Database Content Research Articles

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

We propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.

SUBSTANTIATION OF INFORMATION COMPONENTS OF THE REGULATORY REFERENCE DATABASE OF THE AUTOMATED INFORMATION SYSTEM OF ARMAMENT AND MLITARY EQUIPMENT TEST SUPPORT

The creation of an information system to support the testing of armament and military (special) equipment is aimed at improving the efficiency of the test organization. This is possible due to the operational information support of all test processes, establishing information communication between departments of the organization, improving the quality of test estimations, reducing time for manual work and, accordingly, increasing the capacity of the test organization, automation of organizational management processes.
 The authors cover the approaches to the design and practical implementation of the information system for supporting the testing of armament and military (special) equipment. The issue of detailed description of the information content of the regulatory-reference database of the information system is considered. Based on the analysis of the processes that take place at different stages of testing specimens of armament and military equipment, the needs for thematic information are determined. The authors propose structural and functional schemes of information interaction of the components of the regulatory-reference database and the main software modules, which are offered for use in the information system to support tests. As a result of aggregation of the developed structural and functional schemes the generalized information structure of regulatory-reference database is offered. Possible ways to fill the database are determined.
 Based on the defined components of the database it is possible to perform further information-logical designing of the information system to support the testing, development of the components structure of the regulatory- reference database and their normalization. It is expected that the proposed information content of the regulatory- reference database will help increase the efficiency of the planning and testing of specimens of armament and military equipment, improve the quality of test results and reduce time expenditures.

RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases

Abstract Text-to-SQL is the problem of converting a user question into an SQL query, when the question and database are given. In this article, we present a neural network approach called RYANSQL (Recursively Yielding Annotation Network for SQL) to solve complex Text-to-SQL tasks for cross-domain databases. Statement Position Code (SPC) is defined to transform a nested SQL query into a set of non-nested SELECT statements; a sketch-based slot filling approach is proposed to synthesize each SELECT statement for its corresponding SPC. Additionally, two input manipulation methods are presented to improve generation performance further. RYANSQL achieved competitive result of 58.2% accuracy on the challenging Spider benchmark. At the time of submission (April 2020), RYANSQL v2, a variant of original RYANSQL, is positioned at 3rd place among all systems and 1st place among the systems not using database content with 60.6% exact matching accuracy. The source code is available at https://github.com/kakaoenterprise/RYANSQL.

The hospital telemedicine TELEMED database: Providing information on evidence-based telemedicine services to hospital managers and healthcare professionals

Background Increased use of telemedicine in the healthcare system is a political goal in Denmark. Although the number of hospital patients using interventions such as the video consultation has increased in recent years only a small proportion of the outpatient and inpatient visits involve telemedicine. The TELEMED database (https://telemedicine.cimt.dk/) has been launched at the Center for Innovative Medical Technology in Denmark to ensure that hospital managers and healthcare professionals have access to information about telemedicine services and their effectiveness. This article describes the development and the content of the TELEMED database. Methods A structured literature search was made in the PubMed Database for randomised controlled trials or observational studies with a control group that investigated the effect of telemedicine interventions for hospital patients. Data were extracted from each article on the clinical effectiveness, patient perceptions, economic effects and implementation challenges. As the database should only provide inspiration to healthcare professionals regarding possibilities for use of telemedicine, the risk of bias in the studies was not assessed. Results The literature search resulted in 2825 hits. Based on full text assessment, 331 articles were included for data extraction and assessment. These articles present telemedicine services used in 22 different medical specialities. Forty-eight percent of the studies found a positive, statistically significant clinical effect, while 47% showed no statistically significant difference. In 48% of the studies, patients’ experiences were examined and of these 68% found positive patient experiences. Fifty-four percent of the articles included information on the economic effects and, of these, 51% found reduction in healthcare utilization. In the majority of studies between two and four types of implementation challenges were found. Conclusions and recommendations: The TELEMED database provides an easily accessible overview of existing evidence-based telemedicine services for use by hospital managers and health professionals, who whish to to implement telemedicine. The database is freely available and expected to be continuously improved and broadened over time.

‘New-Age’ Preferential Trade Agreements and Global Value Chains: An Empirical Analysis

This article analyses the impact of the ‘depth’ of new-age preferential trade agreements (PTAs) signed by nations on value-added trade as well as trade in final and intermediate goods carried out between them. This objective of this article goes beyond the black-boxing of a PTA through a dummy and aims to focus on the ‘depth’ of a PTA as measured by its provisions and content. For this purpose, we construct an intensive panel data set. The data set spans across 6 years (3-year intervals across 2000–2015) and is constructed on a dyadic (country pair) level for 61 countries and 110 PTAs across the world, using the Trade in Valued Added (TiVA) Database from Organisation for Economic Co-operation and Development (OECD) and Content of Deep Trade Agreements database from World Bank. Further, we construct two indices for measuring the ‘depth’ of the PTAs based on their content. Our study uses augmented gravity equation with three-way fixed effects, namely country pair, exporter time and importer time, for estimating the results. Our results indicate that the ‘depth’ of a PTA significantly affects trade in final goods, intermediate goods and value-added trade, and the impact is highest for trade in value added. We also observe that PTA with greater number of provisions has a higher impact on trade in final goods, intermediate goods as well as trade in value added.

Automatization and Self‐Maintenance of the O ‐GlcNAcome catalogue: A Smart Scientific Database

O-GlcNAcylation is a post-translational modification widely found in proteins across phyla. It consists of the addition of β-N-acetylglucosamine to the hydroxy group of serine or threonine residues. O-GlcNAcylation modulates a myriad of biological processes with more than 5000 O-GlcNAcylated proteins reported in human to date. Since the loss of the dbOGA database, O-GlcNAc-specific data were dispersed across literature and non-O-GlcNAc-focus web databases, thus raising the need for a single integrated online resource. To this extent, we recently compiled and published the O-GlcNAc Database (www.oglcnac.mcw.edu), which provides the scientific community with a place to browse all the O-GlcNAcylation data published. For human, this O-GlcNAcome catalogue contains more than 5000 proteins and 7000 O-GlcNAcylation sites, each entry being provided with the relevant subset of references. In addition, we provide entry-specific protein-digest tools, as well as an advanced search mode to match large experimental datasets with the O-GlcNAc Database content. We offer download options for entry-specific data (proteins and literature items), entire datasets and graphical representations in several formats for general (CSV, XLSX, PDF, BIB) and programming-oriented (JSON) use. To ensure data consistency, we match each O-GlcNAc site against UniProtKB protein sequences (including isoforms) prior to integration in the Database. The review and integration of new literature items is accelerated by taking advantage of Natural Language Processing methods using Machine Learning. From our manually curated set of references, we generated a list of frequent expression patterns which allow us to describe each document with binary values, depending on the presence or absence of a given expression pattern. Then, we trained a Neural Network classifier which aims to predict, for each document, whether it contains O-GlcNAcylated protein-identification. Upon prediction on newly published O-GlcNAc articles, detailed reports are generated and include prediction results, document-specific keywords for species, proteins, O-GlcNAcylation sites and methods, as well as sentences containing those keywords. Overall, we developed a user-friendly interface within an integrated system mostly administrated through automated pipelines, thus saving human time and limiting interventions to validation and revision steps. We hope the O-GlcNAc Database will be both a useful resource for the field, and an inspiring framework for scientific developers.

Resourcing, annotating, and analysing synthetic peptides of SARS-CoV-2 for immunopeptidomics and other immunological studies.

SARS‐CoV‐2 has caused a significant ongoing pandemic worldwide. A number of studies have examined the T cell mediated immune responses against SARS‐CoV‐2, identifying potential T cell epitopes derived from the SARS‐CoV‐2 proteome. Such studies will aid in identifying targets for vaccination and immune monitoring. In this study, we applied tandem mass spectrometry and proteomic techniques to a library of ∼40,000 synthetic peptides, in order to generate a large dataset of SARS‐CoV‐2 derived peptide MS/MS spectra. On this basis, we built an online knowledgebase, termed virusMS (https://virusms.erc.monash.edu/), to document, annotate and analyse these synthetic peptides and their spectral information. VirusMS incorporates a user‐friendly interface to facilitate searching, browsing and downloading the database content. Detailed annotations of the peptides, including experimental information, peptide modifications, predicted peptide‐HLA (human leukocyte antigen) binding affinities, and peptide MS/MS spectral data, are provided in virusMS.

Research on Application of Computer Aided Mapping in Real Estate Surveying and Mapping

This article analyzes and studies the application advantages of computer-aided drawing. The research content of this paper includes improving the efficiency of drawing, optimizing manual drawing, more perfect graphical output, more convenient storage and management. The author studied the basic data collection, clarifying the principle of layering of drawing elements, determining the form of drawing, editing and processing of collected data, checking of output shape data, graphic plate making and printing and other technical application points. This paper studies the optimization of database content, the improvement of network information sharing, and the optimization of operating systems. The purpose of this article is to optimize the results of real estate surveying and mapping and improve the effect of computer-aided mapping.

The Polito Surface Wave flat-file Database (PSWD): statistical properties of test results and some inter-method comparisons

The compilation and maintenance of experimental databases are of crucial importance in all research fields, allowing for researchers to develop and test new methodologies. In this work, we present a flat-file database of experimental dispersion curves and shear wave velocity profiles, mainly from active surface wave testing, but including also data from passive surface wave testing and invasive methods. The Polito Surface Wave flat-file Database (PSWD) is a gathering of experimental measurements collected within the past 25 years at different Italian sites. Discussion on the database content is reported in this paper to evaluate some statistical properties of surface wave test results. Comparisons with other methods for shear wave velocity measurements are also considered. The main novelty of this work is the homogeneity of the PSWD in terms of processing and interpretation methods. A common processing strategy and a new inversion approach were applied to all the data in the PSWD to guarantee consistency. The PSWD can be useful for further correlation studies and is made available as a reference benchmark for the validation and verification of novel interpretation procedures by other researchers.

Beyond gene ontology (GO): using biocuration approach to improve the gene nomenclature and functional annotation of rice S-domain kinase subfamily

The S-domain subfamily of receptor-like kinases (SDRLKs) in plants is poorly characterized. Most members of this subfamily are currently assigned gene function based on the S-locus Receptor Kinase from Brassica that acts as the female determinant of self-incompatibility (SI). However, Brassica like SI mechanisms does not exist in most plants. Thus, automated Gene Ontology (GO) pipelines are not sufficient for functional annotation of SDRLK subfamily members and lead to erroneous association with the GO biological process of SI. Here, we show that manual bio-curation can help to correct and improve the gene annotations and association with relevant biological processes. Using publicly available genomic and transcriptome datasets, we conducted a detailed analysis of the expansion of the rice (Oryza sativa) SDRLK subfamily, the structure of individual genes and proteins, and their expression.The 144-member SDRLK family in rice consists of 82 receptor-like kinases (RLKs) (67 full-length, 15 truncated),12 receptor-like proteins, 14 SD kinases, 26 kinase-like and 10 GnK2 domain-containing kinases and RLKs. Except for nine genes, all other SDRLK family members are transcribed in rice, but they vary in their tissue-specific and stress-response expression profiles. Furthermore, 98 genes show differential expression under biotic stress and 98 genes show differential expression under abiotic stress conditions, but share 81 genes in common.Our analysis led to the identification of candidate genes likely to play important roles in plant development, pathogen resistance, and abiotic stress tolerance. We propose a nomenclature for 144 SDRLK gene family members based on gene/protein conserved structural features, gene expression profiles, and literature review. Our biocuration approach, rooted in the principles of findability, accessibility, interoperability and reusability, sets forth an example of how manual annotation of large-gene families can fill in the knowledge gap that exists due to the implementation of automated GO projections, thereby helping to improve the quality and contents of public databases.

Spices addition as strategy for polyphenol increase in baked product

Spices are a rich source of polyphenols and when consumed regularly in the diet contribute to the beneficial health effects of these bioactive compounds. The aim of this study was to elaborate a baked product, honeybreads, without spices, control honeybread (CHB) and with ) the addition of spices, spiced honeybread (SHB, estimate their polyphenol content and evaluate their sensory acceptance. The macronutrient composition was calculated using the Brazilian Food Composition Database and phenolic compounds content calculated based on Phenol-Explorer 3.0 database. For sensory evaluation, a consumer panel was established, and a seven-point structured hedonic scale applied for: aroma, texture, appearance, taste and total acceptance. Each formulation yielded seventy 16g servings with similar calories. The total phenolic compound content, per serving, of SHB was 11,198mg and for CHB 9,483mg, content approximately 18% higher with the addition of spices. Both formulations showed good acceptability based on the acceptability index with over 75% on every parameter. Selection of specific ingredients can positively impact polyphenol content in baked products without compromising sensory characteristics.

Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFrag

Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much—yet not enough—information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput “big data” services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.

Enrichment analyses of diseases and pathways associated with precocious puberty using PrecocityDB

Precocious puberty (PP) is an important endocrine disorder affecting children globally. Several genes, SNPs and comorbidities are reported to be associated with PP; however, this data is scattered across scientific literature and has not been systematically collated and analysed. In this study, we present PrecocityDB as the first manually curated online database on genes and their ontology terms, SNPs, and pathways associated with PP. A tool for visualizing SNP coordinates and allelic variation on each chromosome, for genes associated with PP is also incorporated in PrecocityDB. Pathway enrichment analysis of PP-associated genes revealed that endocrine and cancer-related pathways are highly enriched. Disease enrichment analysis indicated that individuals with PP seem to be highly likely to suffer from reproductive and metabolic disorders such as PCOS, hypogonadism, and insulin resistance. PrecocityDB is a useful resource for identification of comorbid conditions and disease risks due to shared genes in PP. PrecocityDB is freely accessible at http://www.precocity.bicnirrh.res.in. The database source code and content can be downloaded through GitHub (https://github.com/bic-nirrh/precocity).

Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ ЗАСТОСУВАННЯ СУЧАСНИХ РАДІОЛОКАЦІЙНИХ ЗАСОБІВ ДЛЯ ПОТРЕБ ВІЙСЬКОВО-МОРСЬКИХ СИЛ ЗБРОЙНИХ СИЛ УКРАЇНИ

The development of the surface reflection system, based on the principles and standards adopted in NATO member states, envisages modernization of existing and creation of new RTPs of the Ukrainian Navy, equipped with modern weapons and equipment and able to adequately and flexibly respond to threats to the state from the sea. In the structure of maritime information networks, modern shore and ship radars (RADAR / ARPA) provide a significant part of the information content of the relevant databases. For example, the domestic mobile radar station SR-210 («Delta-M»), designed for: observation of the ground, surface and air situation in the area of protection of protected objects; control of ground, surface and airspace in the area of tactical units and control of border, customs and economic zones. The design of the radar provides the possibility of its placement on all classes of mobile and stationary media (including small - off-road cars, armored vehicles, etc.). In the course of research, in order to study the organization of information exchange during the construction of technical subsystems of a promising system for displaying the surface situation, a test software package for obtaining and using navigation («target») data was created. The software package is responsible for displaying and entering information about the positions of ships (own ship, etc.) and available targets on electronic maps. During the research, the OpenCPN software application was chosen as a marine navigation software. It can be used to obtain information about «objects of observation», the location, speed and course of the vessel (ship). Information obtained by radar on the position of ships and their courses can be displayed on an electronic map. In addition, the possibilities of connecting additional equipment to the SR-210 radar and ways to receive ARPA-messages in the NMEA-0183 format from external interfaces were considered, but in the course of research, unfortunately, there was no possibility of practical development of theoretical developments directly on the SR-210 radar. Therefore, a similar radar (RADAR / ARPA) manufactured by FURUNO was used to test certain modes of operation of modern radar facilities for receiving ARPA messages. It should be noted that the radar on the RTP is designed primarily to solve problems of monitoring the surface situation, first of all, to identify and identify targets, determine the parameters of the selected radar targets and their support, as well as provide the necessary data on observed targets to external consumers. Thus, as a result of the analysis and evaluation of the hardware capabilities of modern radars (RADAR/ARPA) and the processes of obtaining relevant ARPA-messages in NMEA-format, it is possible to conclude that they have all the necessary dynamic parameters of the monitored targets. The use of radar as part of the RTP and the creation of an effective system for reflecting the surface situation for the needs of the Navy of the Armed Forces of Ukraine. Keywords: radar means, radio engineering fast, display system, surface situation.

Molecular mapping of a novel lesion mimic gene (lm4) associated with enhanced resistance to stripe rust in bread wheat

BackgroundLesion mimics (LMs) are disease-like symptoms that occur randomly on plant green leaves in the absence of pathogens. A previous study showed that LMs are related to enhanced resistance to a broad spectrum of diverse pathogen races and programmed cell death (PCD). Stripe rust is a globally epidemic fungal disease that can substantially reduce the quality and yield of crops. The development of resistant cultivars is an economical and environmentally friendly way to enhance the adaptability and yield stability of crops instead of the use of fungicide applications.ResultsIn this study, a novel LM gene affording Pst resistance was identified and mapped with molecular markers developed for marker-assisted selection (MAS)-based wheat breeding. In this study, a novel LM gene named lm4, which is closely linked (8.06 cM) to SSR markers Xgwm210 and Xgwm455, was identified by using a Yanzhan 1/Neixiang 188 RIL population. The genetic distance of lm4 was then narrowed such that it was flanked by SSR markers with 0.51 cM and 0.77 cM intervals. Two SSR markers, lm4_01_cib and lm4_02_cib, were developed based on the content in the Chinese Spring genome database and wheat 660 K SNP results; these markers can be used to conduct MAS of LMs in wheat. The results also showed that lm4 significantly improved the resistance of stripe rust in wheat.ConclusionsTherefore, lm4 is associated with stripe rust resistance, which may provide theoretical support for future crop disease-resistance breeding and for understanding the plant apoptosis mechanism.

Создание картографической базы данных и веб-сервиса «Конвективные опасные метеорологические явления на территории Центрального федерального округа»

Hazardous convective weather events (HCWE), such as heavy rainfall, large hail, squalls and tornadoes, are one of the substantial sources of natural emergencies in Russia. The territory of the Central Federal District (CFD) is characterized by the highest population density in Russia. On the one hand, this leads to increased risks associated with HCWE, but on the other hand, it provides the possibilities for collecting the most detailed information on them (including the events missed by the observation network and reported based on damage assessment). In this study, we consider the structure and information content of the GIS database of HCWE for the territory of the CFD. The main advantage of the developed database comparing with existing analogues is its structure, which includes information on both the events themselves and their consequences, and the conditions of their occurrence. This includes, in particular, the characteristics of meso-scale convective systems (convective storms) based on the images from meteorological satellites and diagnostic variables characterizing the atmospheric environments according to the data from ERA-5 and CFS reanalysis systems. Also, the developed database is associated with previously published databases on tornadoes in Northern Eurasia and large-scale windthrow events in European Russia. At present, we compiled the data on more than 2,000 cases of HCWE in the CFD for the period 2001–2020, most of which were reported based on damage assessment. The open-source PostgreSQL DBMS is used to manage and edit the database. Open access to the database on the Internet is implemented through an online web map service available at http://convective-storms.psu.ru/.

Growing up in Hungary - Cohort ’18 Hungarian Birth Cohort Study. Technical report 2. Prenatal wave

This volume presents the results of the Cohort '18 Hungarian Birth Cohort Study as the third output in the research series. The first contained the theoretical, methodological and organizational tasks preceding the prenatal data collection wave. The second volume presented the theoretical background and the conceptualization of the Cohort '18 Hungarian Birth Cohort Study. This third volume describes the methodology of the prenatal data collection wave and the technical background to the surveying and data processing. We present the sampling procedure, sample coverage and reliability of the raw data. This is followed by a summary and description of the documents and questionnaires used in the fieldwork, as well the quality control procedures. We also present the data recording, editing and cleaning process, and review the content of the different databases. Finally, we summarize the most important statistics of the fieldwork and the metadata of the survey and conclude with reviewing the research ethics guidelines. Keywords: Cohort '18 Hungarian Birth Cohort, methodology, prenatal data collection wave, sampling procedure, sample coverage, reliability of the raw data, prenatal questionnaire Suggested citation: Laura Szabó, Julianna Boros, Ildikó Fábián, Gabriella Gresits, Anna Hortobágyi, Balázs Kapitány, Réka Kis, Krisztina Kopcsó, Fruzsina Leitheiser, Adél Rohr, Zsolt Spéder and Zsuzsanna Veroszta (2021): Growing up in Hungary - Cohort '18 Hungarian Birth Cohort Study. Technical report 2. Prenatal wave. Working Papers on Population, Family and Welfare, No. 38 Hungarian Demographic Research Institute, Budapest.

Geological Landslide Disaster Monitoring Based on Wireless Network Technology

With the comprehensive influence of natural evolution and human activities, the damage degree of geological disasters is increasing. How to effectively early warning geological disasters has become a problem of concern. How to effectively provide early warning of geological disasters has become a concern of people. This research mainly discusses the geological landslide disaster monitoring based on wireless network technology. First, establish two important early warning indicators of rainfall and geological landslide displacement. The monitoring system is powered by a rechargeable 12V lithium battery, combined with solar panels, which can be charged when the sun is full to ensure the stable operation of the system. The AT45DB161B chip with 16M bytes storage capacity is selected to store data such as geological landslide displacement and rainfall. Use Microsoft SQL Server 2008 database management system to complete database content query, addition, modification, and deletion operations. The TLP521-2 photocoupler is used to isolate the GPIO interface of STM32 from the external unit to improve the anti-interference ability. The communication between the field data collector and the monitoring center data server adopts the GPRS packet data transmission method based on the TCP/IP protocol. Currently, the PDU in the network is an IP data packet. The realization of the TCP/IP protocol at the field data collector is all completed in the master single-chip microcomputer. Use SIEMENSMC35GSM/GPRS module as data transmission terminal. The monitoring results show that the absolute error of the test data does not exceed 6mm in the horizontal distance, the vertical height difference does not exceed 9mm. The results show that the monitoring of geological landslide based on wireless network technology improves the accuracy of distance estimation and reduces the positioning error, which can provide scientific guidance for the planning, monitoring and early warning of landslide area.

Active Learning for Inference and Regeneration of Applications that Access Databases

We present K onure , a new system that uses active learning to infer models of applications that retrieve data from relational databases. K onure comprises a domain-specific language (each model is a program in this language) and associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates inputs and database contents, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. Because the technique works with only externally observable inputs, outputs, and database contents, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). K onure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.