Investigating the Effect of Zinc Chloride to Control External Bleeding in Rats

Background: Despite all progresses in surgical science, bleeding caused by traffic accidents is still a challenge for surgeons to save patients’ lives. Therefore, introducing an effective method to control external bleeding is an important research priority. Objectives: This study aimed to compare haemostatic effect of zinc chloride and simple suturing to control external bleeding. Materials and Methods: In this animal model study, 60 male Wistar rats were used. An incision (two cm in length and half a cm in depth) was made on shaved back of rats. The hemostasis time was measured once using zinc chloride with different concentrations (5%, 10%, 15%, 25%, and 50%) and then using simple suturing. Skin tissue was assessed for pathological changes. Due to abnormal distribution of variables in Kolmogorov-Smirnov test, the data was analyzed using Kruskal-Wallis test Mann-Whitney U tests. Results: In all the groups, complete hemostasis occurred. Hemostasis times of different concentrations of zinc chloride were significantly less than that of the control group (P < 0.001). Conclusions: Zinc chloride was effective to control external bleeding in rats

Comparative study on some physiologic, biometrics, nutritional value and molecular characteristics of Mighan Lake’s Artemia (Arak)

Due to the importance of identifying the major characteristics of Artemia populations, in this study some physiologic, biometric, nutritional and genetic characteristics of one Artemia population from Iran named Arak’s Artemia (Mighan Lake) was studied. The hatched larvae of Artemia were reared in the saline water of 80 g/l with standards method in which percentage of survival and growth were evaluated on days 3, 7, 11 and 15 of culture period. In order to study the morphometric characteristics of Artemia, diameter of full cysts as well as 11 more morphological parameters of adult Artemia were measured. The fatty acid profile was analyzed by gas chromatography. The Genetic characteristics were compared with other Artemia populations by sequencing after PCR amplification of Hsp 26 gene. According to the results, the diameter of cysts and nauplii instar were 276.28 and 544.66 micron, respectively. The growth and survival of brine shrimp Artemia, in comparison with other populations, reflected good growth and survival of this population. The results of fatty acids profile also showed higher amounts of polyunsaturated fatty acids in this Artemia compared to other populations cultured under identical conditions. The morphometric characteristics and genetic study of Hsp 26 gene showed great affinity of this population with the parthenogenetic brine shrimp Artemia. However, individual differences could be used to characterize this population

Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning

In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different backgrounds and geographical locations. Renowned universities and MOOC-specific providers, such as Coursera, offer MOOC courses on various subjects. Automated assessment tasks like grade and early dropout predictions are necessary due to the high enrollment and limited direct interaction between teachers and learners. However, current automated assessment approaches overlook the structural links between different entities involved in the downstream tasks, such as the students and courses. Our hypothesis suggests that these structural relationships, manifested through an interaction graph, contain valuable information that can enhance the performance of the task at hand. To validate this, we construct a unique knowledge graph for a large MOOC dataset, which will be publicly available to the research community. Furthermore, we utilize graph embedding techniques to extract latent structural information encoded in the interactions between entities in the dataset. These techniques do not require ground truth labels and can be utilized for various tasks. Finally, by combining entity-specific features, behavioral features, and extracted structural features, we enhance the performance of predictive machine learning models in student assignment grade prediction. Our experiments demonstrate that structural features can significantly improve the predictive performance of downstream assessment tasks. The code and data are available in \url{https://github.com/DSAatUSU/MOOPer_grade_prediction

CLASSIFICATION OF RICE GRAIN VARIETIES USING TWO ARTIFICIAL NEURAL NETWORKS (MLP AND NEURO-FUZZY)

ABSTRACT Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively

A study on oogenesis of Liza saliens

In this study, gametogenesis of sharp-nose mullet (Liza saliens) was investigated bassed on morphological and histological characteristics. For this purpose, about 150 specimen of this species were collected from beach-seine nets in the southern Caspian Sea and were transferred to the laboratory for futher examinations on gonads. The gonad development was classified to 6 different stages consist of: stage I: The oocytes small, colourless with a big nucleus. stage II: The low increase in size of oocytes; globular yolk of nucleus appearing. stage III: Blood vessels are appearing on the ovary; the oocytes are in the primary vitellogenesis stages; vacules and zona radiata are seen around them. stage IV: The ovules can be easily observed with naked-eyes, vitellogenesis are complete and oocytes are mature. stage V: Oocytes are in maximum growth, nucleus migrating towards animal pole; spawning occures in this stage. stage VI: This stage is after spawning, ovary contains empty follicles. Based on this study, the stage of I to III were observed in August to April; stage of IV in May and June; stage of V in June and July and stage VI in August

When Machine Learning Meets Privacy

The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.</jats:p

Prophylactic Fibrinogen Decreases Postoperative Bleeding but Not Acute Kidney Injury in Patients Undergoing Heart Transplantation

The present study is the premier clinical attempt to scrutinize the practicability of prophylactic fibrinogen infusion in patients undergoing heart transplantation (HT). A total of 67 consecutive patients who had undergone HT between January 2012 and December 2014 were assessed. After exclusion of some patients, 23 patients were given preoperative 2 g fibrinogen concentrate over a period of 15 minutes after the termination of cardiopulmonary bypass pump and complete reversal of heparin, and 30 patients were not given. Some laboratories were measured before general anesthesia and at 6 and 24 hours after surgery. In addition, major adverse events were also evaluated during hospitalization. The mean age of the patients was 39.5 ± 11.4 years, with a predominance of male sex (77.4). All laboratories at baseline were comparable between groups. The length of hospital stay was longer in the control group compared to the fibrinogen group (20 16-22 vs 16 12-19 days; P =.005). There was a trend for patients in the fibrinogen group to have more acute kidney injury (AKI) after surgery (10% vs 30.4%) and less reoperation for bleeding (20% vs 8.7%). The amount of postoperative bleeding was significantly higher in the control group compared to the fibrinogen group (P <.001). The number of packed red blood cell transfused during 24 hours after surgery was significantly lower in the fibrinogen group (P <.001). The transfusion of fibrinogen in patients undergoing HT may be associated with reductions in postoperative bleeding, the number of packed red blood cells, and hospital length of stay; however, it may enhance postoperative AKI. © The Author(s) 2017

Towards energy aware cloud computing application construction

The energy consumption of cloud computing continues to be an area of significant concern as data center growth continues to increase. This paper reports on an energy efficient interoperable cloud architecture realised as a cloud toolbox that focuses on reducing the energy consumption of cloud applications holistically across all deployment models. The architecture supports energy efficiency at service construction, deployment and operation. We discuss our practical experience during implementation of an architectural component, the Virtual Machine Image Constructor (VMIC), required to facilitate construction of energy aware cloud applications. We carry out a performance evaluation of the component on a cloud testbed. The results show the performance of Virtual Machine construction, primarily limited by available I/O, to be adequate for agile, energy aware software development. We conclude that the implementation of the VMIC is feasible, incurs minimal performance overhead comparatively to the time taken by other aspects of the cloud application construction life-cycle, and make recommendations on enhancing its performance

A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study

[EN] Performance evaluation is relevant for supporting managerial decisions related to the improvement of public emergency departments (EDs). As different criteria from ED context and several alternatives need to be considered, selecting a suitable Multicriteria Decision-Making (MCDM) approach has become a crucial step for ED performance evaluation. Although some methodologies have been proposed to address this challenge, a more complete approach is still lacking. This paper bridges this gap by integrating three potent MCDM methods. First, the Fuzzy Analytic Hierarchy Process (FAHP) is used to determine the criteria and sub-criteria weights under uncertainty, followed by the interdependence evaluation via fuzzy Decision-Making Trial and Evaluation Laboratory(FDEMATEL). The fuzzy logic is merged with AHP and DEMATEL to illustrate vague judgments. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used for ranking EDs. This approach is validated in a real 3-ED cluster. The results revealed the critical role of Infrastructure (21.5%) in ED performance and the interactive nature of Patient safety (C+R =12.771). Furthermore, this paper evidences the weaknesses to be tackled for upgrading the performance of each ED.Ortiz-Barrios, M.; Alfaro Saiz, JJ. (2020). A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study. International Journal of Information Technology & Decision Making. 19(6):1485-1548. https://doi.org/10.1142/S0219622020500364S14851548196Lord, K., Parwani, V., Ulrich, A., Finn, E. B., Rothenberg, C., Emerson, B., … Venkatesh, A. K. (2018). Emergency department boarding and adverse hospitalization outcomes among patients admitted to a general medical service. The American Journal of Emergency Medicine, 36(7), 1246-1248. doi:10.1016/j.ajem.2018.03.043Sørup, C. M., Jacobsen, P., & Forberg, J. L. (2013). 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K., Govindan, K., Antucheviciene, J., & Turskis, Z. (2016). Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues. Economic Research-Ekonomska Istraživanja, 29(1), 857-887. doi:10.1080/1331677x.2016.1237302Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Butturi, M. A., Marinello, S., & Rimini, B. (2019). On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application. Expert Systems with Applications, 120, 217-227. doi:10.1016/j.eswa.2018.11.030De Almeida Filho, A. T., Clemente, T. R. N., Morais, D. C., & de Almeida, A. T. (2018). Preference modeling experiments with surrogate weighting procedures for the PROMETHEE method. European Journal of Operational Research, 264(2), 453-461. doi:10.1016/j.ejor.2017.08.006Sun, G., Guan, X., Yi, X., & Zhou, Z. (2018). An innovative TOPSIS approach based on hesitant fuzzy correlation coefficient and its applications. Applied Soft Computing, 68, 249-267. doi:10.1016/j.asoc.2018.04.004Frazão, T. D. C., Camilo, D. G. G., Cabral, E. L. S., & Souza, R. P. (2018). Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps. BMC Medical Informatics and Decision Making, 18(1). doi:10.1186/s12911-018-0663-1Ortiz-Barrios, M. A., Herrera-Fontalvo, Z., Rúa-Muñoz, J., Ojeda-Gutiérrez, S., De Felice, F., & Petrillo, A. (2018). An integrated approach to evaluate the risk of adverse events in hospital sector. Management Decision, 56(10), 2187-2224. doi:10.1108/md-09-2017-0917Al Salem, A. A., & Awasthi, A. (2018). Investigating rank reversal in reciprocal fuzzy preference relation based on additive consistency: Causes and solutions. Computers & Industrial Engineering, 115, 573-581. doi:10.1016/j.cie.2017.11.027Aires, R. F. de F., & Ferreira, L. (2019). A new approach to avoid rank reversal cases in the TOPSIS method. Computers & Industrial Engineering, 132, 84-97. doi:10.1016/j.cie.2019.04.023Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8. doi:10.1016/j.seps.2017.01.008Arya, A., & Yadav, S. P. (2017). Development of FDEA Models to Measure the Performance Efficiencies of DMUs. International Journal of Fuzzy Systems, 20(1), 163-173. doi:10.1007/s40815-017-0325-yMufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438. doi:10.1016/j.cie.2018.03.045Kaliszewski, I., & Podkopaev, D. (2016). Simple additive weighting—A metamodel for multiple criteria decision analysis methods. Expert Systems with Applications, 54, 155-161. doi:10.1016/j.eswa.2016.01.042Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, 466-484. doi:10.1016/j.jclepro.2018.02.062Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508. doi:10.1016/j.jclepro.2019.04.145Jumaah, F. M., Zadain, A. A., Zaidan, B. B., Hamzah, A. K., & Bahbibi, R. (2018). Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement, 118, 83-95. doi:10.1016/j.measurement.2018.01.011Singh, A., & Prasher, A. (2017). Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. 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Multi-criteria decision analysis for supporting the selection of medical devices under uncertainty. European Journal of Operational Research, 247(1), 216-228. doi:10.1016/j.ejor.2015.05.075Kovacs, E., Strobl, R., Phillips, A., Stephan, A.-J., Müller, M., Gensichen, J., & Grill, E. (2018). Systematic Review and Meta-analysis of the Effectiveness of Implementation Strategies for Non-communicable Disease Guidelines in Primary Health Care. Journal of General Internal Medicine, 33(7), 1142-1154. doi:10.1007/s11606-018-4435-5Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018). Emergency department crowding: A systematic review of causes, consequences and solutions. PLOS ONE, 13(8), e0203316. doi:10.1371/journal.pone.0203316Hermann, R. M., Long, E., & Trotta, R. L. (2019). Improving Patients’ Experiences Communicating With Nurses and Providers in the Emergency Department. Journal of Emergency Nursing, 45(5), 523-530. doi:10.1016/j.jen.2018.12.001Hawley, K. L., Mazer-Amirshahi, M., Zocchi, M. S., Fox, E. R., & Pines, J. M. (2015). Longitudinal Trends in U.S. Drug Shortages for Medications Used in Emergency Departments (2001-2014). Academic Emergency Medicine, 23(1), 63-69. doi:10.1111/acem.12838Stang, A. S., Crotts, J., Johnson, D. W., Hartling, L., & Guttmann, A. (2015). Crowding Measures Associated With the Quality of Emergency Department Care: A Systematic Review. Academic Emergency Medicine, 22(6), 643-656. doi:10.1111/acem.12682Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441-453. doi:10.1016/j.asoc.2016.01.007Farup, P. G. (2015). Are measurements of patient safety culture and adverse events valid and reliable? Results from a cross sectional study. BMC Health Services Research, 15(1). doi:10.1186/s12913-015-0852-xCarter, E. J., Pouch, S. M., & Larson, E. L. (2013). 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A., Walker, C., Lahiri, S., Jenkinson, D., Adekanmbi, V., Robertson, W., & Clarke, A. (2018). General practitioners providing non-urgent care in emergency department: a natural experiment. BMJ Open, 8(5), e019736. doi:10.1136/bmjopen-2017-019736Razzak, J. A., Baqir, S. M., Khan, U. R., Heller, D., Bhatti, J., & Hyder, A. A. (2013). Emergency and trauma care in Pakistan: a cross-sectional study of healthcare levels. Emergency Medicine Journal, 32(3), 207-213. doi:10.1136/emermed-2013-202590Dart, R. C., Goldfrank, L. R., Erstad, B. L., Huang, D. T., Todd, K. H., Weitz, J., … Anderson, V. E. (2018). Expert Consensus Guidelines for Stocking of Antidotes in Hospitals That Provide Emergency Care. Annals of Emergency Medicine, 71(3), 314-325.e1. doi:10.1016/j.annemergmed.2017.05.021Mkoka, D. A., Goicolea, I., Kiwara, A., Mwangu, M., & Hurtig, A.-K. (2014). Availability of drugs and medical supplies for emergency obstetric care: experience of health facility managers in a rural District of Tanzania. BMC Pregnancy and Childbirth, 14(1). doi:10.1186/1471-2393-14-108Beck, M. J., Okerblom, D., Kumar, A., Bandyopadhyay, S., & Scalzi, L. V. (2016). Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hospital Practice, 44(5), 252-259. doi:10.1080/21548331.2016.1254559Morais Oliveira, M., Marti, C., Ramlawi, M., Sarasin, F. P., Grosgurin, O., Poletti, P.-A., … Rutschmann, O. T. (2018). Impact of a patient-flow physician coordinator on waiting times and length of stay in an emergency department: A before-after cohort study. PLOS ONE, 13(12), e0209035. doi:10.1371/journal.pone.0209035Vermeulen, M. J., Stukel, T. A., Boozary, A. S., Guttmann, A., & Schull, M. J. (2016). 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