Prevention Effects of Chain Management on Pressure Ulcers of Hospitalized Patients

The study focused on the preventive effects of the chain management model on pressure ulcers in the operating room. Sqoop big data collection module is used to collect patient information from various hospital information systems in a distributed manner. The data were from the clinical data center of the Zhongshan Hospital Xiamen University General Hospital, and 268 patients were selected as the research subjects. A chain management model is constructed, concerning the preventive measures, the management of each link, the perioperative pressure ulcer management, and the reporting of pressure ulcers. Then, the two groups were compared for the SAS and SDS scores before and after nursing, the pressure ulcer sites, pressure ulcer reporting rate, pressure ulcer staging, and nursing satisfaction. The results show that it is not that more collection modules will lead to better cluster performance and that the execution delay is caused by MapReduce requiring the JAVA virtual machine, and after reaching a certain point, the increase in the number of tasks will slow down the process, and as data size increases, DataNote has an expanded capability to analyze data. After nursing treatment, the SAS and SDS scores of the two groups of patients were significantly lower than before treatment ( P < 0.05 ). The pressure ulcers were mainly distributed in the forehead, mandible, cheeks, front chest, and knees in the two groups, and the difference between the two groups was statistically significant ( P < 0.05 ). The total satisfaction of the observation group was 93.28%, and the total satisfaction of the control group was 92.54%. The patients’ satisfaction with the chain management model was higher than that of conventional nursing.

Finding Polluter Tests Using Java PathFinder

Tests that modify (i.e., "pollute") the state shared among tests in a test suite are called \polluter tests". Finding these tests is im- portant because they could result in di erent test outcomes based on the order of the tests in the test suite. Prior work has proposed the PolDet technique for nding polluter tests in runs of JUnit tests on a regular Java Virtual Machine (JVM). Given that Java PathFinder (JPF) provides desirable infrastructure support, such as systematically exploring thread schedules, it is a worthwhile attempt to re-implement techniques such as PolDet in JPF. We present a new implementation of PolDet for nding polluter tests in runs of JUnit tests in JPF. We customize the existing state comparison in JPF to support the so-called \common-root iso- morphism" required by PolDet. We find that our implementation is simple, requiring only -200 lines of code, demonstrating that JPF is a sophisticated infrastructure for rapid exploration of re-search ideas on software testing. We evaluate our implementation on 187 test classes from 13 Java projects and nd 26 polluter tests. Our results show that the runtime overhead of PolDet@JPF com- pared to base JPF is relatively low, on average 1.43x. However, our experiments also show some potential challenges with JPF.

CGPTuner

Properly selecting the configuration of a database management system (DBMS) is essential to increase performance and reduce costs. However, the task is astonishingly tricky due to a large number of tunable configuration parameters and their inter-dependencies. Also, the optimal configuration depends upon the workload to which the DBMS is exposed. To extract the full potential of a DBMS, we must also consider the entire IT stack on which the DBMS is running, comprising layers like the Java virtual machine, the operating system and the physical machine. Each layer offers a multitude of parameters that we should take into account. The available parameters vary as new software versions are released, making it impractical to rely on historical knowledge bases. We present a novel tuning approach for the DBMS configuration auto-tuning that quickly finds a well-performing configuration of an IT stack and adapts it to workload variations, without having to rely on a knowledge base. We evaluate the proposed approach using the Cassandra and MongoDB DBMSs, showing that it adjusts the suggested configuration to the observed workload and is portable across different IT applications. We try to minimise the memory consumption without increasing the response time, showing that the proposed approach reduces the response time and increases the memory requirements only under heavy-load conditions, reducing it again when the load decreases.

Integration of Cassandra and Spark in Computer Aided Drug Design

The primary purpose of this paper is to provide feasibility study of Cassandra and spark in Computer Aided Drug Design (CADD). The Apache Cassandra database is a big data management tool which can be used to store huge amount of data in different file formats. A huge database can be designed with details of all known molecules or compounds that are existing on earth. The information regarding the compounds such as selectivity, solubility, synthetic viability, affinity, adverse reactions, metabolism and environmental toxicity along with the 3 D structure of molecule can be stored in this big database. A data analytics tool “spark” can be efficiently used in mining and managing huge data stored in the database. Integrating big data in CADD helps in identifying the candidate drugs within minutes, not years. It may take eight to fifteen years to develop a new drug traditionally. Spark is written in Scala Programming Language which runs on Java Virtual Machine (JVM) and it supports Scala, Java and Python Programming languages .Cassandra can provide connectors to different programming languages, hence it’s very easy to integrate any other molecular modeling tool with Spark. A python based molecular modeling tool called Pymol can be easily implemented with Spark. CADD helps in identifying new drugs by computational means thus eliminating unnecessary cost incurred in chemical testing of drugs.

Selected tools for Java class and bytecode inspection in the educational environment

Java is not only a modern, powerful, and frequently used programming language, but together with Java Virtual Machine it represents a novel dynamic approach of writing and executing computer programs. The fact that Java programs are executed in a controlled environment has several important implications that define the nature of the language and makes it different from the traditional C-like languages. Knowing the detailed differences between the two types of languages and execution environments is a part of the holistic education of a computer engineer.In this paper, we present some behind-the-scene details about the Java Virtual Machine and we show how these details could be used in the educational process to demonstrate the differences and to emphasise the advantages of the dynamic programming approach when compared to the static one. After presenting some information about class files and about the internal structure and operation of the Java Virtual Machine we demonstrate the usage of public domain programs that could be used in the educational process to put these theoretical concepts into practice.

Scientific scripting in Java with JShellLab and application to deep learning using DeepLearning4j

JShellLab is an easy to use MATLAB-like environment for the Java Virtual Machine (JVM). It implements scientific scripting based on the JShell Application Programming Interface (API) of modern Java. The paper illustrates that JShellLab can significantly facilitate and simplify the development of complex computational demanding scientific software at the JVM. The novelty at the JShellLab is that it completely hides the complexity and the intricate dependencies of optimized scientific software. As an example, the demanded field of deep learning is exploited. Specifically, the implementation of effective practical deep learning-based systems using the JShellLab environment and the Deeplearning4j Java library is considered.