Elemental ratios (δ13C, δ15N and C/N) and carbon and nitrogen concentrations in macrophytes, sediments and sponges of the hypersaline Al-Kharrar Lagoon (KL), central eastern Red Sea coast, were ...measured to distinguish their sources, pathways and see how they have been influenced by biogeochemical processes and terrestrial inputs. The mangroves and halophytes showed the most depleted δ13C values of -27.07±0.2 ‰ and -28.34±0.4 ‰, respectively, indicating their preferential 12C uptake, similar to C3-photosynthetic plants, except for the halophytes Atriplex sp. and Suaeda vermiculata which showed δ13C of -14.31±0.6 ‰, similar to C4-plants. Macroalgae were divided into A and B groups based on their δ13C values. The δ13C of macroalgae A averaged -15.41±0.4 ‰, whereas macroalgae B and seagrasses showed values of -7.41±0.8 ‰ and -7.98 ‰, suggesting uptake of HCO3- as a source for CO2 during photosynthesis. The δ13C of sponges was -10.7±0.3 ‰, suggesting that macroalgae and seagrasses are their main favoured diets. Substrates of all these taxa showed δ13C of -15.52±0.8 ‰, suggesting the KL is at present a macroalgae-dominated lagoon. The δ15N in taxa/sediments averaged 1.68 ‰, suggesting that atmospheric N2-fixation is the main source of nitrogen in/around the lagoon. The heaviest δ15N (10.58 ‰) in halophytes growing in algal mats and sabkha is possibly due to denitrification and ammonia evaporation. The macrophytes in the KL showed high C %, N %, and C/N ratios, but this is not indicated in their substrates due possibly to a rapid turnover of dense, hypersaline waters carrying most of the detached organic materials out into the Red Sea. The δ13C allowed separation of subaerial from aquatic macrophytes, a proxy that could be used when interpreting paleo-sea level or paleoclimatic changes from the coastal marine sediments.
Telomeres maintain genomic integrity in normal cells, and their progressive shortening during successive cell divisions induces chromosomal instability. In the large majority of cancer cells, ...telomere length is maintained by telomerase. Thus, telomere length and telomerase activity are crucial for cancer initiation and the survival of tumors. Several pathways that regulate telomere length have been identified, and genome-scale studies have helped in mapping genes that are involved in telomere length control. Additionally, genomic screening for recurrent human telomerase gene hTERT promoter mutations and mutations in genes involved in the alternative lengthening of telomeres pathway, such as ATRX and DAXX, has elucidated how these genomic changes contribute to the activation of telomere maintenance mechanisms in cancer cells. Attempts have also been made to develop telomere length- and telomerase-based diagnostic tools and anticancer therapeutics. Recent efforts have revealed key aspects of telomerase assembly, intracellular trafficking and recruitment to telomeres for completing DNA synthesis, which may provide novel targets for the development of anticancer agents. Here, we summarize telomere organization and function and its role in oncogenesis. We also highlight genomic mutations that lead to reactivation of telomerase, and mechanisms of telomerase reconstitution and trafficking that shed light on its function in cancer initiation and tumor development. Additionally, recent advances in the clinical development of telomerase inhibitors, as well as potential novel targets, will be summarized.
Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving ...average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.
This article presented a novel modification and application of the salp swarm algorithm (SSA) that is inspired by the chain behavior of salp fishes that live in deep oceans. Firstly, the enhanced ...salp swarm algorithm (ESSA) is proposed to improve the inadequate results of the SSA compared to the other algorithms, especially for the high dimensional functions. The ESSA algorithm is verified using twenty-three benchmark test functions and compared with the original SSA algorithm and other algorithms. The statistical analysis of the obtained results revealed that the ESSA algorithm is significantly improved and the convergence curves showed the fast convergence to the best solution. Secondly, The SSA and ESSA algorithms are applied to enhance the maximum power point tracking and the fault-ride through ability of a grid-tied permanent magnet synchronous generator driven by a variable speed wind turbine (PMSG-VSWT). The multi-objective function (integral squared error) is minimized to find the high dimensional parameters of Takagi–Sugeno–Kang fuzzy logic controllers (TSK-FLC) used in the cascaded control of grid-tied PMSG-VSWT. The simulation results using PSCAD/EMTDC proved that the produced power when using ESSA is higher than when using SSA which mean higher efficiency and lower cost.
•This paper proposes an enhancement to the salp swarm algorithm (ESSA).•The ESSA is tested with twenty-three benchmark functions.•The ESSA is compared with eight published algorithms.•The ESSA and SSA are applied to the variable speed wind generators.
•A novel optimization method is presented for photovoltaic modeling.•An accurate three-diode photovoltaic model is used in this paper.•Transient search optimization is compared with other ...algorithms.•The simulation results of the Photovoltaic model are verified by the measured data.
This paper presents a novel efficient metaheuristic algorithm called Transient Search Optimization (TSO), which is inspired by the transient process of the inductive and capacitive circuits. Also, this paper presents an objective function based on the datasheet of PV modules at standard test conditions (STC). Then, the TSO algorithm is applied to minimize the objective function to find the optimal nine parameters of the three-diode model (TDM) of the PV module. Also, the results of the proposed TSO algorithm are compared with that obtained by using other metaheuristic algorithms, where in this regard the TSO achieved the best results. The proposed technique is verified by applying it to find the optimal TDM of three commercially common PV modules with different cell types, rated power, and terminal voltage. Then, the simulated I-V and P-V characteristics of these PV modules matched with the measured data under many environmental conditions. Accordingly, the results have proved that the offered technique is useful to find the optimal TDM of all PV modules based on the dataset given by the manufacturers.
Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and ...rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs.
The internet of things (IoT) has a significant economic and environmental impact owing to the billions or trillions of interconnected devices that use various types of sensors to communicate through ...the internet. It is well recognized that each sensor requires a small amount of energy to function; but, with billions of sensors, energy consumption can be significant. Therefore, it is crucial to focus on developing energy-efficient IoT technology and sustainable solutions. The contribution of this article is to support the implementation of eco-friendly IoT solutions by presenting a thorough examination of energy-efficient practices and strategies for IoT to assist in the advancement of sustainable and energy-efficient IoT technologies in the future. Four framework principles for achieving this are discussed, including (i) energy-efficient machine-to-machine (M2M) communications, (ii) energy-efficient and eco-sustainable wireless sensor networks (WSN), (iii) energy-efficient radio-frequency identification (RFID), and (iv) energy-efficient microcontroller units and integrated circuits (IC). This review aims to contribute to the next-generation implementation of eco-sustainable and energy-efficient IoT technologies.
This article offers a new physical-based meta-heuristic optimization algorithm, which is named Transient Search Optimization (TSO) algorithm. This algorithm is inspired by the transient behavior of ...switched electrical circuits that include storage elements such as inductance and capacitance. The exploration and exploitation of the TSO algorithm are verified by using twenty-three benchmark, where its statistical (average and standard deviation) results are compared with the most recent 15 optimization algorithms. Furthermore, the non-parametric sign test,
p
value test, execution time, and convergence curves proved the superiority of the TSO against other algorithms. Also, the TSO algorithm is applied for the optimal design of three well-known constrained engineering problems (coil spring, welded beam, and pressure vessel). In conclusion, the comparison revealed that the TSO is promising and very competitive algorithm for solving different engineering problems.
Whilst a net zero energy (NZE) home produces the same amount of energy as it consumes it still exchanges significant amount of energy with the grid due to mismatch between the generation and load ...patterns. Consequently, the homeowner has to pay an annual electric bill because the cost of imported energy is usually higher than that of exported energy. Installing a local battery energy storage system (BESS) can reduce the electric bill by exchanging less energy with the grid. This paper proposes a method of determining the optimal size of a BESS for a typical NZE home with rooftop solar photovoltaic (PV) system to minimize the annual net payment for electricity and battery cost. The optimal battery size is determined through solving an optimization problem which is formulated using hourly load and PV generation data for a South Australian home, battery annual payment rate, retail price (RP), and feed-in tariff (FIT). The effects of interest rate, RP and FIT on the annual net payment are investigated. The results obtained are thoroughly analysed and clearly indicate that, with current installation cost of BESS and South Australian RP and FIT, the use of a local BESS is economically beneficial for the homeowner.
•Proposed a method to optimize battery size for PV-connected net zero energy home.•It minimizes home owner's annual net payment for electricity usage and battery cost.•The method has been applied to a typical South Australian (SA) NZE home.•It shows that optimally sized battery storage is economically beneficial.•The method is applicable for NZE homes in any part of the world.
Rheumatic heart disease remains an important preventable cause of cardiovascular death and disability, particularly in low-income and middle-income countries. We estimated global, regional, and ...national trends in the prevalence of and mortality due to rheumatic heart disease as part of the 2015 Global Burden of Disease study.
We systematically reviewed data on fatal and nonfatal rheumatic heart disease for the period from 1990 through 2015. Two Global Burden of Disease analytic tools, the Cause of Death Ensemble model and DisMod-MR 2.1, were used to produce estimates of mortality and prevalence, including estimates of uncertainty.
We estimated that there were 319,400 (95% uncertainty interval, 297,300 to 337,300) deaths due to rheumatic heart disease in 2015. Global age-standardized mortality due to rheumatic heart disease decreased by 47.8% (95% uncertainty interval, 44.7 to 50.9) from 1990 to 2015, but large differences were observed across regions. In 2015, the highest age-standardized mortality due to and prevalence of rheumatic heart disease were observed in Oceania, South Asia, and central sub-Saharan Africa. We estimated that in 2015 there were 33.4 million (95% uncertainty interval, 29.7 million to 43.1 million) cases of rheumatic heart disease and 10.5 million (95% uncertainty interval, 9.6 million to 11.5 million) disability-adjusted life-years due to rheumatic heart disease globally.
We estimated the global disease prevalence of and mortality due to rheumatic heart disease over a 25-year period. The health-related burden of rheumatic heart disease has declined worldwide, but high rates of disease persist in some of the poorest regions in the world. (Funded by the Bill and Melinda Gates Foundation and the Medtronic Foundation.).
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