Digital Terrain Models Generation from Airborne LiDAR Point Clouds Using A Multi-scale Terrain Filtering Method

A Digital Terrain Model (DTM) is an important topographic product, required in many applications. Data needed to create a DTM was traditionally obtained via land surveying, however this method can be costly and time consuming depending on the size of the geographic area. Over time, the land surveying was partially replaced by photogrammetry. Today, airborne Light Detection and Ranging (LiDAR) has become another powerful alternative that collect 3D point clouds for digital surface models (DSM) acquisition. LiDAR is especially useful when dealing with heavily vegetated areas using a canopy penetration feature of laser pulse. Nowadays, LiDAR plays an important role in DTM generation. This thesis presents a hierarchical recovery method to generate DTMs from a cloud of 3D points composed of “single returns” and “multiple returns” from laser pulses using the idea of layering. The proposed method will begin by registering the last return points, then layering them. The layering is done by dividing the points into different height layers and assigning layer numbers to each point. The layer numbers are used as a comparison feature in a later identification process. Then a series of rasterized pyramid levels, which consists of the lowest points in each cell, are generated. After layering, outliers are removed; cells in the top level are assumed as terrain points and used as references for identifying cells in the second level. The identification process will identify the cells of the second level into terrain cells and off-terrain cells, and an interpolation will then occur in the cells which identified as off-terrain. The interpolated level will be used as references for the next level and the same process is then repeated for each level that comes after. Once this process has been completed for the bottom level, the proposed method adjusts the results based on the first return feedback, followed by another interpolation. As a result, the final DTM is produced. The developed method is data driven, and does not assume a prior knowledge about the scene complexity. The proposed method was tested with three airborne LiDAR datasets, covering different terrain types and filtering difficulties. Results illustrated that the proposed method can perform well for areas of flat terrain or gentle slope A comparative study was conducted over existing filters and showed that results of the proposed method has similar accuracy in above mentioned area and faster speed than two comparing algorithms

Hepatotoxicity induced by zoledronic acid in an aged woman with primary osteoporosis

Zoledronic acid, a bisphosphonate, has been approved for treatment and prevention of osteoporosis. This case describes a 73-year-old woman with primary osteoporosis who developed transient hepatotoxicity after zoledronic acid (ZOL) treatment. Three days after ZOL infusion, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and gamma-glutamyltransferase (GGT) were increased by 9.9, 8.1, and 3.7 times, respectively, compared with pretreatment values. Liver protective agents were administered. The aminotransferase returned within normal ranges 12 days post-infusion. Currently, the relationship of ZOL and liver damage is not quite clear, which cannot be explained by its pharmacokinetics. The aim of this case report is to increase the clinician’s awareness of the possible adverse effect on the liver, and ZOL should be cautiously administered in patients with liver disease

Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis

BackgroundBipolar disorder and metabolic syndrome are both associated with the expression of immune disorders. The current study aims to find the effective diagnostic candidate genes for bipolar affective disorder with metabolic syndrome.MethodsA validation data set of bipolar disorder and metabolic syndrome was provided by the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found utilizing the Limma package, followed by weighted gene co-expression network analysis (WGCNA). Further analyses were performed to identify the key immune-related center genes through function enrichment analysis, followed by machine learning-based techniques for the construction of protein–protein interaction (PPI) network and identification of the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). The receiver operating characteristic (ROC) curve was plotted to diagnose bipolar affective disorder with metabolic syndrome. To investigate the immune cell imbalance in bipolar disorder, the infiltration of the immune cells was developed.ResultsThere were 2,289 DEGs in bipolar disorder, and 691 module genes in metabolic syndrome were identified. The DEGs of bipolar disorder and metabolic syndrome module genes crossed into 129 genes, so a total of 5 candidate genes were finally selected through machine learning. The ROC curve results-based assessment of the diagnostic value was done. These results suggest that these candidate genes have high diagnostic value.ConclusionPotential candidate genes for bipolar disorder with metabolic syndrome were found in 5 candidate genes (AP1G2, C1orf54, DMAC2L, RABEPK and ZFAND5), all of which have diagnostic significance

s2p: Provenance Research for Stream Processing System

The main purpose of our provenance research for DSP (distributed stream processing) systems is to analyze abnormal results. Provenance for these systems is not nontrivial because of the ephemerality of stream data and instant data processing mode in modern DSP systems. Challenges include but are not limited to an optimization solution for avoiding excessive runtime overhead, reducing provenance-related data storage, and providing it in an easy-to-use fashion. Without any prior knowledge about which kinds of data may finally lead to the abnormal, we have to track all transformations in detail, which potentially causes hard system burden. This paper proposes s2p (Stream Process Provenance), which mainly consists of online provenance and offline provenance, to provide fine- and coarse-grained provenance in different precision. We base our design of s2p on the fact that, for a mature online DSP system, the abnormal results are rare, and the results that require a detailed analysis are even rarer. We also consider state transition in our provenance explanation. We implement s2p on Apache Flink named as s2p-flink and conduct three experiments to evaluate its scalability, efficiency, and overhead from end-to-end cost, throughput, and space overhead. Our evaluation shows that s2p-flink incurs a 13% to 32% cost overhead, 11% to 24% decline in throughput, and few additional space costs in the online provenance phase. Experiments also demonstrates the s2p-flink can scale well. A case study is presented to demonstrate the feasibility of the whole s2p solution

A model-based assurance case construction approach for system control software

Abstract As the massive damage caused by the failures of system control software becomes increasingly prominent, people pay more attention to the construction of assurance case to demonstrate the dependability level of system control software. In this paper, a new assurance case construction approach for system control software is proposed. Based on the metamodel of modular GSN, we give the basic procedure and tree structure deductive algorithm of the approach, and verify our work using Brake Control software used in an aircraft. The results show that the approach can develop assurance case effectively and efficiently

SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing

Adoption of distributed stream processing (DSP) systems such as Apache Flink in real-time big data processing is increasing. However, DSP programs are prone to be buggy, especially when one programmer neglects some DSP features (e.g., source data reordering), which motivates development of approaches for testing and verification. In this paper, we focus on the test data generation problem for DSP programs. Currently, there is a lack of an approach that generates test data for DSP programs with both high path coverage and covering different stream reordering situations. We present a novel solution, SPOT (i.e., Stream Processing Program Test), to achieve these two goals simultaneously. At first, SPOT generates a set of individual test data representing each path of one DSP program through symbolic execution. Then, SPOT composes these independent data into various time series data (a.k.a, stream) in diverse reordering. Finally, we can perform a test by feeding the DSP program with these streams continuously. To automatically support symbolic analysis, we also developed JPF-Flink, a JPF (i.e., Java Pathfinder) extension to coordinate the execution of Flink programs. We present four case studies to illustrate that: (1) SPOT can support symbolic analysis for the commonly used DSP operators; (2) test data generated by SPOT can more efficiently achieve high JDU (i.e., Joint Dataflow and UDF) path coverage than two recent DSP testing approaches; (3) test data generated by SPOT can more easily trigger software failure when comparing with those two DSP testing approaches; and (4) the data randomly generated by those two test techniques are highly skewed in terms of stream reordering, which is measured by the entropy metric. In comparison, it is even for test data from SPOT

A dependability case construction approach based on dependability deviation analysis

As computer-based systems play an important role in our daily life, dependability assessment of these systems is of great significance. Simply combining the result of each dependability attribute could not reflect system dependability faithfully. Therefore, holistic methods to assess dependability are necessary. As a new and promising method which focused on demonstrating the dependability of product-specific system, structured assurance case model receives growing attention. Build a dependability case systematically and effectively is challenging work, particularly for systems which involves multi-contractor who have different responsibilities. In this paper, we proposed a new dependability case construction method to address the ownership problem of system dependability case which involves multiple contractors. Our method extends the existing Dependability Deviation Analysis with contractors analysis and elicitation and integrates the contractors information into the whole dependability argument architecture. We illustrate our contributions by application to a Train Control and Monitor System which preliminarily shows the effectiveness of our method