Iowa Roads and Streets Functional Classification Manual, 1970

This manual provides a set of procedural rules and regulations for use in functionally classifying all roads and streets in Iowa according to the character of service they are intended to provide. Functional classification is a requirement of House File 394 (Functional Highway Classification Bill) enacted by the 63rd General Assembly of the Iowa Legislature. Functional classification is defined in this Bill as: "The grouping of roads and streets into systems according to the character of service they will be expected to provide, and the assignment of jurisdiction over each class to the governmental unit having primary interest in each type of service."

Iowa Roads and Streets Functional Classification Manual, 1975

This manual provides a set of procedural rules and regulations for use in functionally classifying all roads and streets in Iowa according to the character of service they are intended to provide. Functional classification is a requirement of the 1973 Code of Iowa (Chapter 306) as amended by Senate File 1062 enacted by the 2nd session of the 65th General Assembly of Iowa. Functional classification is defined as the grouping of roads and streets into systems according to the character of service they will be expected to provide, and the assignment of jurisdiction over each class to the governmental unit having primary interest in each type of service. Stated objectives of the legislation are: "Functional classification will serve the legislator by providing an equitable basis for determination of proper source of tax support and providing for the assignment of financial resources to the governmental unit having responsibility for each class of service. Functional classification promotes the ability of the administrator to effectively prepare and carry out long range programs which reflect the transportation needs of the public." All roads and streets in legal existence will be classified. Instructions are also included in this manual for a continuous reporting to the Highway Commission of changes in classification and/or jurisdiction resulting from new construction, corporation line changes, relocations, and deletions. This continuous updating of records is absolutely essential for modern day transportation planning as it is the only possible way to monitor the status of existing road systems, and consequently determine adequacy and needs with accuracy.

Characterization and multivariate classification of grapes and wines of two Cabernet Sauvignon clones

The objective of this work was to assess and characterize two clones, 169 and 685, of Cabernet Sauvignon grapes and to evaluate the wine produced from these grapes. The experiment was carried out in São Joaquim, SC, Brazil, during the 2009 harvest season. During grape ripening, the evolution of physical-chemical properties, phenolic compounds, organic acids, and anthocyanins was evaluated. During grape harvest, yield components were determined for each clone. Individual and total phenolics, individual and total anthocyanins, and antioxidant activity were evaluated for wine. The clones were also assessed regarding the duration of their phenological cycle. During ripening, the evolution of phenolic compounds and of physical-chemical parameters was similar for both clones; however, during harvest, significant differences were observed regarding yield, number of bunches per plant and berries per bunch, leaf area, and organic acid, polyphenol, and anthocyanin content. The wines produced from these clones showed significant differences regarding chemical composition. The clones showed similar phenological cycle and responses to bioclimatic parameters. Principal component analysis shows that clone 685 is strongly correlated with color characteristics, mainly monomeric anthocyanins, while clone 169 is correlated with individual phenolic compounds.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Approximate polytope ensemble for one-class classification

In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.

Detection and chemical profiling of medicine counterfeits by Raman spectroscopy and chemometrics

Raman spectroscopy combined with chemometrics has recently become a widespread technique for the analysis of pharmaceutical solid forms. The application presented in this paper is the investigation of counterfeit medicines. This increasingly serious issue involves networks that are an integral part of industrialized organized crime. Efficient analytical tools are consequently required to fight against it. Quick and reliable authentication means are needed to allow the deployment of measures from the company and the authorities. For this purpose a method in two steps has been implemented here. The first step enables the identification of pharmaceutical tablets and capsules and the detection of their counterfeits. A nonlinear classification method, the Support Vector Machines (SVM), is computed together with a correlation with the database and the detection of Active Pharmaceutical Ingredient (API) peaks in the suspect product. If a counterfeit is detected, the second step allows its chemical profiling among former counterfeits in a forensic intelligence perspective. For this second step a classification based on Principal Component Analysis (PCA) and correlation distance measurements is applied to the Raman spectra of the counterfeits.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Length variation in HIV-1 gp120 as the product of DNA misalignment mechanism

Background: To determine whether misalignment structures such as duplications, repeats, and palindromes are associated to insertions/deletions (indels) in gp120, indicating that indels are indeed frameshift mutations generated by DNA misalignment mechanism. Methods: Cloning and sequencing of a fragment of HIV-1 gp120 spanning C2-C4 derived from plasma RNA in 12 patients with early chronic disease and naïve to antiretroviral therapy. Results: Indels in V4 involved always insertion and deletion of duplicated nucleotide segments, and AAT repeats, and were associated to the presence of palindromic sequences. No duplications were detected in V3 and C3. Palindromic sequences occurred with similar frequencies in V3, C3 and V4; the frequency of palindromes in individual genes was found to be significantly higher in structural (gp120, p ≤ 3.00E-7) and significantly lower in regulatory (Tat, p ≤ 9.00E-7) genes, as compared to the average frequency calculated over the full genome. Discussion: Indels in V4 are associated to misalignment structures (i.e. duplications repeat and palindromes) indicating DNA misalignment as the mechanism underlying length variation in V4. The finding that indels in V4 are caused by DNA misalignment has some very important implications: 1) indels in V4 are likely to occur in proviral DNA (and not in RNA), after integration of HIV into the host genome; 2) they are likely to occur as progressive modifications of the early founder virus during chronic infection, as more and more cells get infected; 3) frameshift mutations involving any number of base pairs are likely to occur evenly across gp120; however, only those mutants carrying a functional gp120 (indels as multiples of three base pairs) will be able to perpetuate the virus cycle and to keep spreading through the population.

Product Perfect Codes and Steganography

A new coding technique to be used in steganography is evaluated. The performanceof this new technique is computed and comparisons with the well-known theoreticalupper bound, Hamming upper bound and basic LSB are established.

Classification and fingerprinting of different berries based on biochemical profiling and antioxidant capacity

The objective of this work was to evaluate the biochemical composition of six berry types belonging to Fragaria, Rubus, Vaccinium and Ribes genus. Fruit samples were collected in triplicate (50 fruit each) from 18 different species or cultivars of the mentioned genera, during three years (2008 to 2010). Content of individual sugars, organic acids, flavonols, and phenolic acids were determined by high performance liquid chromatography (HPLC) analysis, while total phenolics (TPC) and total antioxidant capacity (TAC), by using spectrophotometry. Principal component analysis (PCA) and hierarchical cluster analysis (CA) were performed to evaluate the differences in fruit biochemical profile. The highest contents of bioactive components were found in Ribes nigrum and in Fragaria vesca, Rubus plicatus, and Vaccinium myrtillus. PCA and CA were able to partially discriminate between berries on the basis of their biochemical composition. Individual and total sugars, myricetin, ellagic acid, TPC and TAC showed the highest impact on biochemical composition of the berry fruits. CA separated blackberry, raspberry, and blueberry as isolate groups, while classification of strawberry, black and red currant in a specific group has not occurred. There is a large variability both between and within the different types of berries. Metabolite fingerprinting of the evaluated berries showed unique biochemical profiles and specific combination of bioactive compound contents.

Materiaalitietokanta prosessilaitetoimituksen laitevalintojen tukena

In this master's thesis, material characteristics which includes material database will be developed, through which will be managed process device deliveries device selections. The analysed bulk materials exact values serve as basic data when choosing glass- and dried product industry raw material equipment solutions. In this study, the material database function structure will be divided into classified bulk materials characteristics and process device guidelines and planning which they are based on. In order to achieve the best possible advantage in use of the material database, the raw material handling with the process equipment has to be controlled with the help of database bulk material information. With the help of material database, the personnel of the company will find the needed material and device information to implement the processes. The structure of the database enables adding of information, and hence the database will be always kept updated. The material database uses free PhpOpenDatabase software programme, which adapts itself flexibly with the software and requirements of the company. The material frame and considered features of the material database were determined by the requirements of the company's device technology. National and international classifications were used as help. The collected material information was analysed and the correspondence between internal study results and found classifications were determined. As presented before, on the grounds of chosen and specified material features, also the controllability of device selecting and solutions was improved with device-specific screening and planning guidelines. As material frame of the material database was chosen ANSI/CEMA Standard 550. Its almost 1000 bulk materials form an extensive database frame. Over 600 of these raw materials have been determined according to classification. However, the most important ones are the materials of glass- and dried material plants, which consist of the last part of material frame. The analysis will be sustained and material information will be completed primarily of material part with test run in one's own material laboratory.

A proposal for an international retinoblastoma staging system.

BACKGROUND: Although intra-retinal tumor has long been staged presurgically according to the Reese-Ellsworth (R-E) system, retinoblastoma differs from other pediatric neoplasms in never having had a widely accepted classification system that encompasses the entire spectrum of the disease. Comparisons among studies that consider disease extension, risk factors for extra-ocular relapse, and response to therapy require a universally accepted staging system for extra-ocular disease. PROCEDURE: A committee of retinoblastoma experts from large centers worldwide has developed a consensus classification that can encompass all retinoblastoma cases and is presented herein. Patients are classified according to extent of disease and the presence of overt extra-ocular extension. In addition, a proposal for substaging considering histopathological features of enucleated specimens is presented to further discriminate between Stage I and II patients. RESULTS: The following is a summary of the classification system developed-Stage 0: Patients treated conservatively (subject to presurgical ophthalmologic classifications); Stage I: Eye enucleated, completely resected histologically; Stage II: Eye enucleated, microscopic residual tumor; Stage III: Regional extension [(a) overt orbital disease, (b) preauricular or cervical lymph node extension]; Stage IV: Metastatic disease [(a) hematogenous metastasis: (1) single lesion, (2) multiple lesions; (b) CNS extension: (1) prechiasmatic lesion, (2) CNS mass, (3) leptomeningeal disease]. A proposal is also presented for substaging of enucleated Stages I and II eyes. CONCLUSIONS: The proposed staging system is the product of an international effort to adopt a uniform staging system for patients with retinoblastoma to cover the whole spectrum of the disease.

Product costing in SAP environment

Kustannuslaskennan ala on muuttumassa. IT teknologian hämmästyttävän nopea kehitys viime vuosina on luonut kustannuslaskennalle uusia mahdollisuuksia. Toisaalta myös kustannuslaskennalle asetetut vaatimukset ovat muuttuneet. Nykyisin kustannustietoutta tarvitaan erityisesti päätöksenteon tueksi. Kaikesta huolimatta yleisesti käytetyt laskentamenetelmät eivät ole muuttuneet. Työn tavoite on tutkia mahdollisia tapoja tuotekohtaisen kustannuslaskennan toteuttamiseksi SAP toiminnanohjausjärjestelmän avulla. Erityisesti toiminnanohjausjärjestelmien nopea kehitys on mahdollistanut aikaisempaa nopeamman ja tarkemman kustannuslaskennan. On kuitenkin muistettava, että järjestelmät ovat vaintyökaluja. On siis erittäin tärkeää valita tarkoituksenmukainen laskentamenetelmä. Työn perimmäinen tarkoitus onkin valita kyseessä olevaan tilanteeseen sopivin menetelmä.Viime vuosien akateemisessa kirjoittelussa on esitelty useita uusia kustannuslaskentamenetelmiä. Toimintolaskennan uusi versio, Time-Driven Activity-Based Costing, on eräs varteenotettava vaihtoehto tuotekohtaisen kustannuslaskennan toteuttamiseksi SAP-ympäristössä. Toinen hyvin soveltuva menetelmä on Resource Consumption Accounting (RCA), joka soveltuu erityisen hyvin SAP-ympäristöön.Molemmilla menetelmillä on hyvät ja huonot puolet, mutta tässä tapauksessa RCA soveltuu tehtävään paremmin. RCA on joustava ja tarjoaa laaja-alaista tietoa. Tästä syystä Resource Consumption Accounting oli paras vaihtoehto.Työssä rakennettu RCA malli testattiin kolmen erilaisen tuotteen historiallisella datalla. RCA:ntuomia mahdollisuuksia pohdittiin ja ongelmia selvitettiin. RCA on hyvin saman tyyppinen yrityksessä jo käytettävän laskentamenetelmän kanssa. Implementointi ei siis olisi mahdoton tehtävä. RCA kuitenkin tarjoaa mahdollisuuksia, joita nykyisellä menetelmällä ei voida saavuttaa.