Evaluation of Silicon Supplementation for Drought Stress under Water-Deficit Conditions: An Application of Sustainable Agriculture

Drought is a key abiotic stress that confines agriculture development worldwide. Silicon (Si) is commonly considered to be a valuable element for resistance against drought and for sustainable agriculture. To investigate the morpho-physiological and biochemical characteristics of Gerbera jamesonii plants, a pot experiment was conducted under greenhouse conditions and exposed to water stress (60% FC) and well-watered (100% FC) conditions. Foliar application of Si was carried out after ten days (48 days after sowing) of drought treatment and was repeated weekly, while well-water was regarded as control. Water deficiency significantly abridged the morphological attributes, pigments, and stress-related metabolites and negatively affected the photosynthetic apparatus in drought-stressed gerbera plants. However, Si supplementation by 40 mg L-1 produced increased leaf area (31%), stem length (25%), flower diameter (22%), plant fresh biomass (17%), total chlorophyll (48%), and concentration of carotenoids (54%) in water-stressed plants. Similarly, the accretion of a total free amino acid (41%) and the activities of peroxidase, catalase, superoxide dismutase, ascorbate peroxidase, glycinebetaine, total soluble proteins, total free proline, and malondialdehyde were enhanced by 44%, 31%, 53%, 33%, 330%, 61%, 51%, and 66%, respectively, under drought stress in comparison with control conditions. Meanwhile, the photosynthetic rate (89%), the transpiration rate (12%), and stomatal conductance (55%) were significantly enhanced in water-deficit gerbera leaves with Si supplementation. This study proposes that the foliar application of Si is a viable and convenient method of improving the performance of elegant gerbera flower plants in regions of the world that are facing severe water deficiency

An integrated approach to diagnosis and management of severe haemoptysis in patients admitted to the intensive care unit: a case series from a referral centre

BACKGROUND: Limited data are available concerning patients admitted to the intensive care unit (ICU) for severe haemoptysis. We reviewed a large series of patients managed in a uniform way to describe the clinical spectrum and outcome of haemoptysis in this setting, and better define the indications for bronchial artery embolisation (BAE). METHODS: A retrospective chart review of 196 patients referred for severe haemoptysis to a respiratory intermediate care ward and ICU between January 1999 and December 2001. A follow-up by telephone interview or a visit. RESULTS: Patients (148 males) were aged 51 (± sd, 16) years, with a median cumulated amount of bleeding averaging 200 ml on admission. Bronchiectasis, lung cancer, tuberculosis and mycetoma were the main underlying causes. In 21 patients (11%), no cause was identified. A first-line bronchial arteriography was attempted in 147 patients (75%), whereas 46 (23%) received conservative treatment. Patients who underwent BAE had a higher respiratory rate, greater amount of bleeding, persistent bloody sputum and/or evidence of active bleeding on fiberoptic bronchoscopy. When completed (n = 131/147), BAE controlled haemoptysis in 80% of patients, both in the short and long (> 30 days) terms. Surgery was mostly performed when bronchial arteriography had failed and/or bleeding recurred early after completed BAE. Bleeding was controlled by conservative measures alone in 44 patients. The ICU mortality rate was low (4%). CONCLUSION: Patients with evidence of more severe or persistent haemoptysis were more likely to receive BAE rather than conservative management. The procedure was effective and safe in most patients with severe haemoptysis, and surgery was mostly reserved to failure of arteriography and/or early recurrences after BAE

Using Weighted Goal Programming Model for Planning Regional Sustainable Development to Optimal Workforce Allocation:An Application for Provinces of Iran

Due to the urbanization and economic growth, planning of regional sustainable development has become one of the major challenges in the world. The key indicators such as gross domestic product (GDP), electricity and energy consumption and greenhouse gas emission (GHG) are considered in sustainable development planning. This paper determines number of required workforce in diferent sectors of each province in Iran considering targets/goals for sustainable development indicators in the 2030 macroeconomic and regional planning. First, the relative goals are designed for GDP, electricity, energy and GHG emission and then, two weighted goal programming models are applied to allocate the optimal workforce among four sectors: agriculture, industry, services and transportation. The frst model minimizes recruitment of new workforce and allows current workforce exchange among the four sectors in each province in order to achieve the goals, while the second model indicates equitable distribution of new workforce recruitment in diferent sectors within each province. In both models, the workforce changes have been investigated based on achieving the desirable growth rates of GDP, GHG, electricity and energy consumption as planned by the government. Based on the results of this paper, policy makers can manage workforce and the government can make optimized decisions to macroeconomic and regional planning

Determination of optimum dosage of Ovaprim injectionon artificial spawning efficiency of Esox lucius

This project was conducted to goal of optimum dosage determination of ovaprim injection to artificial spawning efficiency of Esox lucius. The research implemented by 4 treatments with 3 replicates for each ones. 3 female and 6 male brooders injected in each replicate. The animals in 1, 2 and 3 treatments injected by 10, 20 and 30 µg/kg BW, respectively, and 4th treatment as a control injected with 4 mg/kg BW pituitary gland extract. Average weight of brooders were 1361±521, 1376±954, 1009±160 and 1100 ±422 g in 1, 2, 3 and 4 treatments in females and 689±145, 734±197, 547±118 and 794±238 g in males, respectively. In addition, positive response percent to hormone injection were measured 77.8 ±19.24 , 88.9 ± 19.24 , 55.5 ±50.91 and 55.5 ± 19.24 % in 1, 2, 3 and 4 treatments in female and 94.4 ± 9.58, 88.9 ±19.26 , 83.3±28.86 and 88.9 ± 19.26 % in male brooders, respectively, but there was no significant different between all of treatments (p<0.05). Incubation period from fertilization till hatching step in 7 to 15 ˚C was 5 to 10 days with average of 7±1.5 days. Fertilization content was in 1 to 4 treatments measured 87.1±10, 88.04±7.7, 83.9±5.2 and 72.4±19.7 %, respectively and also the treatments didn’t show any different significantly together (p<0.05). Average percentage of eyed eggs 66.6±15.9 in treat 1, 61.2±22.3 in treat 2, 58.3±10.7 in treat 3 and 56.1±15.04 in treat 4, without any significant different between of them (p<0.05). Hatching of eggs mean were measured 27.41±19.8 in treat 1, 39.53±26.9 in treat 2, 95.18±5.6 in treat 3 and 26.78±12.4 in treat 4, and significant different observed between of them too (p<0.05).In the other hand, mean percent of larvae with active feeding in these treatments were measured 18.77±14.6, 20.1±8.51, 55.6±11.6 and 14.51±7.72 as the treatments had significant different (p<0.05). Also, the best temperature and dosage injection of ovaprim hormone was 9 to 12.5 ˚C and 20µg/kg BW, respectively. The end of trial, from 103740 larvae introduced to earthen pond obtained 8000 fingerlings with weight of 2.68±0.6 g and length of 6.96±0.51 cm

Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review

[EN] The supplier evaluation and selection process is critical to increase the sustainability and resilience of the agri-food supply chain. Therefore, in this sector, it is necessary to consider sustainability and resilience criteria in the supplier evaluation and selection process. The use of arti¿cial intelligence techniques allows managing of a lot of information and the reduction of uncertainty for decision making. The objective of this article is to analyze articles that address the selection of suppliers in agrifood supply chains that pursue to increase their sustainability and resilience by using arti¿cial intelligence techniques to analyze the techniques and criteria used and draw conclusions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review. IFIP Advances in Information and Communication Technology. 598:501-510. https://doi.org/10.1007/978-3-030-62412-5_41S501510598Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S.: Quantitative models for sustainable supply chain management: developments and directions. Eur. J. Oper. Res. 233, 299–312 (2014)Ocampo, L.A., Abad, G.K.M., Cabusas, K.G.L., Padon, M.L.A., Sevilla, N.C.: Recent approaches to supplier selection: a review of literature within 2006–2016. Int. J. Integr. Supply Manage. 12, 22–68 (2018)Valipour, S., Safaei, A.: A resilience approach for supplier selection: using Fuzzy analytic network process and grey VIKOR techniques. J. Clean. Prod. 161, 431–451 (2017)Amindoust, A.: A resilient-sustainable based supplier selection model using a hybrid intelligent method. Comput. Ind. Eng. 126, 122–135 (2018)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Villalobos, J.R., Soto-Silva, W.E., González-Araya, M.C., González-Ramirez, R.G.: Research directions in technology development to support real-time decisions of fresh produce logistics: A review and research agenda. Comput. Electron. Agric. 167, 105092 (2019)Ristono, A., Santoso, P.B., Tama, I.P.: A literature review of design of criteria for supplier selection. J. Ind. Eng. Manage. 11, 680–696 (2018)Torres-Ruiz, A., Ravindran, A.R.: Multiple criteria framework for the sustainability risk assessment of a supplier portfolio. J. Clean. Prod. 172, 4478–4493 (2018)Setak, M., Sharifi, S., Alimohammadian, A.: Supplier selection and order allocation models in supply chain management: a review. World Appl. Sci. J. 18, 55–72 (2012)Ravindran, A.R., Warsing, D.P.: Supplier selection models and methods. In: Supply Chain Engineering: Models and Applications. Taylor and Francis Group, Boca Raton, Florida (2013)De Boer, L., Labro, E., Morlacchi, P.: A review of methods supporting supplier selection. Eur. J. Purch. Supply Manage. 7, 75–89 (2011)De Felice, F., Deldoost, M.H., Faizollahi, M., Petrillo, A.: Performance measurement model for the supplier selection based on AHP. Int. J. Eng. Bus. Manag. 7, 1–13 (2015)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2014)Ali, A., Mahfouz, A., Arisha, A.: Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Manage. 22, 16–39 (2017)Verdecho, M., Alarcón-Valero, F., Pérez-Perales, D., et al.: A methodology to select suppliers to increase sustainability within supply chains. Cent. Eur. J. Oper. Res. (2020). https://doi.org/10.1007/s10100-019-00668-3Rabelo, L., Bhide, S., Gutierrez, E.: Artificial Intelligence: Advances in Research and Applications. Nova Science Publishers, Inc., Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States (2017)Denyer, D., Tranfield, D.: Producing a systematic review. In: The Sage Handbook of Organizational Research Methods. SAGE Publications Ltd., pp. 671–689 (2019)Chen, Y.-J.: Structured methodology for supplier selection and evaluation in a supply chain. Inf. Sci. (Ny) 181, 1651–1670 (2011)Hamdi, F., Ghorbel, A., Masmoudi, F., Dupont, L.: Optimization of a supply portfolio in the context of supply chain risk management: literature review. J. Intell. Manuf. 29(4), 763–788 (2015). https://doi.org/10.1007/s10845-015-1128-3Kumar, V., Srinivasan, S., Das, S.: Optimal solution for supplier selection based on SMART fuzzy case base approach. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems. SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems. ISIS 2014, Institute of Electrical and Electronics Engineers Inc., Department of Computer Science, IISJ Yokohama, Tokai Chiba, Japan, pp. 386–391 (2014)Jahani, A., Murad, M.A.A., bin Sulaiman, M.N., Selamat, M.H.: An agent-based supplier selection framework: Fuzzy case-based reasoning perspective. Strateg. Outsourcing 8, 180–205 (2015)Wang, Q.: Hybrid knowledge-based flexible supplier selection. In: 8th International Conference on Management of e-Commerce and e-Government. ICMeCG 2014. Institute of Electrical and Electronics Engineers Inc., Department of Information Management, Shanghai Finance University, Shanghai, China, pp. 235–239 (2014)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124, 252–264 (2010)Guo, F., Lu, Q.: Partner selection optimization model of agricultural enterprises in supply chain. Adv. J. Food Sci. Technol. 5, 1285–1291 (2013)Azadnia, A.H., Saman, M.Z.M., Wong, K.Y.: Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process. Int. J. Prod. Res. 53, 383–408 (2015)Miranda-Ackerman, M.A., Azzaro-Pantel, C., Aguilar-Lasserre, A.A.: A green supply chain network design framework for the processed food industry: application to the orange juice agrofood cluster. Comput. Ind. Eng. 109, 369–389 (2017)Hajikhani, A., Khalilzadeh, M., Sadjadi, S.J.: A fuzzy multi-objective multi-product supplier selection and order-allocation problem in supply chain under coverage and price considerations: an urban agricultural case study. Sci. Iran. 25, 431–449 (2018)Zhang, H., Cui, Y.: A model combining a Bayesian network with a modified genetic algorithm for green supplier selection. Simulation 95, 1165–1183 (2019)Yadav, S., Garg, D., Luthra, S.: Selection of third-party logistics services for internet of things-based agriculture supply chain management. Int. J. Logist. Syst. Manage. 35, 204–230 (2020)Yazdani, M., Wang, Z.X., Chan, F.T.S.: A decision support model based on the combined structure of DEMATEL, QFD and fuzzy values. Soft. Comput. 24(16), 12449–12468 (2020). https://doi.org/10.1007/s00500-020-04685-2Zhang, H., Feng, H., Cui, Y., Wang, Y.: A fuzzy Bayesian network model for quality control in O2O e-commerce. Int. J. Comput. Commun. Control 15(1), (2020). article number 1003. https://doi.org/10.15837/ijccc.2020.1.3783Amiri, S.A.H.S., Zahedi, A., Kazemi, M., Soroor, J., Hajiaghaei-Keshteli, M.: Determination of the optimal sales level of perishable goods in a two-echelon supply chain network. Comput. Ind. Eng. 139, 106156 (2020)Roy, S., et al.: A framework for sustainable supplier selection with transportation criteria. Int. J. Sustain. Eng. 13(2), 77–92 (2020)Parkouhi, S.V., Ghadikolaei, A.S., Lajimi, H.F.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Camarinha-Matos, L.M., Afsarmanesh, H., Galeano, N., Molina, A.: Collaborative networked organizations – concepts and practice in manufacturing enterprises. Comput. Ind. Eng. 57, 46–60 (2009)Lezoche, M., Panetto, H., Kacprzyk, J., Hernandez, J., Díaz, M.A.: Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117, 103187 (2020)Alikhani, R., Torabi, S., Altay, N.: Strategic supplier selection under sustainability and risk criteria. Int. J. Prod. Econ. 208, 69–82 (2019

Preparation instructions micro-algae cultivate and concentrate it for use in silver carp feed and training necessary

According to the importance of micro-algae in aquatic feed such as fish in this study have been investigated Preparation of useful algae powder or concentrate for breeding silver carp , their impact on the growth of silver carp , rate per unit area and estimate n economic Development. Different algal species were isolated from hydrothermal fish farms, then were purified and mass culture . The next step microalgae were dried and powdered by spray dryer and were examined the fish feeding on them. During this study, 6 species of chlorophyt( Scenedesmus obliquus, Scenedesmus acuminatus, Chlorella vulgaris, Pediastrum boryanum, Pandorina morum, Ankistrodesmus falcatus) ,3 species of cyanophyta ( Anabaena flosaquae, Oscillatoria agardhi and Spirulina platensis) and 1 species of Bacillariophta ( Cyclotella meneghiniana were isolated from.Green algae and Blue -green algae were cultured in Zaindr medium, diatoms were cultured in Zaindr medium but with water of Anzali logoon and also in F2 medium with artificial sea water and spirulina was cultured in Zarouk medium. Microalgae were cultures then concentrated.Then the impact was examined on fish silver carp 2 to 3 grams. The results showed that Cyclotella has a greater role in the growth of silver carp and Anabaena floes aquae and Spirulina platensis tend to growth less than cyclotella. Scenedesmus obliquus and Scenedesmus acuminatus were respectively next algae that showed the greatest impact on fish growth. Scenedesmus obliquus feed rate was greater than any other algae for Daphnia

Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events

[EN] Agri-food supply chains (AFSCs) are very vulnerable to high risks such as pandemics, causing economic and social impacts mainly on the most vulnerable population. Thus, it is a priority to implement resilient strategies that enable AFSCs to resist, respond and adapt to new market challenges. At the same time, implementing resilient strategies impact on the social, economic and environmental dimensions of sustainability. The objective of this paper is twofold: analyze resilient strategies on AFSCs in the literature and identify how these resilient strategies applied in the face of high risks affect the achievement of sustainability dimensions. The analysis of the articles is carried out in three points: consequences faced by agri-food supply chains due to high risks, strategies applicable in AFSCs, and relationship between resilient strategies and the achievement of sustainability dimensions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events. IFIP Advances in Information and Communication Technology. 598:560-570. https://doi.org/10.1007/978-3-030-62412-5_46S560570598Gray, R.: Agriculture, transportation, and the COVID-19 crisis. Can. J. Agric. Econ. 68, 239–243 (2020)Queiroz, M.M., Ivanov, D., Dolgui, A., Fosso Wamba, S.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03685-7Hobbs, J.: Food supply chains during the COVID-19 pandemic. Can. J. Agric. Econ. 68, 171–176 (2020)Shashi, P., Centobelli, P., Cerchione, R., Ertz, M.: Managing supply chain resilience to pursue business and environmental strategies. Bus. Strateg. Environ. 29(3), 1215–1246 (2019)Ivanov, D.: Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E Logist. Transp. Rev. 136, 101922 (2020)Mamani, H., Chick, S.E., Simchi-Levi, D.: A game-theoretic model of international influenza vaccination coordination. Manage. Sci. 59(7), 1650–1670 (2013)Liu, M., Zhang, D.: A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating. J. Oper. Res. Soc. 67, 841–852 (2016)Hessel, L.: Pandemic influenza vaccines: meeting the supply, distribution and deployment challenges. Influenza Other Respir. Viruses 3, 165–170 (2009)Orenstein, W., Schaffner, W.: Lessons learned: role of influenza vaccine production, distribution, supply, and demand—what it means for the provider. Am. J. Med. 121, S22–S27 (2008)Büyüktahtakın, I., Des-Bordes, E., Kıbış, E.: A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa. Eur. J. Oper. Res. 26, 1046–1063 (2018)Anparasan, A., Lejeune, M.: Analyzing the response to epidemics: concept of evidence-based Haddon matrix. J. Humanit. Logist. Supply Chain Manag. 7, 266–283 (2017)Anparasan, A.A., Lejeune, M.A.: Data laboratory for supply chain response models during epidemic outbreaks. Ann. Oper. Res. 270, 53–64 (2018). https://doi.org/10.1007/s10479-017-2462-yAnparasan, A., Lejeune, M.: Resource deployment and donation allocation for epidemic outbreaks. Ann. Oper. Res. 283, 9–32 (2019). https://doi.org/10.1007/s10479-016-2392-0Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58, 2904–2915 (2020)Ivanov, D.: Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03640-6Ekici, A., Keskinocak, P., Swann, J.: Modeling influenza pandemic and planning food distribution. Manuf. Serv. Oper. Manag. 16, 11–27 (2014)Miranda, R., Schaffner, D.: Virus risk in the food supply chain. Curr. Op. Food Sci. 30, 43–48 (2019)Magalhães, A., Rossi, A., Zattar, I., Marques, M., Seleme, R.: Food traceability technologies and foodborne outbreak occurrences. Br. Food J. 121, 3362–3379 (2019)Denyer, D., Tranfield, D.: Producing a systematic review. In: Buchanan, D., Bryman, A. (eds.) The Sage Handbook of Organizational Research Methods, pp. 671–689. SAGE Publications Ltd., London (2009)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2004)Dolgui, A., Ivanov, D., Sokolov, B.: Ripple effect in the supply chain: an analysis and recent literature. Int. J. Prod. Res. 56, 414–430 (2018)Jüttner, U., Peck, H., Christopher, M.: Supply chain risk management: outlining an agenda for future research. Int. J. Logist. Res. 6, 197–210 (2003)Behzadi, G., O’Sullivan, M., Olsen, T., Zhang, A.: Agribusiness supply chain risk management: a review of quantitative decision models. Omega (United Kingdom) 79, 21–42 (2018)Kleindorfer, P., Saad, G.: Managing disruption risks in supply chains. Pr. Op. Man. 14, 53–68 (2005)Vishnu, C., Sridharan, R., Gunasekaran, A., Ram Kumar, P.: Strategic capabilities for managing risks in supply chains: current state and research futurities. J. Adv. Manag. Res. 17(2), 173–211 (2019)Deaton, B., Deaton, B.: Food security and Canada’s agricultural system challenged by COVID-19. Can. J. Agric. Econ. 68(2), 143–149 (2020)Richards, T., Rickard, B.: COVID-19 impact on fruit and vegetable markets. C. J. Ag. Ec. 68(2), 189–194 (2020)Larue, B.: Labor issues and COVID-19. Can. J. Agric. Econ. Can. d’agroeconomie (2020). https://doi.org/10.1111/cjag.12233Hollnagel, E.: Epilogue: RAG: the resilience analysis grid. In: Hollnagel, E., Paries, J., Woods, D., Wreathall, J. (eds.) Resilience Engineering in Practice: A Guidebook. Ashgate Pr., pp. 275–296 (2011)Ponomarov, S., Holcomb, M.: Understanding the concept of supply chain resilience. Int. J. Logist. Manag. 20, 124–143 (2009)Wu, T., Huang, S., Blackhurst, J., Zhang, X., Wang, S.: Supply chain risk management: an agent-based simulation to study the impact of retail stockouts. IEEE Trans. Eng. Manag. 60, 676–686 (2013)Schmitt, A., Singh, M.: A quantitative analysis of disruption risk in a multi-echelon supply chain. Int. J. Prod. Econ. 139, 22–32 (2012)Vroegindewey, R., Hodbod, J.: Resilience of agricultural value chains in developing country contexts: a framework and assessment approach. Sustainability 10, 916 (2018)Behzadi, G., O’Sullivan, M., Olsen, T., Scrimgeour, F., Zhang, A.: Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. Int. J. Prod. Econ. 191, 207–220 (2017)Bottani, E., Murino, T., Schiavo, M., Akkerman, R.: Resilient food supply chain design: modelling framework and metaheuristic solution approach. Comput. Ind. Eng. 135, 177–198 (2019)Meuwissen, M., et al.: A framework to assess the resilience of farming systems. Agric. Syst. 176, 102656 (2019)Dutta, P., Shrivastava, H.: The design and planning of an integrated supply chain for perishable products under uncertainties: a case study in milk industry. J. Model. Manag. (2020). https://doi.org/10.1108/JM2-03-2019-0071Aboah, J., Wilson, M., Rich, M., Lyne, M.: Operationalising resilience in tropical agricultural value chains. Supply Chain Manag. 24, 271–300 (2019)Ravulakollu, A., Urciuoli, L., Rukanova, B., Tan, Y., Hakvoort, R.: Risk based framework for assessing resilience in a complex multi-actor supply chain domain. Supply Chain Forum 19, 266–281 (2018)Das, K.: Integrating lean, green, and resilience criteria in designing a sustainable food supply chain. Proc. Int. Conf. Ind. Eng. Oper. Manag. 2018, 462–473 (2018)Zhu, Q., Krikke, H.: Managing a sustainable and resilient perishable food supply chain (PFSC) after an outbreak. Sustainability 12, 5004 (2020)Rozhkov, M., Ivanov, D.: Contingency production-inventory control policy for capacity disruptions in the retail supply chain with perishable products. IFAC-PapersOnLine 51, 1448–1452 (2018)Yavari, M., Zaker, H.: Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Comput. Chem. Eng. 134, 106680 (2020)Ye, F., Hou, G., Li, Y., Fu, S.: Managing bioethanol supply chain resiliency: a risk-sharing model to mitigate yield uncertainty risk. Ind. Manag. Data Syst. 118, 1510–1527 (2018)Jabbarzadeh, A., Fahimnia, B., Sheu, J., Moghadam, H.: Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp. Res. Part B Methodol. 94, 121–149 (2016)O’Leary, D.: Evolving information systems and technology research issues for COVID-19 and other pandemics. J. Organ. Comput. Electron. Commer. 30, 1–8 (2020)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Fahimni, B., Jabbarzadeh, A.: Marrying supply chain sustainability and resilience: a match made in heaven. Transp. Res. Part E Logist. Transp. Rev. 91, 306–324 (2016)Verdecho, M.-J., Alarcón-Valero, F., Pérez-Perales, D., Alfaro-Saiz, J.-J., Rodríguez-Rodríguez, R.: A methodology to select suppliers to increase sustainability within supply chains. CEJOR (2020). https://doi.org/10.1007/s10100-019-00668-3Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124(1), 252–264 (2010)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Valipour, S., Safaei, A., Fallah, H.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Yang, S., Xiao, Y., Kuo, Y.: The supply chain design for perishable food with stochastic demand. Sustainability 9, 1195 (2017)Zahiri, B., Zhuang, J., Mohammadi, M.: Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp. Res. Part E Logist. Transp. Rev. 103, 109–142 (2017)Duong, L., Chong, J.: Supply chain collaboration in the presence of disruptions: a literature review. Int. J. Prod. Res. 58, 3488–3507 (2020

Analyzing Stakeholder Water Source Preference Based upon Social Capital: a Case Study of the Fajr Jam Gas Refinery in Iran

Gas refineries are among the most water-intensive industries in the world. The Fajr Jam gas refinery is one such example, located in the southern Iran. The indiscriminate use of aquifer resources for this highly profitable industry creates tragedy of the commons effects, causing significant environmental controversy and threatening the long-term water security of the region. It behooves decision makers, therefore, to examine a broad range of adaptive water management strategies for this industry. The implementation of such strategies requires understanding the preferences and potential conflicts that may emerge among competing stakeholder interests. This quantitative social scientific study examines stakeholder preferences among water management options through the lens of social capital. Elite stakeholder representatives (including agricultural organizations, governmental organizations, the Water, and Power Authority, Department of Health, Bureau of Water and Wastewater) were canvassed through a survey instrument using paired comparisons. Data were analyzed using Expert Choice software and an analytic hierarchy process technique. The results show that accountability is the main criterion for selecting the best water sources and ranked first with the Eigenvector 0.62. Also, the results show that the least important criterion was social cohesion with the Eigenvalue 0.033. The criteria of partnership and trust ranked as two and three with Eigenvalues 0.215 and 0.133, respectively. The results indicate that the construction of salt water transmission from the sea (A = 0.240) is the preferred option among other alternatives, and this is confirmed by sensitivity analysis

Positive End-Expiratory Pressure may alter breathing cardiovascular variability and baroreflex gain in mechanically ventilated patients

<p>Abstract</p> <p>Background</p> <p>Baroreflex allows to reduce sudden rises or falls of arterial pressure through parallel RR interval fluctuations induced by autonomic nervous system. During spontaneous breathing, the application of positive end-expiratory pressure (PEEP) may affect the autonomic nervous system, as suggested by changes in baroreflex efficiency and RR variability. During mechanical ventilation, some patients have stable cardiorespiratory phase difference and high-frequency amplitude of RR variability (HF-RR amplitude) over time and others do not. Our first hypothesis was that a steady pattern could be associated with reduced baroreflex sensitivity and HF-RR amplitude, reflecting a blunted autonomic nervous function. Our second hypothesis was that PEEP, widely used in critical care patients, could affect their autonomic function, promoting both steady pattern and reduced baroreflex sensitivity.</p> <p>Methods</p> <p>We tested the effect of increasing PEEP from 5 to 10 cm H2O on the breathing variability of arterial pressure and RR intervals, and on the baroreflex. Invasive arterial pressure, ECG and ventilatory flow were recorded in 23 mechanically ventilated patients during 15 minutes for both PEEP levels. HF amplitude of RR and systolic blood pressure (SBP) time series and HF phase differences between RR, SBP and ventilatory signals were continuously computed by complex demodulation. Cross-spectral analysis was used to assess the coherence and gain functions between RR and SBP, yielding baroreflex-sensitivity indices.</p> <p>Results</p> <p>At PEEP 10, the 12 patients with a stable pattern had lower baroreflex gain and HF-RR amplitude of variability than the 11 other patients. Increasing PEEP was generally associated with a decreased baroreflex gain and a greater stability of HF-RR amplitude and cardiorespiratory phase difference. Four patients who exhibited a variable pattern at PEEP 5 became stable at PEEP 10. At PEEP 10, a stable pattern was associated with higher organ failure score and catecholamine dosage.</p> <p>Conclusions</p> <p>During mechanical ventilation, stable HF-RR amplitude and cardiorespiratory phase difference over time reflect a blunted autonomic nervous function which might worsen as PEEP increases.</p