Educational research on mathematics differential equation to simulate the model of children's mental health prevention and control system

In order to solve the practicality of the infant mental health tracking system, the system is simulated based on mathematical differential equations. The SSH framework technology of the mode-view-controller (MVC) design model analyses and designs the model of the prevention and control system of children's mental health. In this design pattern, the Struts framework is used to realise the separation of the view layer, the business layer and the control layer, and the Hibernate framework is used to realise the data persistence layer, thereby obtaining a system solution with strong scalability, low coupling and easy maintenance.

NVLSM: A Persistent Memory Key-Value Store Using Log-Structured Merge Tree with Accumulative Compaction

Computer systems utilizing byte-addressable Non-Volatile Memory ( NVM ) as memory/storage can provide low-latency data persistence. The widely used key-value stores using Log-Structured Merge Tree ( LSM-Tree ) are still beneficial for NVM systems in aspects of the space and write efficiency. However, the significant write amplification introduced by the leveled compaction of LSM-Tree degrades the write performance of the key-value store and shortens the lifetime of the NVM devices. The existing studies propose new compaction methods to reduce write amplification. Unfortunately, they result in a relatively large read amplification. In this article, we propose NVLSM, a key-value store for NVM systems using LSM-Tree with new accumulative compaction. By fully utilizing the byte-addressability of NVM, accumulative compaction uses pointers to accumulate data into multiple floors in a logically sorted run to reduce the number of compactions required. We have also proposed a cascading searching scheme for reads among the multiple floors to reduce read amplification. Therefore, NVLSM reduces write amplification with small increases in read amplification. We compare NVLSM with key-value stores using LSM-Tree with two other compaction methods: leveled compaction and fragmented compaction. Our evaluations show that NVLSM reduces write amplification by up to 67% compared with LSM-Tree using leveled compaction without significantly increasing the read amplification. In write-intensive workloads, NVLSM reduces the average latency by 15.73%–41.2% compared to other key-value stores.

SWARM: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data

The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. Existing systems are using static spatial partitioning to distribute the workload. In contrast, the real-time streamed spatial data follows non-uniform spatial distributions that are continuously changing over time. Distributed spatial streaming systems need to react to the changes in the distribution of spatial data and queries. This article introduces SWARM, a lightweight adaptivity protocol that continuously monitors the data and query workloads across the distributed processes of the spatial data streaming system and redistributes and rebalances the workloads as soon as performance bottlenecks get detected. SWARM is able to handle multiple query-execution and data-persistence models. A distributed streaming system can directly use SWARM to adaptively rebalance the system’s workload among its machines with minimal changes to the original code of the underlying spatial application. Extensive experimental evaluation using real and synthetic datasets illustrate that, on average, SWARM achieves 2 improvement in throughput over a static grid partitioning that is determined based on observing a limited history of the data and query workloads. Moreover, SWARM reduces execution latency on average 4 compared with the other technique.

LSVP

The ubiquity of mobile phones allows video content to be watched on the go. However, users' current on-the-go video learning experience on phones is encumbered by issues of toggling and managing attention between the video and surroundings, as informed by our initial qualitative study. To alleviate this, we explore how combining the emergent smart glasses (Optical Head-Mounted Display or OHMD) platform with a redesigned video presentation style can better distribute users' attention between learning and walking tasks. We evaluated three presentation techniques: highlighting, sequentiality, and data persistence to find that combining sequentiality and data persistence is highly effective, yielding a 56% higher immediate recall score compared to a static video presentation. We also compared the OHMD against smartphones to delineate the advantages of either platform for on-the-go video learning in the context of everyday mobility tasks. We found that OHMDs improved users' 7-day delayed recall scores by 17% while still allowing 5.6% faster walking speed, especially during complex mobility tasks. Based on the findings, we introduce Layered Serial Visual Presentation (LSVP) style, which incorporates sequentiality, strict data persistence, and transparent background, among other properties, for future OHMD-based on-the-go video learning.

The rhythms of shame in digital sexual assault: Rhythmic resistance and the repeated assault

The non-consensual distribution of intimate images is a highly mediated kind of sexual violence. This article shows how data persistence and shareability constitute victim experiences of digital sexual assault as ‘repeated assaults’; as recurring instances of exposure and shaming facilitated by mediated rhythms and circulation. I also show how the participants of this study, young women victimised by digital assault, managed to create stability and resist the rhythms of their assault by modifying their social media presence and politicising their experiences.

dCache: Inter-disciplinary storage system

The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters and long term data persistence on a tertiary storage. Though it was originally developed for the HEP experiments, today it is used by various scientific communities, including astrophysics, biomed, life science, which have their specific requirements. In this paper we describe some of the new requirements as well as demonstrate how dCache developers are addressing them.