides an intuitive and consistent human machine interface to improve the efficiency during the machining process. The proposed application paves the way for further development of intelligent control process through AR devices in the future

Effectiveness of early rhythm control in improving clinical outcomes in patients with atrial fibrillation:a systematic review and meta-analysis

BackgroundCurrent guidelines recommend rhythm control for improving symptoms and quality of life in symptomatic patients with atrial fibrillation (AF). However, the long-term prognostic outcomes of rhythm control compared with rate control are still inconclusive. In this meta-analysis, we aimed to assess the effects of early rhythm control compared with rate control on clinical outcomes in newly diagnosed AF patients.MethodsWe systematically searched the PubMed and Embase databases up to August 2022 for randomized and observational studies reporting the associations of early rhythm control (defined as within 12 months of AF diagnosis) with effectiveness outcomes. The primary outcome was a composite of death, stroke, admission to hospital for heart failure (HF), or acute coronary syndrome (ACS). Hazard ratios (HRs) and 95% confidence intervals (CIs) from each study were pooled using a random-effects model, complemented with an inverse variance heterogeneity or quality effects model.ResultsA total of 8 studies involving 447,202 AF patients were included, and 23.5% of participants underwent an early rhythm-control therapy. In the pooled analysis using the random-effects model, compared with rate control, the early rhythm-control strategy was significantly associated with reductions in the primary composite outcome (HR = 0.88, 95% CI: 0.86-0.89) and secondary outcomes, including stroke or systemic embolism (HR = 0.78, 95% CI: 0.71-0.85), ischemic stroke (HR = 0.81, 95% CI: 0.69-0.94), cardiovascular death (HR = 0.83, 95% CI: 0.70-0.99), HF hospitalization (HR = 0.90, 95% CI: 0.88-0.92), and ACS (HR = 0.86, 95% CI: 0.76-0.98). Reanalyses using the inverse variance heterogeneity or quality effects model yielded similar results.ConclusionsOur current meta-analysis suggested that early initiation of rhythm control treatment was associated with improved adverse effectiveness outcomes in patients who had been diagnosed with AF within 1 year.RegistrationThe study protocol was registered to PROSPERO (CRD42021295405)