Research on the Risk of Social Stability of Enterprise Credit Supervision Mechanism Based on Big Data

The study aims to establish a platform-based enterprise credit supervision mechanism, and combined with big data, accurately evaluate the credit assets of enterprises under the influence of social stability risk, and improve the ability of enterprises to deal with risks. Using descriptive statistical methods, the study shows that most local enterprises exist in the form of micro loans, which promotes the development of local economy to a certain extent, but it is a vicious cycle of economic development; The overall prediction accuracy of the single enterprise risk assessment model under the influence of social stability risk is 65%. Compared with the single algorithm, the prediction accuracy of the integrated algorithm model is significantly improved, and the prediction accuracy can reach 83.5%, the standard deviation of data prediction is small, and the stability of the model is high.

Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model

<p>Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.</p>

Sustainability Assessment Model of the Buriganga River Restoration Project in Bangladesh: A System Dynamics and Inclusive Wealth Study

The Bangladesh government initiated the Buriganga River Restoration Project in 2010 to clean the heavily polluted Turag-Buriganga River. This study assessed the dynamic impact of the project on intergenerational well-being and developing a sustainable river system. The project outcomes were modeled for three future scenarios—varying waste control, streamflow, and migration control levels. System dynamics modeling—based on Streeter-Phelps’ water quality model and inclusive wealth (IW) index—was applied to secondary data (including remotely sensed data). The simulation model indicated that the project (with increasing streamflow up to 160 m3/s) will not ensure sustainability because dissolved oxygen (DO) is meaningfully decreasing, biological oxygen demand (BOD) is increasing, and IW is declining over time. However, sustainability can be achieved in scenario 3, an integrated strategy (streamflow: 160 m3/s, waste control: 87.78% and migration control: 6%) that will ensure DO of 8.3 mg/L, BOD of 3.1 mg/L, and IW of 57.5 billion USD in 2041, which is equivalent to 2.22% cumulative gross domestic product by 2041. This study is the first to use combined modeling to assess the dynamic impacts of a river restoration project. The findings can help policymakers to achieve sustainability and determine the optimal strategy for restoring polluted rivers.

How to think about pain with the whole person in mind

Too often, pain is reduced to a simple symptom of illness or injury – a puzzle piece to fit into the differential diagnostic jigsaw. Pain reports that fit the emerging pathoanatomical picture are validated and treated accordingly. But many reports don’t fit this picture, and the widespread stigma associated with persistent pain is most commonly directed toward these individuals, whose symptoms aren’t well explained by known pain mechanisms. A root problem is not seeing the person in pain or the suffering they experience. This presentation aims to help participants develop a more comprehensive perspective on pain that better integrates its complexities within clinical practice. Participants will be introduced to the Multi-modal Assessment model of Pain (MAP; Wideman et al, Clinical Journal of Pain 2019; 35(3): 212). MAP offers a novel framework to understand the fundamentally subjective natures of pain and suffering and how they can be best addressed within clinical practice. MAP aims to help clinicians view pain, first and foremost, as an experience (like sadness), which may or may not correspond to specific pathology or diagnostic criteria (like clinical depression). MAP aims to facilitate a more compassionate approach to pain management by providing a rationale for why all reported pain should be validated, even when poorly understood. Viewing pain in this manner helps highlight the central importance of listening to patients’ narrative reports, trying to understand the meaning and context for their experiences of pain and using this understanding to help alleviate suffering.