Optimisation of the vehicle transmission and the gear-shifting strategy for the minimum fuel consumption and the minimum nitrogen oxide emissions

The paper outlines a computationally efficient analytical method for evaluating the fuel consumption and the nitrogen oxide emissions during manoeuvres pertaining to the New European Driving Cycle. An integrated optimisation procedure is also included in the analyses with minimisation of the brake specific fuel consumption and minimisation of the nitrogen oxide emissions as objective functions. A set of optimum gear ratios are determined for a four-speed transmission, a five-speed transmission and six-speed transmission as the governing parameters in the optimisation process. The analysis highlights the determination of gear-shifting objective-driven strategies based on the minimisation of either of the declared objective functions. A reduction of 7.5% in the brake specific fuel consumption and a reduction of 6.75% in nitrogen oxide emissions are attainable in the best-case scenario for a six-speed transmission and a gear-shifting strategy based on the lowest brake specific fuel consumption for the case of an engine. The novel integrated analytical simulations and multi-objective optimisation have not been hitherto reported in literature. It provides the opportunity for an objective intelligent-based approach to the use of gear shift indicator technology. The results of this study also show that transmission optimisation can act as an effective and inexpensive mean to enhance the fuel efficiency and to reduce the emissions

Efficient and Unbiased Estimation of Population Size

Population sizing from still aerial pictures is of wide applicability in ecological and social sciences. The problem is long standing because current automatic detection and counting algorithms are known to fail in most cases, and exhaustive manual counting is tedious, slow, difficult to verify and unfeasible for large populations. An alternative is to multiply population density with some reference area but, unfortunately, sampling details, handling of edge effects, etc., are seldom described. For the first time we address the problem using principles of geometric sampling. These principles are old and solid, but largely unknown outside the areas of three dimensional microscopy and stereology. Here we adapt them to estimate the size of any population of individuals lying on an essentially planar area, e.g. people, animals, trees on a savanna, etc. The proposed design is unbiased irrespective of population size, pattern, perspective artifacts, etc. The implementation is very simple—it is based on the random superimposition of coarse quadrat grids. Also, an objective error assessment is often lacking. For the latter purpose the quadrat counts are often assumed to be independent. We demonstrate that this approach can perform very poorly, and we propose (and check via Monte Carlo resampling) a new theoretical error prediction formula. As far as efficiency, counting about 50 (100) individuals in 20 quadrats, can yield relative standard errors of about 8% (5%) in typical cases. This fact effectively breaks the barrier hitherto imposed by the current lack of automatic face detection algorithms, because semiautomatic sampling and manual counting becomes an attractive option

New potential antitumoral fluorescent tetracyclic thieno[3,2-b]pyridine derivatives: interaction with DNA and nanosized liposomes

Fluorescence properties of two new potential antitumoral tetracyclic thieno[3,2-b]pyridine derivatives were studied in solution and in liposomes of DPPC (dipalmitoyl phosphatidylcholine), egg lecithin (phosphatidylcholine from egg yolk; Egg-PC) and DODAB (dioctadecyldimethylammonium bromide). Compound 1, pyrido[2',3':3,2]thieno[4,5-d]pyrido[1,2-a]pyrimidin-6-one, exhibits reasonably high fluorescence quantum yields in all solvents studied (0.20 ≤ ΦF ≤ 0.30), while for compound 2, 3-[(p-methoxyphenyl)ethynyl]pyrido[2',3':3,2]thieno[4,5-d]pyrido[1,2-a]pyrimidin-6-one, the values are much lower (0.01 ≤ ΦF ≤ 0.05). The interaction of these compounds with salmon sperm DNA was studied using spectroscopic methods, allowing the determination of intrinsic binding constants, Ki = (8.7 ± 0.9) × 103 M-1 for compound 1 and Ki = (5.9 ± 0.6) × 103 M-1 for 2, and binding site sizes of n = 11 ± 3 and n = 7 ± 2 base pairs, respectively. Compound 2 is the most intercalative compound in salmon sperm DNA (35%), while for compound 1 only 11% of the molecules are intercalated. Studies of incorporation of both compounds in liposomes of DPPC, Egg-PC and DODAB revealed that compound 2 is mainly located in the hydrophobic region of the lipid bilayer, while compound 1 prefers a hydrated and fluid environment

Orthodontics in the era of big data analytics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/1/ocr12279_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/2/ocr12279.pd

Monitoring credit risk in the social economy sector by means of a binary goal programming model

The final publication is available at Springer via http://dx.doi.org/10.1007/s11628-012-0173-7Monitoring the credit risk of firms in the social economy sector presents a considerable challenge, since it is difficult to calculate ratings with traditional methods such as logit or discriminant analysis, due to the relatively small number of firms in the sector and the low default rate among cooperatives. This paper intro- duces a goal programming model to overcome such constraints and to successfully manage credit risk using economic and financial information, as well as expert advice. After introducing the model, its application to a set of Spanish cooperative societies is described.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). Monitoring credit risk in the social economy sector by means of a binary goal programming model. Service Business. 7(3):483-495. doi:10.1007/s11628-012-0173-7S48349573Alfares H, Duffuaa S (2009) Assigning cardinal weights in multi-criteria decision making based on ordinal rankings. J Multicriteria Decis Anal 15:125–133Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23:589–609Altman EI, Hadelman RG, Narayanan P (1977) Zeta analysis: a new model to identify bankruptcy risk of corporations. J Bank Financ 1:29–54Andenmatten A (1995) Evaluation du risque de défaillance des emetteurs d’obligations: Une approche par l’aide multicritère á la décision. Presses Polytechniques et Univertitaires Romandes, LausanneBeaver WH (1966) Financial ratios as predictors of failure. J Account Res 4:71–111Boritz JE, Kennedey DB (1995) Effectiveness of neural network types for prediction of business failure. Expert Syst Appl 9:503–512Bottomley P, Doyle J, Green R (2000) Testing the reliability of weight elicitation methods: direct rating versus point allocation. J Mark Res 37:508–513Casey M, McGee V, Stinkey C (1986) Discriminating between reorganized and liquidated firms in bankruptcy. Account Rev 61:249–262Cruz S, Gonzalez T, Perez C (2010) Marketing capabilities, stakeholders’ satisfaction, and performance. Serv Bus 4:209–223Díaz M, Marcuello C (2010) Impacto económico de las cooperativas. La generación de empleo en las sociedades cooperativas y su relación con el PIB. CIRIEC 67:23–44Dimitras AI, Zopounidis C, Hurson C (1995) A multicriteria decision aid method for the assessment of business failure risk. Found Comput Decis Sci 20:99–112Dimitras AI, Slowinski R, Susmaga R, Zopounidis C (1999) Business failure prediction using rough sets. Eur J Oper Res 114:263–280Elmer PJ, Borowski DM (1988) An expert system approach to financial analysis: the case of S&L bankruptcy. Financ Manage 17:66–76Frydman H, Altman EI, Kao DL (1985) Introducing recursive partitioning for financial classification: the case of financial distress. J Financ 40:269–291García F, Guijarro F, Moya I (2008) La valoración de empresas agroalimentarias: una extensión de los modelos factoriales. Rev Estud Agro-Soc 217:155–181Gupta MC, Huefner RJ (1972) A cluster analysis study of financial ratios and industry characteristics. J Account Res 10:77–95Jensen RE (1971) A cluster analysis study of financial performance of selected firms. Account Rev 16:35–56Juliá J (2011) Social economy: a responsible people-oriented economy. Serv Bus 5:173–175Keasey K, Mcguinnes P, Short H (1990) Multilogit approach to predicting corporate failure: further analysis and the issue of signal consistency. Omega-Int J Manage S 18:85–94Li H, Adeli H, Sun J, Han JG (2011) Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction. Comput Oper Res 38:409–419Luoma M, Laitinen EK (1991) Survival analysis as a tool for firm failure prediction. Omega-Int J Manage S 19:673–678March I, Yagüe RM (2009) Desempeño en empresas de economía social. Un modelo para su medición. CIRIEC 64:105–131Martin D (1977) Early warning of bank failure: a logit regression approach. J Bank Financ 1:249–276Mateos A, Marín M, Marí S, Seguí E (2011) Los modelos de predicción del fracaso empresarial y su aplicabilidad en cooperativas agrarias. CIRIEC 70:179–208McKee T (2000) Developing a bankruptcy prediction model via rough sets theory. Int J Intell Syst Account Finan Manage 9:159–173Messier WF, Hansen JV (1988) Inducing rules for expert system development: an example using default and bankruptcy data. Manage Sci 34:1403–1415Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131Peel MJ (1987) Timeliness of private firm reports predicting corporate failure. Invest Anal J 83:23–27Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkScapens RW, Ryan RJ, Flecher L (1981) Explaining corporate failure: a catastrophe theory approach. J Bus Finan Account 8:1–26Skogsvik R (1990) Current cost accounting ratios as predictors of business failures: the Swedish case. J Bus Finan Account 17:137–160Slowinski R, Zopounidis C (1995) Application of the rough set approach to evaluation of bankruptcy risk. Int J Intell Syst Account Finan Manage 4:24–41Vranas AS (1992) The significance of financial characteristics in predicting business failure: an analysis in the Greek context. Found Comput Decis Sci 17:257–275Westgaard S, Wijst N (2001) Default probabilities in a corporate bank portfolio: a logistic model approach. Eur J Oper Res 135:338–349Wilson RL, Sharda R (1994) Bankruptcy prediction using neuronal networks. Decis Support Syst 11:545–557Zavgren CV (1985) Assessing the vulnerability to failure of American industrial firms. A logistic analysis. J Bus Financ Account 12:19–45Zmijewski M (1984) Methodological issues related to the estimation of financial distress prediction models. Studies on Current Econometric Issues in Accounting Research. J Account Res 22:59–86Zopounidis C, Doumpos M (2002) Multicriteria classification and sorting methods: a literature review. Eur J Oper Res 138:229–24

Changes in the Viral Distribution Pattern after the Appearance of the Novel Influenza A H1N1 (pH1N1) Virus in Influenza-Like Illness Patients in Peru

Background: We describe the temporal variation in viral agents detected in influenza like illness (ILI) patients before and after the appearance of the ongoing pandemic influenza A (H1N1) (pH1N1) in Peru between 4-January and 13-July 2009. Methods: At the health centers, one oropharyngeal swab was obtained for viral isolation. From epidemiological week (EW) 1 to 18, at the US Naval Medical Research Center Detachment (NMRCD) in Lima, the specimens were inoculated into four cell lines for virus isolation. In addition, from EW 19 to 28, the specimens were also analyzed by real time-polymerase-chainreaction (rRT-PCR). Results: We enrolled 2,872 patients: 1,422 cases before the appearance of the pH1N1 virus, and 1,450 during the pandemic. Non-pH1N1 influenza A virus was the predominant viral strain circulating in Peru through (EW) 18, representing 57.8% of the confirmed cases; however, this predominance shifted to pH1N1 (51.5%) from EW 19–28. During this study period, most of pH1N1 cases were diagnosed in the capital city (Lima) followed by other cities including Cusco and Trujillo. In contrast, novel influenza cases were essentially absent in the tropical rain forest (jungle) cities during our study period. The city of Iquitos (Jungle) had the highest number of influenza B cases and only one pH1N1 case. Conclusions: The viral distribution in Peru changed upon the introduction of the pH1N1 virus compared to previous months. Although influenza A viruses continue to be the predominant viral pathogen, the pH1N1 virus predominated over the other influenza A viruses