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The current study primarily aimed to simulate detonation initiation via turbulent jet flame acceleration in partial-premixed H2-air mixtures. Different vertical concentration gradients were generated by varying the duration of hydrogen injection (referred to as diffusion time) within an enclosed channel filled with air. H2-air mixtures with average hydrogen concentrations of 22.5% (lean mixture) and 30% (near stoichiometric mixture) were investigated at diffusion times of 3, 5, and 60 seconds. Numerical results show that the vertical concentration gradient has a major influence on the early-stage of flame acceleration (FA). In the stratified lean mixture, detonation began in all the diffusion times, and comparing the flame-speed graphs showed that a decrease in the diffusion time and an increase in the mixture inhomogeneity speeded up the flame propagation and the jet flame to detonation transition occurrence in the channel. In the stratified H2-air mixture with an average hydrogen concentration of 30%, transition from a turbulent jet flame to detonation occurred in all the cases, and the mixture inhomogeneity weakened the FA and delayed the detonation initiation.

Abstract Background: This study aimed to evaluate the efficacy and safety concerns of remdesivir and type 1 interferons (INFs) on the clinical outcomes of multiple sclerosis (MS) patients who were hospitalized with COVID-19. Methods: Using electronic health records systems, this is a cross-sectional study of two years of hospital admissions in terms of COVID-19 in Iran from March 2019 to August 2021. The severities of COVID-19 outcomes were ICU admission, hospitalization days, and 30-day mortality rates. Results: Nine hundred ninety-nine hospitalized MS patients with a definite diagnosis of COVID-19 based on PCR were recorded in the electronic health systems. Almost half of the patients were under treatment with an anti-CD-20 agent (rituximab or ocrelizumab) at the time of hospital admission (50.3%), with higher mortality rates, needing ICU admission, and longer hospitalization (p<0.05). There was a significant association between taking IFN alone (adjusted IRR=1.21, 95% CI 1.32 to 1.42) or in combination with RDV (adjusted IRR=1.30, 95% CI 1.18 to 1.5042) and longer hospitalization. There were no significant associations between antiviral treatment (RDV alone, IFNβ1 alone, and IFNβ1 plus RDV) and ICU admission (p>0.2), in-hospital mortality rate (p>0.2), or 30-day survival rate (p>0.2). The results were similar in patients who did or did not receive anti-CD-20 agents. Conclusion: Our data reveal that RDV, IFN, or a combination of IFN and RDV administration has no benefit in the treatment of hospitalized MS patients with COVID-19.

Phospholipases are among the important elements involved in lipid-dependent cell signaling that lead to the induction of downstream pathways. In the current study, phospholipases D (PLDs) gene family was characterized and compared in two important oilseed crops, Brassica napus and Camelina sativa. The results revealed that PLD has 33 members in Camelina sativa (CsPLD) and 41 members in Brassica napus (BnPLD). All studied PLDs showed a negative GRAVY value, indicating that PLDs are probably hydrophilic proteins. Phylogenetic analysis classified PLDs into five main subfamilies, including gamma, delta, beta, alpha, and zeta. According to evolution analysis, a different evolution process was observed between CsPLD and BnPLD. In addition, the results disclosed that most of the PLD genes have been segmentally duplicated under purifying selection. Cis-regulatory elements related to ABA and auxin responsiveness were found more in the upstream region of CsPLDs, while elements linked with MeJA responsiveness were observed more in the promoter region of BnPLDs. Analysis of the expression data showed that PLD alpha genes have a wide expression in most tissues. Quantitative expression analysis (qPCR) of CsPLD genes under salt stress, 200 mM of NaCl, was conducted in different time series. The results revealed that the CsPLD genes are involved in the response to salinity stress and their expression levels enhance with increasing salinity stress period. The outcomes of this research will be useful for future molecular works related to lipid signaling in oilseed plants.

Basil (Ocimum basilicum L.) is one of the most important medicinal and aromatic plants. Light intensity is an indispensable factor for plants due to its effect on photosynthesis and physiological processes. Here, we investigated the impact of light intensities and harvesting times on the photosynthesis of green and purple basil. The experiment involved subjecting plants to three different levels of sunlight for 12 days: complete—100%, 50%, and 30%—sunlight. In addition, we evaluated the impact of harvest time during the day. The highest levels of photosynthetic and protective pigments were detected under full sunlight conditions in purple basil harvested at noon. The highest levels of soluble and storage carbohydrates were recorded in the purple basil grown under full sunlight and harvested during the early morning. By contrast, the lowest levels were obtained in plants grown under 30% sunlight and harvested at noon time. Under all light treatments, the maximum quantum yield of photosystem II (FV/FM) was detected at 4 a.m. in both basil varieties; it decreased at noon and increased again at 5 p.m. Non-Photochemical Quenching (NPQ) was most elevated in the green variety under all light intensities at noon. However, the highest NPQ was detected in the purple variety at 8 a.m. The NPQ was lowest in both basil varieties during the early morning and afternoon. Full sunlight at noon caused temporary photoinhibition and reduced carbohydrates while enhancing pigment concentration and photo-protective mechanisms in basil plants.

Digital transformation has become the top priority for 80% of sports companies worldwide, but statistics show that between 70 and 95% of all digital transformation projects fail due to the significant and varied challenges that sports businesses face during the digital transformation process. This is because strategy, not digital technology, drives digital transformation, and without a mature digital transformation model, success is unlikely. Moreover, the digital transformation model of developing and developed countries is not the same, and it cannot be adopted as a blueprint. Therefore, the aim of this research was to propose a framework for the digital transformation maturity of electronic sports businesses in developing countries. Semi-structured interviews were conducted with a sample of 15 stakeholders of electronic sports businesses in Iran. Thematic analysis was used to analyze the interviews, and three main perspectives were identified: (1) enablers, (2) digital resilience and capabilities, and (3) digital transformation maturity stages. The enablers were formed by six sub-themes, including governance and leadership, strategy, culture, and skills, innovation, technology, and data. Digital resilience and capabilities were formed by four sub-themes, i.e., business model and ecosystem, digital customer experience, digital employee experience, and digital processes. Additionally, it was found that the digital transformation maturity stages were based on four stages: digital beginners, digital followers, digital conservatives, and digital leaders. The findings indicate that moving towards digital transformation and achieving digital resilience in electronic sports businesses will be impossible without synergy between enablers and digital resilience and capabilities. This framework can be useful for electronic sports businesses to evaluate their current digital status and effectively guide them towards a desirable digital status. Furthermore, electronic sports businesses can prioritize their efforts and resources for digital transformation by focusing on targeted capabilities and enablers and ensure the more effective and efficient allocation of resources towards digital transformation.

Abstract Today, growing the production of the agricultural sector in order to meet the increasing demand of food is happening through excessive consumption of water resources, energy, chemical fertilizers and pesticides, especially in developing countries. Therefore, the present study aims to design a new approach for sustainable management of resources and realization of clean production in the agricultural sector. In order to design the new approach of the present study, the integration of two concepts of water-energy-food (WEF) nexus and decoupling pollution- agricultural growth (DEC) was used. The proposed approach is modeled through the multi-objective programming model by optimizing the WEF and DEC indices, which is the first research effort in this field. In this regard, the performance of this approach was evaluated in Jiroft county in Kerman province, Iran. The results of the study showed that the consumption of chemical fertilizers and pesticides in the optimal pattern with the simultaneous provision of WEF and gross margin (GM) goals (Optimal 1) is about 110 kg and 0.19 liters per hectare, respectively, more than the proposed pattern (Optimal 3). On the other hand, the consumption of water and energy resources in the optimal pattern with the simultaneous provision of DEC and GM goals (Optimal 2) is about 8785 cubic meters and 1108 MJ per hectare more than the proposed pattern (Optimal 3), respectively. Therefore, this approach, due to the simultaneous integration of two concepts of sustainable development in the agricultural sector, can overcome the weakness of the WEF nexus in not paying attention to the issues of environmental pollution affected by the use of chemical inputs and the weakness of separating pollution- agricultural growth in not paying attention to the management of water and energy resources in the agricultural sector.

AbstractNitrogen (N), phosphorus (P), and potassium (K) contents are crucial quality indicators for forage in alpine natural grasslands and are closely related to plant growth and reproduction. One of the greatest challenges for the sustainable utilization of grassland resources and the development of high-quality animal husbandry is to efficiently and accurately obtain information about the distribution and dynamic changes in N, P, and K contents in alpine grasslands. A new generation of multispectral sensors, the Sentinel-2 multispectral instrument (MSI) and Tiangong-2 moderate-resolution wide-wavelength imager (MWI), is equipped with several spectral bands suitable for specific applications, showing great potential for mapping forage nutrients at the regional scale. This study aims to achieve high-accuracy spatial mapping of the N, P, and K contents in alpine grasslands at the regional scale on the eastern Qinghai-Tibet Plateau. The Sentinel-2 MSI and Tiangong-2 MWI data, coupled with multiple feature selection algorithms and machine learning models, are applied to develop forage N, P, and K estimation models from data collected at 92 sample sites ranging from the vigorous growth stage to the senescent stage. The results show that the spectral bands of both the Sentinel-2 MSI and Tiangong-2 MWI have an excellent performance in estimating the forage N, P, and K contents (the R2 values are 0.68–0.76, 0.54–0.73, and 0.74–0.82 for forage N, P, and K estimations, respectively). Moreover, the model integrating the spectral bands of these two sensors explains 78%, 74%, and 84% of the variations in the forage N, P, and K contents, respectively. These results indicate that the estimation ability of forage nutrients can be further improved by integrating Tiangong-2 MWI and Sentinel-2 MSI data. In conclusion, integration of the spectral bands of multiple sensors is a promising approach to map the forage N, P, and K contents in alpine grasslands with high accuracy at the regional scale. This study offers valuable information for growth monitoring and real-time determination of forage quality in alpine grasslands.

Abstract Limited resources and suitable farming lands, climate changes, and fast population growth are among the main concerns of the societies that bring security challenges to the governments. One solution to overcome these challenges is creating controlled areas for cultivation, growing plants, and farming, such as a greenhouse. greenhouses significantly improve the efficiency of land use in agriculture, both increasing the crop yield and the number of harvesting throughout the year, which has long proved their effectiveness. Although the history of greenhouses for farming is from Roman times, however, there are different barriers to their applications. Energy supply to control the cultivation conditions of the plants for greenhouses, in particular for heating and cooling for hot and cold climate areas, are an example. On the other hand, based on the global energy trend, decentralized energy production based on solar energy is highly regarded. Greenhouses also like households have significant potential to harvest solar energy but since greenhouses need sunlight to cultivate plants, reducing sunlight by using common solar panels is not a logical action. However, by implementing semitransparent solar cells in these greenhouses, the issue of reduced sunlight could be addressed and a further improvement in efficiency by mitigating energy demand in these greenhouses could be achieved. This research is to investigate the integration of the energy supply system with consumption in greenhouses. First, we assess different conventional types of greenhouses in terms of energy demand. Then, we investigate the energy demand with OPV integration for each type. Finally, the best design of the greenhouse for OPV integration is recommended. Results show that Flat-arch geometry is the best choice for dry and cold climates while Sawtooth geometry showed better improvements in tropical climates. In both temperate/mesothermal and continental/microthermal climates, A-frame geometry showed superiority in energy-saving. Simulations revealed an annual electricity generation of 6.9 MWh to 9.9 MWh for the optimum structural geometries that decrease the energy consumption of greenhouses.