A Decision Support Tool for the Selection of Promoting Actions to Encourage Collaboration in Projects for the Agriculture Sector

[EN] Development and innovation agencies promote consortiums of agricultural stakeholders to collaborate in the proposal of projects for public calls. To achieve this partnerships, these agencies should select between different promoting actions to be performed with two objectives: maximize the number of project proposals presented and minimize the resources invested. To support agencies with these decisions, a computer tool based on a multi-objective integer linear programming model is proposed. To deal with the two objectives the weighting sum method is implemented. The model is validated in different scenarios by means a realistic case of an agency in Brittany (France). The results show the conflict between the two objectives considered and the dependency of the solutions on the scenarios defined. As a conclusion it can be stated that: 1) decision-makers should be careful in defining the weights of each objective and 2) the impact of the different promoting actions on the level of stakeholders¿ participation should be precisely estimated.The authors acknowledge the support of the project 691249, RUCAPS: "Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems", funded by the European Union¿s research and innovation programme under the H2020 Marie Sk¿odowska-Curie Actions.Alemany Díaz, MDM.; Alarcón Valero, F.; Pérez Perales, D.; Guyon, C. (2020). A Decision Support Tool for the Selection of Promoting Actions to Encourage Collaboration in Projects for the Agriculture Sector. IFIP Advances in Information and Communication Technology. 598:534-545. https://doi.org/10.1007/978-3-030-62412-5_44S534545598European Comission Funded Programs. https://ec.europa.eu/programmes/horizon2020Zoie, C., Radulescu, M.: Decision analysis for the project selection problem under risk. IFAC Proc. 34(8), 445–450 (2001)Sadi-Nezhad, S.: A state-of-art survey on project selection using MCDM techniques. J. Project Manage. 2, 1–10 (2017)Caballero, H.C., Chopra, S., Schmidt, E.K.: Project portfolio selection using mathematical programming and optimization methods. In: Paper presented at PMI® Global Congress 2012–North America, Vancouver, British Columbia, Canada, Newtown Square, PA, Project Management Institute (2012)Ahmad, B., Haq, I.: Project selection techniques, relevance and applications in Pakistan. Int. J. Technol. Res. 4, 52–60 (2016)Inuiguchi, M., Ramı́k, J.: Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets Syst. 111(1), 3–28 (2000)Stewart, R., Mohamed, S.: IT/IS projects selection using multi-criteria utility theory. Log. Inf. Manage. 15(4), 254–270 (2002)Alzober, W., Yaakub, A.R.: Integrated model for MCDM: selection contractor in Malaysian construction industry. In: Applied Mechanics and Materials 548, pp. 1587–1595. Trans Tech Publications (2014)Adhikary, P., Roy, P.K., Mazumdar, A.: Optimal renewable energy project selection: a multi-criteria optimization technique approach. Global J. Pure Appl. Math. 11(5), 3319–3329 (2015)Strang, K.D.: Portfolio selection methodology for a nuclear project. Project Manage. J. 42(2), 81–93 (2011)Benjamin, C.O.: A linear goal-programming model for public-sector project selection. J. Oper. Res. Soc. 36(1), 13–23 (1985)Coronado, J.R., Pardo-Mora, E.M., Valero, M.: A multi-objective model for selection of projects to finance new enterprise SMEs in Colombia. J. Ind. Eng. Manage. 4(3), 407–417 (2011)Mat, N.A.C., Cheung, Y.: Partner selection: criteria for successful collaborative network. In: 20th Australian Conference on Information Systems, pp. 631–641 (2009)Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative Networks. In: Wang, K., Kovacs, G.L., Wozny, M., Fang, M. (eds.) PROLAMAT 2006. IIFIP, vol. 207, pp. 26–40. Springer, Boston, MA (2006). https://doi.org/10.1007/0-387-34403-9_4Paixão, M., Sbragia, R., Kruglianskas, I.: Factors for selecting partners in innovation projects–evidences from alliances in the Brazilian petrochemical leader. Rev. Admin. Innov. São Paulo 11(2), 241–272 (2014)Duisters, D., Duysters, G., de Man, A.P.: The partner selection process: steps, effectiveness, governance. Ann. Hematol. 2, 7–25 (2011)Zhang, X.: Criteria for selecting the private-sector partner in public-private partnerships. J. Constr. Eng. Manage. 131(6), 631–644 (2005

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Metabolic Programming during Lactation Stimulates Renal Na+ Transport in the Adult Offspring Due to an Early Impact on Local Angiotensin II Pathways

BACKGROUND: Several studies have correlated perinatal malnutrition with diseases in adulthood, giving support to the programming hypothesis. In this study, the effects of maternal undernutrition during lactation on renal Na(+)-transporters and on the local angiotensin II (Ang II) signaling cascade in rats were investigated. METHODOLOGY/PRINCIPAL FINDINGS: Female rats received a hypoproteic diet (8% protein) throughout lactation. Control and programmed offspring consumed a diet containing 20% protein after weaning. Programming caused a decrease in the number of nephrons (35%), in the area of the Bowman's capsule (30%) and the capillary tuft (30%), and increased collagen deposition in the cortex and medulla (by 175% and 700%, respectively). In programmed rats the expression of (Na(+)+K(+))ATPase in proximal tubules increased by 40%, but its activity was doubled owing to a threefold increase in affinity for K(+). Programming doubled the ouabain-insensitive Na(+)-ATPase activity with loss of its physiological response to Ang II, increased the expression of AT(1) and decreased the expression of AT(2) receptors), and caused a pronounced inhibition (90%) of protein kinase C activity with decrease in the expression of the α (24%) and ε (13%) isoforms. Activity and expression of cyclic AMP-dependent protein kinase decreased in the same proportion as the AT(2) receptors (30%). In vivo studies at 60 days revealed an increased glomerular filtration rate (GFR) (70%), increased Na(+) excretion (80%) and intense proteinuria (increase of 400% in protein excretion). Programmed rats, which had normal arterial pressure at 60 days, became hypertensive by 150 days. CONCLUSIONS/SIGNIFICANCE: Maternal protein restriction during lactation results in alterations in GFR, renal Na(+) handling and in components of the Ang II-linked regulatory pathway of renal Na(+) reabsorption. At the molecular level, they provide a framework for understanding how metabolic programming of renal mechanisms contributes to the onset of hypertension in adulthood

Kualitas Hidup Pasien Diabetes Melitus Tipe 2 di Puskesmas Se Kota Kupang

Diabetes Mellitus is well known as a chronic disease which can lead to a decrease in quality of life in all domains. The study aims to explore the diabetic type 2 patient\u27s quality of life and find out the factors affecting in type 2 diabetic mellitus patients. The cross-sectional study design is used that included 65 patient with type 2 diabetes mellitus, in 11 public health centers of Kupang City. Data were collected by using Short Form Survey (SF-36) that assessed 8-scale health profile. Independent sample t-test is used to analyze the correlation between the factors affecting and the quality of life. the study showed that the QoL of DM patients decreased in all 8- health profile including physical functioning, social functioning, mental health, general health, pain, change in the role due to physical problems and emotional problems. The Study also showed there was a relationship between gender, duration of suffering from Diabetes mellitus, and complications to the quality of life. Male perceived a better quality of life than female