HSE Management System Criteria Ranking to Evaluate Contractors Prequalification Using the Analytical Hierarchy Process in the Fifth Refinery of South Pars Gas Complex

According to the International Association of Oil and Gas Producers (OGP) statistics, the number of oil and gas industries contractors has had a rising trend; on the other hand, there has been a considerable increase in both the frequency of the time wasted due to the injuries and the risk exposure ratio, based on the contractors statistics. South Pars Gas Complex (SPGC), like other large companies, uses the capabilities of numerous organizations as contractors to execute its programs. Based on the studies and internal audits, these contractors only few of which hold the regulated and modern system of the health, safety and environment (HSE) are responsible for approximately 85% of the incidents occurred in SPGC. The lack of adequacy and efficiency of the criteria presented in contractors HSE prequalification checklist prior to signing the contract as well as improperly prioritizing these criteria regarding the compan

Identificación y clasificación de estrategias de implementación de recursos humanos Gestión de la mejora de la productividad en IRAN Insurance Company

Mejorar la calidad de la fuerza laboral, adquirir nuevas habilidades e identificar los factores que influyen en la productividad de los recursos humanos ayudará a la organización a luchar por su mejora. La implementación de estrategias de gestión para mejorar la productividad permite promover la productividad como un proceso continuo, identificando rutas de productividad y sentando las bases necesarias. Este estudio se realizó para identificar y clasificar las estrategias de mejora de la gestión de recursos humanos en la Compañía de Seguros de Irán, con indicadores efectivos e impresionables. Para hacer esto, se utilizaron dos métodos de muestreo no probabilístico y de bola de nieve, las opiniones de 15 expertos de Iran Insurance Company se consideraron como la red de suministro de seguros más grande del país. El proceso de esta investigación consistió en cinco pasos: a) Identificar los factores que influyen en la productividad de los recursos humanos mediante el estudio de la literatura relevante, b) Selección de los factores identificados utilizando la técnica Fuzzy Delphi en la Compañía de Seguros de Irán, c) Determinar las interacciones y priorizar los factores que influyen en la productividad de los recursos humanos utilizando Fuzzy Demetel; d) Determinar estrategias de despliegue para mejorar la productividad a través de entrevistas en profundidad con expertos; y e) Clasificación de estrategias de implementación de productividad utilizando el método Fuzzy Victor. Los resultados mostraron que el factor de satisfacción laboral es el más efectivo y también la más alta prioridad entre los 15 factores que influyen en la productividad de los recursos humanos en Iran Insurance Company. Los resultados también mostraron que el establecimiento del sistema de investigación de productividad se clasificó como el más alto entre las 7 estrategias de investigación identificada

Investigating the Possibility of Autumn-Sown and Determining the Most Suitable Planting Date and the Best Bolt-Resistant Cultivar of Sugar Beet in Khorasan Region

IntroductionSugar beet (Beta vulgaris L.) is the second most important sugar crop after sugarcane, which annually produces about 40% of total sugar production worldwide and is adapted to different climatic conditions (El-Hag et al., 2015). Due to global warming, autumn cultivation of sugar beet is predicted to become more priority in the future, but autumn cultivation is in danger of bolting and flowering in many areas. Excessive bolting reduces sugar content, root yield, and purity of raw syrup. In general, both early sowing and delayed sowing reduce root yield, sugar, and leaf area index and increase the percentage of impurities. Therefore, this experiment was designed and implemented with the aim of feasibility study of autumn cultivation of sugar beet and determination of the best planting date in North, Razavi, and South Khorasan provinces for three new varieties resistant to sugar beet.Materials and MethodsThe experiment was conducted as a split-plot design based on a randomized complete block design with three replications in the provinces of North Khorasan (Shirvan), Khorasan Razavi (Mashhad), and South Khorasan  (Khezri Dasht-e Bayaz) in 2019-2020. The main plots included three planting dates (2, 7, and 12 October) and the subplots included three bolt-resistant sugar beet cultivars (Giada, Merak, and Sharif). Each plot consisted of 6 rows with a length of 5 m, at a distance of 50 cm and a distance between two plants of 20 cm, and planting was done manually. To determine root yield from the middle rows of each plot by eliminating the margin, harvest was done at an area of 4 m2. A sample of root paste of each treatment was sent to the Beta Lizer laboratory of Mashhad Agricultural Research and Agricultural Services Company to determine the percentage of sugar. Other quality parameters were measured by Beta Lizer (Braunschweig method). Using the polarimetry method (Sucromat), the percentage of sugar content and white sugar yield, and other quality parameters were calculated for all experimental plots. Combined analysis of variance for different locations and mean comparison based on least significant difference (LSD) at the level of 5% probability using SAS 9.4 software was performed. Also, the graph plots were performed using Excel software.Results and DiscussionThe results of the analysis of variance showed that the interaction effects of the location and cultivar were significant on bolting percentage, root yield, sugar content, Na content, yield coefficient, and white sugar yield. The first planting date (October 2) in Mashhad region for all three cultivars led to the highest percentage of bolting (78-90%). Delay in planting date from 2 October to 12 October, the bolting percentage of cultivars was reduced, significantly. The bolting percentage in Shirvan region was less than 8%. On the third planting date (October 12) in all regions, cultivars showed also a bolting percentage of less than 10%. Giada cultivar in Mashhad region with 47.3 ton.ha-1 had the highest and Sharif cultivar in Shirvan region with 22.6 ton. ha-1 had the lowest root yield. Shirvan region had less root yield than the other two regions. The highest sugar content (18.78%) belonged to Giada cultivar in Shirvan region and the lowest sugar content (13.01%) was observed in Sharif cultivar in Mashhad region. The planting date of 12 October was significantly lower in impurities, alkalinity coefficient, and molasses compared to earlier planting dates. The first planting date had the lowest (62.3%) and the third planting date had the highest (74.2%) extraction coefficient. Giada cultivar in Shirvan region had the highest extraction coefficient (78.1%) and the lowest extraction coefficient (60.8%) belonged to Sharif cultivar in Mashhad region.ConclusionIn Shirvan and Khezri regions, Giada cultivar but in Mashhad region, Merak cultivars had the highest white sugar yield. In general, the results showed that in Shirvan region, planting on 2 October and in Mashhad and Khezri regions planting on 7 October could lead to reaching maximum white sugar yield

Comparison of Brain Magnetic Resonance Imaging Lesions in Opium Addict and Non-addict Patients with Thrombotic Stroke: A Case-Control Study

Background: There is no consensus on the effect of opium on stroke yet. Some studies show the negative effects of opium on ischemic strokes. Here, we attempt to compare the volume of lesions in opium addict and non-addict patients with thrombotic stroke. Methods: This case-control study was conducted on patients with thrombotic stroke at Shafa Hospital in Kerman, Iran. The diagnosis was confirmed by clinical examinations, imaging, and laboratory tests. The volume of lesions was calculated by magnetic resonance imaging (MRI), and the data were analyzed by descriptive statistics, Mann-Whitney U test, Chi-square test, and linear regression analysis. Findings: A total of 60 patients were studied, 30 of whom were opium addicts and the rest were non-addicts. The mean volume of the lesion was 46.008350 ± 7.488990 (in the addict group) and 31.023335 ± 1.441570 (in the non-addict group), indicating a significant difference between the two groups (P = 0.005). Regression analysis results showed a significant relationship between the volume of stroke with opium addiction (P = 0.017), ischemic heart diseases (IHDs) (P = 0.006), hyperlipidemia (HLP) (P = 0.016), age (P = 0.035), and smoking (P = 0.044). Conclusion: The results of this study showed a higher volume of lesion in opium-addict patients compared to that in non-addicts as an indicator of stroke severity

Assessment of Moisture Status and Crop Production in Different Climate of Iran

Drought varies with regard to the time of occurrence, duration, intensity, and extent of the area affected from year to year. The objective of this study was therefore to gather and analyze standardized information on Role of Early Warning Systems by FAO56 and UNESCO models for cereals (wheat, barley, corn and rice), leguminous (bean, chickpea, lentil and alfalfa) and industrial crops (soybean, sunflower, canola, sugare beat, potato and cotton) in Iran environmental zones. To gather information on perceived risks and foreseen impacts of climatic factors on crops production, we designed a set of qualitative and quantitative data from agrometeorological and agriculture organizations in 44 stations in Iran (1961-2010). Annual average rainfall (mm.y-1) and ETo (mm.y-1) in stations with very dry climate are 76.56 and 3001.03, respectively, these rates for stations with dry climate are 195.41 mm.y-1 and 2249.44 mm.y-1, for stations with semi dry climate is 343.9 mm.y-1 and 1351.62 mm.y-1, for stations with semi humid climate is 583.8 mm.y-1 and 1153.4 mm.y-1 and for stations with humid climate is 1272.16 mm.y-1 and 949.91 mm.y-1. The maximum and minimum of Annual average rainfall happened in Rasht (1337.5 mm.y-1) and Zabol (57.7 mm.y-1) stations, and the maximum and minimum for Annual average ETo happened in Chabahar (3909.15 mm.y-1) and Anzali harbor (890.6 mm.y-1), respectively. Therefore, 13.63 percent of stations have suitable conditions for crop productions and 86.37 percent are in critical and unsustainable conditions

Agroecological Analysis of Sugar Beet Ecosystem (Beta vulgaris L.) in Torbat-e Heydarieh

IntroductionDuring the 1950s and 1960s, the green revolution led to a dramatic increase in global food and fodder production to eliminate hunger and boost food security. This production enhancement was accompanied by an intensified agricultural and chemical input consumption and increased cultivated area and mechanization. Although yield per unit area has improved in most crops, concerns about food security for the world's rising population are still significant. Guaranteeing food security in the future will necessitate a shift in management approaches to boost output, agroecosystem sustainability, and stability and reduce the environmental harm caused by agriculture. The first step to achieving sustainability and ecological intensification in agricultural systems is to have a comprehensive agroecological analysis of agricultural systems in each region. Hence, the complete evaluation and analysis of agroecological features according to their type in each region is necessary for establishing an optimal management technique. After analyzing the present state of each region's shared ecosystems, the optimal strategy for boosting production stability must be devised and implemented.Materials and MethodsThe goal of this study was to undertake a detailed investigation of the agroecological state of the sugar beet ecosystems on a local scale. For this purpose, data were collected on the area under cultivation, yield, and input consumption (including nitrogen and phosphorus fertilizers and chemical pesticides) from 2001 to 2016. Data was acquired from the Ministry of Agriculture and other related organizations and direct interviews with the farmers. In addition, data on climatic parameters (including daily minimum and maximum temperatures, precipitation, and sunny hours) were collected from the Torbat-e Heydariyeh meteorological station. This study researched the most important agroecological indicators of sugar beet farming systems in the Torbat-e Heydarieh region. Study indicators include variations in sugar beet cultivation area and yield, Potential yield via the methods FAO and FAO modified, beet yield gap, Regional Yield Factor trend, Changes in the intensification, yield stability, nitrogen uptake, and nitrogen utilization, and nitrogen use efficiency.Results and DiscussionAccording to this study results, sugar beet production increased by 59 percent between 2001 and 2016. During the research years, sugar beet ecosystems saw a drop in the cultivation area. Potential yield calculations using both FAO and modified FAO methodologies revealed that potential yield was nearly consistent over the research period in the region.The sugar beet yield gap averaged 35 ton.ha-1 over the research period. According to the findings, the percentage of sugar beet yield gap ranged from 53 to 69 %, with an average of 63 %. The extent of the yield gap decreased over the research period. The study of the regional yield factor (RYF) revealed that improving the management system resulted in higher actual yield and thus a smaller yield gap in sugar beet ecosystems. In sugar beet cultivation systems, the results revealed that by increasing intensification, the stability decreased. In sugar beet cultivation systems, there was a reduction in yield stability. Given that nitrogen consumption efficiency is one of the most important factors influencing the degree of stability in agricultural systems, the findings revealed that the rate of nitrogen uptake efficiency (NUpE), nitrogen utilization efficiency (NUtE), and nitrogen use efficiency (NUE) all decreased during the studied years in the region.ConclusionAccording to the findings, the major cause of the increase in nitrogen consumption, growing intensification, and decreasing stability in the analyzed systems appears to be a deficiency of nitrogen use efficiency and its downward trend. As a result, planning and altering management methods focusing on enhancing Nitrogen use efficiency may be proposed as the first step toward boosting sustainability in the Torbat-e Heydarieh sugar beet agroecosystems

Computational optical imaging with a photonic lantern

[EN] The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spatial resolution. Here, we demonstrate a route to high resolution microendoscopy using a multicore fibre (MCF) with an adiabatic multimode-to-single-mode "photonic lantern" transition formed at the distal end by tapering. We show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, and that these patterns are highly stable to fibre movement. This capability is then exploited to demonstrate a form of single-pixel imaging, where a single pixel detector is used to detect the fraction of light transmitted through the object for each multimode pattern. A custom computational imaging algorithm we call SARA-COIL is used to reconstruct the object using only the pre-measured multimode patterns themselves and the detector signals.This work was funded through the "Proteus" Engineering and Physical Sciences Research Council (EPSRC) Interdisciplinary Research Collaboration (IRC) (EP/K03197X/1), by the Science and Technology Facilities Council (STFC) through STFC-CLASP grants ST/K006509/1 and ST/K006460/1, STFC Consortium grants ST/N000625/1 and ST/N000544/1. S.L. acknowledges support from the National Natural Science Foundation of China under Grant no. 61705073. DBP acknowledges support from the Royal Academy of Engineering, and the European Research Council (PhotUntangle, 804626). The authors thank Philip Emanuel for the use of his confocal image of A549 cells and Eckhardt Optics for their image of the USAF 1951 target. The authors sincerely thank the anonymous reviewers of this paper for their detailed and considered feedback which helped us to improve the quality of this paper significantly.Choudhury, D.; Mcnicholl, DK.; Repetti, A.; Gris-Sánchez, I.; Li, S.; Phillips, DB.; Whyte, G.... (2020). Computational optical imaging with a photonic lantern. Nature Communications. 11(1):1-9. https://doi.org/10.1038/s41467-020-18818-6S19111Wood, H. A. C., Harrington, K., Birks, T. A., Knight, J. C. & Stone, J. M. High-resolution air-clad imaging fibers. Opt. Lett. 43, 5311–5314 (2018).Akram, A. R. et al. In situ identification of Gram-negative bacteria in human lungs using a topical fluorescent peptide targeting lipid A. Sci. Transl. Med. 10, eaal0033 (2018).Shin, J., Bosworth, B. T. & Foster, M. A. Compressive fluorescence imaging using a multi-core fiber and spatially dependent scattering. Opt. Lett. 42, 109–112 (2017).Papadopoulos, I. N., Farahi, S., Moser, C. & Psaltis, D. Focusing and scanning light through a multimode optical fiber using digital phase conjugation. Opt. Express 20, 10583–10590 (2012).Čižmár, T. & Dholakia, K. Exploiting multimode waveguides for pure fibre-based imaging. Nat. Commun. 3, 1027 (2012).Plöschner, M., Tyc, T. & Čižmár, T. Seeing through chaos in multimode fibres. Nat. Photon. 9, 529–535 (2015).Birks, T. A., Gris-Sánchez, I., Yerolatsitis, S., Leon-Saval, S. G. & Thomson, R. R. The photonic lantern. Adv. Opt. Photon. 7, 107–167 (2015).Birks, T. A., Mangan, B. J., Díez, A., Cruz, J. L. & Murphy, D. F. Photonic lantern’ spectral filters in multi-core fiber. Opt. Express 20, 13996–14008 (2012).Edgar, M. P., Gibson, G. M. & Padgett, M. J. Principles and prospects for single-pixel imaging. Nat. Photon. 13, 13–20 (2019).Mahalati, R. N., Yu, Gu. R. & Kahn, J. M. Resolution limits for imaging through multi-mode fiber. Opt. Express 21, 1656–1668 (2013).Amitonova, L. V. & de Boer, J. F. Compressive imaging through a multimode fiber. Opt. Lett. 43, 5427–5430 (2018).Mallat, S. A Wavelet Tour of Signal Processing 2nd edn (Academic Press, Burlington, MA, 2009).Lustig, M., Donoho, D. & Pauly, J. M. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58, 1182–1195 (2007).Davies, M., Puy, G., Vandergheynst, P. & Wiaux, Y. A compressed sensing framework for magnetic resonance fingerprinting. SIAM J. Imaging Sci. 7, 2623–2656 (2014).Wiaux, Y., Puy, G., Scaife, A. M. M. & Vandergheynst, P. Compressed sensing imaging techniques in radio interferometry. Mon. Not. R. Astron. Soc. 395, 1733–1742 (2009).Carrillo, R. E., McEwen, J. D. & Wiaux, Y. Sparsity averaging reweighted analysis (SARA): a novel algorithm for radio-interferometric imaging. Mon. Not. R. Astron. Soc. 426, 1223–1234 (2012).Katz, O., Bromberg, Y. & Silberberg, Y. Compressive ghost imaging. Appl. Phys. Lett. 95, 131110 (2009).Sun, B., Welsh, S. S., Edgar, M. P., Shapiro, J. H. & Padgett, M. J. Normalized ghost imaging. Opt. Express 20, 16892–16901 (2012).Kim, M., Park, C., Rodriguez, C., Park, Y. & Cho, Y.-H. Superresolution imaging with optical fluctuation using speckle patterns illumination. Sci. Rep. 5, 16525 (2015).Combettes, P. L. & Pesquet, J. -C. in Fixed-Point Algorithms for Inverse Problems in Science and Engineering (Springer, New York, 2011).Komodakis, N. & Pesquet, J.-C. Playing with duality: an overview of recent primal dual approaches for solving large-scale optimization problems. IEEE Signal Proc. Mag. 32, 31–54 (2015).Chandrasekharan, H. K. et al. Multiplexed single-mode wavelength-to-time mapping of multimode light. Nat. Commun. 8, 14080 (2017).Wadsworth, W. J. et al. Very high numerical aperture fibers. Photon. Technol. Lett. 16, 843–845 (2004).Pesquet, J.-C. & Repetti, A. A class of randomized primal-dual algorithms for distributed optimization. J. Nonlinear Convex Anal. 16, 2353–2490 (2015).Chambolle, A., Ehrhardt, M. J., Richtárik, P. & Schönlieb, C.-B. Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications. SIAM J. Optim. 28, 2783–2808 (2018).Bolte, J., Sabach, S. & Teboulle, M. Proximal alternating linearized minimization for nonconvex and nonsmooth problems. Math. Program. 146, 459–494 (2014).Chouzenoux, E., Pesquet, J.-C. & Repetti, A. A block coordinate variable metric forward-backward algorithm. J. Glob. Optim. 66, 457–485 (2016).Flusberg, B. A. et al. Fiber-optic fluorescence imaging. Nat. Methods 2, 941–950 (2005).Tsvirkun, V. et al. Bending-induced inter-core group delays in multicore fibers. Opt. Express 25, 31863–31875 (2017).Candès, E. J., Wakin, M. B. & Boyd, S. Enhancing sparsity by reweighted l1 minimization. J. Fourier Anal. Appl. 14, 877–905 (2008).Condat, L. A primal–dual splitting method for convex optimization involving lipschitzian, proximable and linear composite terms. J. Optim. Theory Appl. 158, 460–479 (2013).Vu, B. C. A splitting algorithm for dual monotone inclusions involving cocoercive operators. Adv. Comp. Math. 38, 667–681 (2013)