Intelligent retrieval system for conditions of contract documents in construction

The outcome of this research is an Intelligent Retrieval System for Conditions of Contract Documents. The objective of the research is to improve the method of retrieving data from a computer version of a construction Conditions of Contract document. SmartDoc, a prototype computer system has been developed for this purpose. The system provides recommendations to aid the user in the process of retrieving clauses from the construction Conditions of Contract document. The prototype system integrates two computer technologies: hypermedia and expert systems. Hypermedia is utilized to provide a dynamic way for retrieving data from the document. Expert systems technology is utilized to build a set of rules that activate the recommendations to aid the user during the process of retrieval of clauses. The rules are based on experts knowledge. The prototype system helps the user retrieve related clauses that are not explicitly cross-referenced but, according to expert experience, are relevant to the topic that the user is interested in.

Top-K Query Processing in Edge-Labeled Graph Data

Edge-labeled graphs have proliferated rapidly over the last decade due to the increased popularity of social networks and the Semantic Web. In social networks, relationships between people are represented by edges and each edge is labeled with a semantic annotation. Hence, a huge single graph can express many different relationships between entities. The Semantic Web represents each single fragment of knowledge as a triple (subject, predicate, object), which is conceptually identical to an edge from subject to object labeled with predicates. A set of triples constitutes an edge-labeled graph on which knowledge inference is performed. Subgraph matching has been extensively used as a query language for patterns in the context of edge-labeled graphs. For example, in social networks, users can specify a subgraph matching query to find all people that have certain neighborhood relationships. Heavily used fragments of the SPARQL query language for the Semantic Web and graph queries of other graph DBMS can also be viewed as subgraph matching over large graphs. Though subgraph matching has been extensively studied as a query paradigm in the Semantic Web and in social networks, a user can get a large number of answers in response to a query. These answers can be shown to the user in accordance with an importance ranking. In this thesis proposal, we present four different scoring models along with scalable algorithms to find the top-k answers via a suite of intelligent pruning techniques. The suggested models consist of a practically important subset of the SPARQL query language augmented with some additional useful features. The first model called Substitution Importance Query (SIQ) identifies the top-k answers whose scores are calculated from matched vertices' properties in each answer in accordance with a user-specified notion of importance. The second model called Vertex Importance Query (VIQ) identifies important vertices in accordance with a user-defined scoring method that builds on top of various subgraphs articulated by the user. Approximate Importance Query (AIQ), our third model, allows partial and inexact matchings and returns top-k of them with a user-specified approximation terms and scoring functions. In the fourth model called Probabilistic Importance Query (PIQ), a query consists of several sub-blocks: one mandatory block that must be mapped and other blocks that can be opportunistically mapped. The probability is calculated from various aspects of answers such as the number of mapped blocks, vertices' properties in each block and so on and the most top-k probable answers are returned. An important distinguishing feature of our work is that we allow the user a huge amount of freedom in specifying: (i) what pattern and approximation he considers important, (ii) how to score answers - irrespective of whether they are vertices or substitution, and (iii) how to combine and aggregate scores generated by multiple patterns and/or multiple substitutions. Because so much power is given to the user, indexing is more challenging than in situations where additional restrictions are imposed on the queries the user can ask. The proposed algorithms for the first model can also be used for answering SPARQL queries with ORDER BY and LIMIT, and the method for the second model also works for SPARQL queries with GROUP BY, ORDER BY and LIMIT. We test our algorithms on multiple real-world graph databases, showing that our algorithms are far more efficient than popular triple stores.

Implementation of an intelligent sensor for measurement and prediction of solar radiation and atmospheric temperature

Dissertação de mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2011

DESIGN OF EFFICIENT IN-NETWORK DATA PROCESSING AND DISSEMINATION FOR VANETS

By providing vehicle-to-vehicle and vehicle-to-infrastructure wireless communications, vehicular ad hoc networks (VANETs), also known as the “networks on wheels”, can greatly enhance traffic safety, traffic efficiency and driving experience for intelligent transportation system (ITS). However, the unique features of VANETs, such as high mobility and uneven distribution of vehicular nodes, impose critical challenges of high efficiency and reliability for the implementation of VANETs. This dissertation is motivated by the great application potentials of VANETs in the design of efficient in-network data processing and dissemination. Considering the significance of message aggregation, data dissemination and data collection, this dissertation research targets at enhancing the traffic safety and traffic efficiency, as well as developing novel commercial applications, based on VANETs, following four aspects: 1) accurate and efficient message aggregation to detect on-road safety relevant events, 2) reliable data dissemination to reliably notify remote vehicles, 3) efficient and reliable spatial data collection from vehicular sensors, and 4) novel promising applications to exploit the commercial potentials of VANETs. Specifically, to enable cooperative detection of safety relevant events on the roads, the structure-less message aggregation (SLMA) scheme is proposed to improve communication efficiency and message accuracy. The scheme of relative position based message dissemination (RPB-MD) is proposed to reliably and efficiently disseminate messages to all intended vehicles in the zone-of-relevance in varying traffic density. Due to numerous vehicular sensor data available based on VANETs, the scheme of compressive sampling based data collection (CS-DC) is proposed to efficiently collect the spatial relevance data in a large scale, especially in the dense traffic. In addition, with novel and efficient solutions proposed for the application specific issues of data dissemination and data collection, several appealing value-added applications for VANETs are developed to exploit the commercial potentials of VANETs, namely general purpose automatic survey (GPAS), VANET-based ambient ad dissemination (VAAD) and VANET based vehicle performance monitoring and analysis (VehicleView). Thus, by improving the efficiency and reliability in in-network data processing and dissemination, including message aggregation, data dissemination and data collection, together with the development of novel promising applications, this dissertation will help push VANETs further to the stage of massive deployment.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Hardware-software design of embedded systems for intelligent sensing applications

This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain, Wireless Sensor Network developments and an AI framework for vibration-based structural assessment.

Fat Substitutes In Processing Of Sausages Using Piramutaba Waste.

The aim of this study was to evaluate fat substitute in processing of sausages prepared with surimi of waste from piramutaba filleting. The formulation ingredients were mixed with the fat substitutes added according to a fractional planning 2(4-1), where the independent variables, manioc starch (Ms), hydrogenated soy fat (F), texturized soybean protein (Tsp) and carrageenan (Cg) were evaluated on the responses of pH, texture (Tx), raw batter stability (RBS) and water holding capacity (WHC) of the sausage. Fat substitutes were evaluated in 11 formulations and the results showed that the greatest effects on the responses were found to Ms, F and Cg, being eliminated from the formulation Tsp. To find the best formulation for processing piramutaba sausage was made a complete factorial planning of 2(3) to evaluate the concentrations of fat substitutes in an enlarged range. The optimum condition found for fat substitutes in the sausages formulation were carrageenan (0.51%), manioc starch (1.45%) and fat (1.2%).

Central Auditory Processing Outcome After Stroke In Children.

To investigate central auditory processing in children with unilateral stroke and to verify whether the hemisphere affected by the lesion influenced auditory competence. 23 children (13 male) between 7 and 16 years old were evaluated through speech-in-noise tests (auditory closure); dichotic digit test and staggered spondaic word test (selective attention); pitch pattern and duration pattern sequence tests (temporal processing) and their results were compared with control children. Auditory competence was established according to the performance in auditory analysis ability. Was verified similar performance between groups in auditory closure ability and pronounced deficits in selective attention and temporal processing abilities. Most children with stroke showed an impaired auditory ability in a moderate degree. Children with stroke showed deficits in auditory processing and the degree of impairment was not related to the hemisphere affected by the lesion.

Temporal Auditory Processing And Phonological Awareness In Children With Benign Epilepsy With Centrotemporal Spikes.

The aim of this research was to analyze temporal auditory processing and phonological awareness in school-age children with benign childhood epilepsy with centrotemporal spikes (BECTS). Patient group (GI) consisted of 13 children diagnosed with BECTS. Control group (GII) consisted of 17 healthy children. After neurological and peripheral audiological assessment, children underwent a behavioral auditory evaluation and phonological awareness assessment. The procedures applied were: Gaps-in-Noise test (GIN), Duration Pattern test, and Phonological Awareness test (PCF). Results were compared between the groups and a correlation analysis was performed between temporal tasks and phonological awareness performance. GII performed significantly better than the children with BECTS (GI) in both GIN and Duration Pattern test (P < 0.001). GI performed significantly worse in all of the 4 categories of phonological awareness assessed: syllabic (P = 0.001), phonemic (P = 0.006), rhyme (P = 0.015) and alliteration (P = 0.010). Statistical analysis showed a significant positive correlation between the phonological awareness assessment and Duration Pattern test (P < 0.001). From the analysis of the results, it was concluded that children with BECTS may have difficulties in temporal resolution, temporal ordering, and phonological awareness skills. A correlation was observed between auditory temporal processing and phonological awareness in the suited sample.

Biofilms Of Enterococcus Faecalis And Enterococcus Faecium Isolated From The Processing Of Ricotta And The Control Of These Pathogens Through Cleaning And Sanitization Procedures.

The biofilm formation of Enterococcus faecalis and Enterococcus faecium isolated from the processing of ricotta on stainless steel coupons was evaluated, and the effect of cleaning and sanitization procedures in the control of these biofilms was determined. The formation of biofilms was observed while varying the incubation temperature (7, 25 and 39°C) and time (0, 1, 2, 4, 6 and 8days). At 7°C, the counts of E. faecalis and E. faecium were below 2log10CFU/cm(2). For the temperatures of 25 and 39°C, after 1day, the counts of E. faecalis and E. faecium were 5.75 and 6.07log10CFU/cm(2), respectively, which is characteristic of biofilm formation. The tested sanitation procedures a) acid-anionic tensioactive cleaning, b) anionic tensioactive cleaning+sanitizer and c) acid-anionic tensioactive cleaning+sanitizer were effective in removing the biofilms, reducing the counts to levels below 0.4log10CFU/cm(2). The sanitizer biguanide was the least effective, and peracetic acid was the most effective. These studies revealed the ability of enterococci to form biofilms and the importance of the cleaning step and the type of sanitizer used in sanitation processes for the effective removal of biofilms.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

Behavioral alarm treatment for nocturnal enuresis

PURPOSES: To investigate the efficacy of alarm treatment in a sample of Brazilian children and adolescents with nocturnal enuresis and relate treatment success to age and type of clinical support. MATERIALS AND METHODS: During 32 weeks, 84 children and adolescents received alarm treatment together with weekly psychological support sessions for individual families or groups of 5 to 10 families. RESULTS: 71% of the participants achieved success, defined as 14 consecutive dry nights. The result was similar for children and adolescents and for individual or group support. The time until success was shorter for participants missing fewer support sessions. CONCLUSIONS: Alarm treatment was effective for the present sample, regardless of age or type of support. Missing a higher number of support sessions, which may reflect low motivation for treatment, increased the risk of failure.

Changes in parents' and self-reports of behavioral problems in Brazilian adolescents after behavioral treatment with urine alarm for nocturnal enuresis

PURPOSE: Compare parents' reports of youth problems (PRYP) with adolescent problems self-reports (APSR) pre/post behavioral treatment of nocturnal enuresis (NE) based on the use of a urine alarm. MATERIALS AND METHODS: Adolescents (N = 19) with mono-symptomatic (primary or secondary) nocturnal enuresis group treatment for 40 weeks. Discharge criterion was established as 8 weeks with consecutive dry nights. PRYP and APSR were scored by the Child Behavior Checklist (CBCL) and Youth Self-Report (YSR). RESULTS: Pre-treatment data: 1) Higher number of clinical cases based on parent report than on self-report for Internalizing Problems (IP) (13/19 vs. 4/19), Externalizing Problems (EP) (7/19 vs. 5/19) and Total Problem (TP) (11/19 vs. 5/19); 2) Mean PRYP scores for IP (60.8) and TP (61) were within the deviant range (T score ≥ 60); while mean PRYP scores for EP (57.4) and mean APSR scores (IP = 52.4, EP = 49.5, TP = 52.4) were within the normal range. Difference between PRYP' and APSR' scores was significant. Post treatment data: 1) Discharge for majority of the participants (16/19); 2) Reduction in the number of clinical cases on parental evaluation: 9/19 adolescents remained within clinical range for IP, 2/19 for EP, and 7/19 for TP. 3) All post-treatment mean scores were within the normal range; the difference between pre and post evaluation scores was significant for PRYP. CONCLUSIONS: The behavioral treatment based on the use of urine alarm is effective for adolescents with mono-symptomatic (primary and secondary) nocturnal enuresis. The study favors the hypothesis that enuresis is a cause, not a consequence, of other behavioral problems.

Vegetable-origin foam employed in dye extraction in tanning and leather processing facilities

This study addressed the use of conventional and vegetable origin polyurethane foams to extract C. I. Acid Orange 61 dye. The quantitative determination of the residual dye was carried out with an UV/Vis absorption spectrophotometer. The extraction of the dye was found to depend on various factors such as pH of the solution, foam cell structure, contact time and dye and foam interactions. After 45 days, better results were obtained for conventional foam when compared to vegetable foam. Despite presenting a lower percentage of extraction, vegetable foam is advantageous as it is considered a polymer with biodegradable characteristics.