Search

Present paper aims to investigate effects of nanoparticle addition to thermal oil of parabolic trough collectors which are integrated to Kalina cycle for electricity generation to run an alkaline electrolyzer for hydrogen production. Thermodynamic models for Kalina cycle, thermal model for parabolic collectors and electrochemical models for water electrolyzer are developed for overall plant modeling. Three types of nanoparticles including: copper oxide, alumina and titanium oxide are considered and overall plant performance is investigated using these nanoparticles and is compared with basefluid. The nanofluid thermophysical properties are evaluated as a function of nanoparticles' properties, volume fraction and the base fluid properties. The effects of key variables such as nanoparticle volume fraction, collector inlet temperature, evaporation pressure and ammonia concentration of Kalina system were evaluated on hydrogen production as well as exergetic efficiencies. Results revealed greater positive influence for CuO compared to TiO 2 and Al 2 O 3 on performance improvement for both hydrogen generation and its exergetic efficiency. Also, compared to basefluid (without nanoparticles) case, it was found that CuO particles result in a solar-to-H 2 exergy efficiency improvement by 4.7%. Using this nanofluid with a volume fraction of 5%, the H 2 production rate enhances from 0.633 kg / h to 0.664 kg / h , indicating 4.9 % improvement. • An alkaline electrolyzer-based hydrogen production plant powered by PTC is modeled. • Three types of nanoparticles are employed for hydrogen production enhancement. • Copper oxide nanoparticles are found to be better than titanium oxide and alumina. • Nanoparticles are applied to enhance the PTC thermal performance. • Hydrogen production rate can be enhanced by 4.9 % using CuO nanoparticles. [ABSTRACT FROM AUTHOR]

In this article, a maximum Power Point Tracking (MPPT) controller is designed for photovoltaic (PV) applications. This controller has been implemented with Fuzzy Gain Scheduling of Proportional–Integral–Derivative (PID) type controller (FGS-PID). To implement this controller, scaling factors (SF) for the input signals of FGS are applied. The recommended adaptive two-level controller has all the welfare of fuzzy logic system (FLC) and PID control. Zieglere–Nichols technique is used to fine-tune the initial PID's gains. The PID's gains are updated with FGS-PID in transient and steady-state conditions to cope with fluctuations, minimize settling time and ensure stability. FLC is used for gain factors to deal with tuning of conditioned input signals of the FGS-PID. Moreover, FLC and an improved shuffled frog leaping algorithm (ISFLA) are applied to tune the member functions (MFs) of FGS. The use of this algorithm can lead to automatic regulation of the triangular MFs. Simulations are done to confirm the edge of this approach over conventional methods. It is very fast and accurate in tracking the maximum power. It provides minimum oscillations and improved dynamic response than other approaches. The speed of the tracking is also improved with acceptable accuracy. [ABSTRACT FROM AUTHOR]

How to better grasp students' learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students' self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student's learning needs, this paper proposes recommendations for developing students' learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students' learning dynamics as it generates cognitive content about students' learning and allows for the timely discovery of students' learning expectations and cutting-edge dynamics. [ABSTRACT FROM AUTHOR]

In this research, a proton-conducting membrane fuel cell is used in conjunction with a gasifier for producing electricity with air considered as the gasification agent. The simultaneous electricity, heat, and hot water generation of these two when coupled with an Organic Rankine Cycle (ORC) installed within a Thermoelectric Generator (TEG) was studied. Additionally, to utilize the electricity generated from various parts of the process; i.e. the fuel cell, organic Rankine cycle turbine, and the thermoelectric generator, the reverse osmosis (OR) water desalination unit was coupled to the process. The aforementioned system was studied and analyzed based on thermodynamic and thermos-economic aspects. A parametric study was also carried out to analyze the effect of main factors on power output, energy/exergy yield, and the total cost of each system. Finally, a dual objective optimization was achieved by taking the main factors into consideration and using a genetic algorithm. Results indicated that the work generated by the system was not significant; however, the amount of hot water produced was relatively promising. Based on exergy and exergoeconomic evaluation, the After-Burner is an important non-renewable energy source while the fuel cell has a higher inefficiency cost which could be due to low work generation. Results indicate that the moisture in the biomass and the figure of merit, are important parameters that have to be kept as low as possible. The energy yield of the fuel cell was calculated to be 45.05%. Additionally, considering the optimized operating point, pure power output and system cost rate will be 1.81 KW and 5.15 $/hr respectively. • Proposing a novel cycle based on fuel cell for electricity generation. • Adding nanomaterial on the fuel cell for increasing the performance. • Double-objective optimization of the related energy system. [ABSTRACT FROM AUTHOR]

This study deals with multi-objective energy systems' performance analysis and optimization, including power generation and chilling. The system studied comprises a fuel cell, a gas turbine, an absorption chiller, and a steam recovery generator. This way, the cycle is thermodynamically modeled to allow catching optimum design points using the genetic algorithm. The study compares the optimum points of this cycle with that in a hybrid fuel cell (FC) - gas turbine (GT) cycle. The study uses machine learning methods for optimization to reduce calculation time and costs. This energy system can generate 500–1000 kW of output power. The cooling load varies from 10 to 65 kW, depending on the decision-making parameters. According to the optimization results, the energy efficiency can be improved by up to 65%, while the total cost rate can be diminished by up to $16 per hour in the improved cycle. Environmentally, the exergoenvironmental index of 0.4803 and the sustainability index of 2.443 were obtained for the hybrid gas turbine-fuel cell cycle. • Proposing a novel cycle for cooling and power generation. • Comparative ML of adding SOFC to enhance the performance. • Implementing various machine learning methods on the simulation data. • Double-objective optimization of the related energy system. [ABSTRACT FROM AUTHOR]

The usage of Nanofluid to enhance heat transfer has been investigated by many researchers in small-scale heat transfers; however, in large-scale power plants, it has not been taken care of. In this research paper, a newly proposed energy system for hydrogen generation based on renewable sources is proposed and analyzed in detail. Unlike other power input systems, the vanadium chloride system generates hydrogen because it uses waste heat. In this regard, the nanoparticles of Al 2 O 3 and CuO are utilized in the main heat exchanger to enhance the heat transfer to the reactor of VCLC to generate more hydrogen. Also, the compound system has the gasifier-based internally fired gas turbine as the main system and can generate hydrogen in a more green way. The optimization based on deep learning methods is applied to seek the highest point of operation by the system. The results exhibit that Al 2 O 3 causes more heat transfer and efficiency enhancement, thus in hydrogen production, causing an increase of 18.5% compared to base fluid and 8.3% compared to CuO in H 2 Generation In circumstances when it is optimal, the values for exergetic efficiency and total hydrogen production are 64.5% and 4.5 kg/s respectively. In addition, using a nanofluid heat exchanger and biomass energy reduces CO 2 emissions to 0.91 kg/kWh. • Nanomaterial addition to Main heat exchanger in Power plant is investigated. • The enhancement in hydrogen generation by using nanoparticles is achived. • Performance assessment of system and deep learning based optimization is conducted. [ABSTRACT FROM AUTHOR]

Electricity is produced by electrochemical reactions in devices known as Solid Oxide Fuel Cells (SOFCs), which are renowned for their high efficiency, minimal emissions, great fuel flexibility, lack of moving parts, and quiet operation; however, the counterpart of them the molten carbonate fuel cells are less in attention. The present study deals with an MCFC-based supercritical CO 2 recovered system for generating high-efficiency power and cooling based on energy from biomass. The system is modeled, and the key design variables' effect is foreseen. Using MATLAB, the machine learning techniques are implied to reduce the emission and LCOP while keeping the efficiency at its highest value. The results indicate that Current density, fuel utilization factor, cell temperature, and pressure ratio have a critical effect on system performance. Also, the efficiency reduces by reducing cell voltage due to an increase in current density and reaching 52%. Increasing fuel utilization factor causes a 7% drop in net power production. Finally, At the best solution point, Levelized product cost of 0.11 ($/kWh), an exergy efficiency of 56.5%, and a CO 2 emission of 0.35 kg/kWh are reached. • This study addresses MCFC-based supercritical CO 2 recovery cycle for power and cooling. • Cell voltage drops as current density rises, reducing efficiency to 52%. • MATLAB machine learning is used to reduces emissions and LCOP while increasing efficiency. • Increasing fuel utilization factor reduces net power by 7%. • Tri-objective optimization recommends 56.5% exergy efficiency, 0.11$/GJ total cost, and 0.35 ton/MWh CO 2 emission. [ABSTRACT FROM AUTHOR]

Renewable resources are a crucial component in power system research within contemporary intelligent power systems. It is imperative that they are given meticulous consideration. Microgrids (MGs) have emerged as a solution to facilitate the integration of renewable energy sources on a large scale. The incorporation of power production innovations that exhibit high levels of unpredictability in their generation would have a notable impact on the management of power resource planning within microgrids. Therefore, it is imperative to implement efficient power management mechanisms in microgrids. This study introduces a scheduling arrangement for a microgrid (MG) that includes a solar power unit (PV), which operates on a day-ahead basis. This paper explores the effects of various weather variables on the power generated by photovoltaic units and the optimum scheduling of microgrids. The present study employs the modified fluid search optimization algorithm to address the challenge of optimum management of energy in a microgrid connected to the grid that is characterized by a high level of unpredictability. The simulation results of the study indicate that the implementation of a photovoltaic model in an actual setting enhances the precision of the system for managing energy and reduces the overall operational costs of the interconnected microgrid. The algorithm under focus has been evaluated on a standard MG. The method under consideration has been executed on the MATLAB/Simulink computational environment and its efficacy has been evaluated. [ABSTRACT FROM AUTHOR]

To lower operational costs as well as emissions when wind and solar resources are available in a microgrid (MG), this study discusses the scheduling of electric vehicles (EVs) and responsive demands simultaneously. To mitigate the effects associated with undispatchable energy sources such as wind and solar, the proposed system makes use of EVs for peak shaving and load curve changes, while responsive demands provide the reserves required to do so. In addition, a two‐stage model is provided to evaluate MG's planned running costs (energy and reserve). Costs related to generating and reserving electricity are minimized in Stage 1, while costs related to adjusting unit scheduling to account for fluctuations in wind and photovoltaic output are minimized in Stage 2. Converged barnacles mating optimizer (CBMO) is a highly effective and powerful optimization tool that is used to handle the resultant objective optimization issue. An MG consisting of multiple dispersed generations is used to implement the proposed model. It is worth mentioning that three scenarios have been defined to analyze the impact of joint scheduling of EVs and controllable loads on the MG's day‐ahead operation. The three cost terms, that is, the generation cost, the reserve cost, and the startup cost of units in this scenario, are derived as $745.6913, $10.5278, and $6.35, respectively, remarkably less than the values reported in Scenarios 1 and 2. In Scenario 1, the CBMO algorithm yielded a lower MG operational cost than methods by a margin of 843.2 $/day. Costs per day of operation in Scenario 2 are derived to be $819.3 using the CBMO technique, whereas in Scenario 3, they are determined to be $743.1. [ABSTRACT FROM AUTHOR]

The hybrid renewable energy-based power generation systems offer a reliable solution to mitigate the drawbacks of individual energy source. In this respect the biomass and geothermal energies have some well-known advantages (such as continuous provision) over the solar and wind energies. In the present paper, two integration modes are proposed for hybridization of geothermal heat with a biomass-driven gas turbine based combined power cycle. The Mode 1 configuration employs geothermal heat to increase the temperature of feedwater before the deaerator, whereas the Mode 2 structure applies the geothermal energy to generate more steam for the low pressure steam turbine. Detailed exergetic, environmental and economic appraisals are carried out to examine the hybrid systems performances. In order to represent an accurate comparison of the suggested hybridization modes, the systems are optimized to achieve minimum levelized electricity cost. The results have indicated better performance for Mode 2 hybridization scenario over the Mode 1. The former system yields 10.0% greater exergy efficiency with 20.1% less electricity cost compared to Mode 1, while it can generate 22.5% more electricity. Also, it brings about 22.5% lower CO 2 emission. However, the Mode 2 configuration possess one drawback compared to Mode 1, which is its higher overall system cost rate by 17.9%. The results show that, the higher cost rate of Mode 2 configuration is mainly due to larger pressure ratio of the air compressor which causes more costs for the compressor and gas turbine components. • Geothermal energy is hybridized with biomass energy to enhance the power generation. • Different hybridization modes are offered and optimized. • Exergetic, economic and environmental remediation investigations are conducted. • Levelized cost of electricity is estimated for each hybrid modes. [ABSTRACT FROM AUTHOR]

In a worldwide environment context where the emergency need to decrease pollutant emissions is an important issue, the research for solutions is increasing. Fuel cell technology is anticipated to become a practicable approach for solving the problem of pollution due to its environmentally friendly characteristics. In this work, a novel problem formulation is suggested for the efficient recognition of PEMFC parameters which the solving is by using the Improved Fish Migration Optimizer (IFMO) technique. After coding the steps of the algorithm in MATLAB, the objective function is resolved for the fuel cell. Comprehensive simulations evaluate the formulation performance with the suggested and traditional objective functions; then, the outcomes are compared. To confirm the suggested formulation ascendancy compared to the traditional curve-fitting method, a complete assessment based on convergence rate, the value of the objective function, and the value of absolute voltage error are performed. The achieved value of the objective function, absolute voltage error, and average time of computation is 0.005, 0.4, and 1.63, respectively. Environmentally, the combustion of hydrogen and its use in PEMFC produce no carbon dioxide. • Using a new metaheuristic method, called Improved Fish Migration Optimizer. • Suggesting a new problem formulation for the efficient extraction of PEMFC parameters. • Decreasing pollutant emissions by a practicable approach. • Comprehensive evaluation based on objective function value, convergence speed, and the absolute voltage error value. [ABSTRACT FROM AUTHOR]

The main aim of this paper is the optimal power extraction based on an intelligent structure. It is implemented through fuzzy gain scheduling of PID (FGS-PID) controller in combination with the radial basis function network sliding mode (RBFNSM) for controlling a grid-connected hybrid generating system. A wind turbine (WT) based on the permanent magnet synchronous generator (PMSG) and a photovoltaic (PV) is considered for this study. FGS-PID controller equipped with scaling factors (SF) for the input signals of FGS are used to reach MPPT for the PV system. In order to regulate the member functions (MFs) of FGS, the fuzzy logic controller (FLC) and developed farmland fertility optimization (IFFO) algorithm are used. Moreover, the pitch angle control is applied for the WT. The pitch angle control of the WT is implemented by the RBFNSM to control the generated power and the speed at the nominal value. For protecting the wind turbine architecturally and escape catastrophic operation, this idea is implemented. MATLAB software is used to show the effectiveness of the proposed controller. The main advantages of the proposed method over other approaches are efficiency, fast and accurately tracking the highest generated power of the PV system. [ABSTRACT FROM AUTHOR]

In general, solar-driven systems have substantial heat loss; so that brought designers to consider organic Rankine cycle for heat recovery. However, it still wastes amount of energy in the organic Rankine cycle's condensers. In this context, the present study proposes a novel multigeneration system with the aim of hydrogen and cooling load production, as well as power generation using heat recovery units by considering environmental impacts. The present solar-driven system comprises two power cycles, thermoelectric generator, hydrogen production unit, and absorption chiller subsystem. Performance assessment of the system in terms of energy, exergy, economic, and environmental was executed using a developed programming code, based on a mathematical model. The model's functions were parametrically investigated by considering the most effective parameters. The time-consuming optimization process caused a machine learning optimization procedure to be employed. After optimization, the exergy efficiency, total cost rate, and hydrogen production rate of 13.05%, 101.4 $/h, and 5.15 kg/h were obtained, respectively. For optimized case, the environmental analysis showed that the amount of CO 2 emission reduction rate was resulted in 203.2 kg/h, which is 70 kg/h more than the result of base case. San Francisco, as a case study, was investigated to be considered for the potential of implementing the proposed system. As a result, the hydrogen production rate of 5.94 kg/h and net power generation of 1001 kW were acquired at peak. [Display omitted] • Hydrogen and cooling production using heat recovery in a solar-driven system. • Optimizing the objective functions of the model through a deep learning method. • Comparing base case and optimized case in terms of economy and environmental. • San Francisco was considered as a case study to implement the proposed system. [ABSTRACT FROM AUTHOR]

Identifying cohesive subgraphs is an important topic in graph theory and complex network analysis. The quasi‐clique, as a generalization of clique, can be used to identify the functional and structural properties of various networks. In this paper, the authors study the maximum weighted quasi‐clique problem and propose a local search algorithm for solving the problem. In the algorithm, an iterated local search method is used as the search framework. To find the quasi‐clique with the maximum total weights, hybrid vertex selection strategies are proposed and incorporated into the authors' algorithm. The hybrid strategies utilize a probability‐based mechanism for choosing sub‐strategies in each round of the local search. The authors conduct experiments on synthetic networks and real‐world networks to show the effectiveness of the authors' algorithm. The results indicate that hybrid strategies perform better than existing methods, and thus the authors' algorithm has a good ability to tackle various networks. [ABSTRACT FROM AUTHOR]

The (weighted) partial maximum satisfiability ((W)PMS) problem is an important generalization of the classic problem of propositional (Boolean) satisfiability with a wide range of real-world applications. In this paper, we propose an initialization and a diversification strategy to improve local search for the (W)PMS problem. Our initialization strategy is based on a novel definition of variables' structural entropy, and it aims to generate a solution that is close to a high-quality feasible one. Then, our diversification strategy picks a variable in two possible ways, depending on a parameter: continuing to pick variables with the best benefits or focusing on a clause with the greatest penalty and then selecting variables probabilistically. Based on these strategies, we developed a local search solver dubbed ImSATLike , as well as a hybrid solver ImSATLike-TT , and experimental results on (weighted) partial MaxSAT instances in recent MaxSAT Evaluations show that they outperform or have nearly the same performances as state-of-the-art local search and hybrid competitors, respectively, in general. Furthermore, we carried out experiments to confirm the individual impacts of each proposed strategy. [ABSTRACT FROM AUTHOR]

A fast analytical method for predicting the shielding effectiveness (SE) of heterotypic enclosures was proposed. By applying the concept of electromagnetic topology, the observation points and the walls are treated as nodes, and the space between them as tubes. Equivalent circuit models of the enclosures are derived in which the aperture on the front wall are considered as a two‐port network and so is the shape of the discontinuous part, which resembles a window structure. Then, the expressions of the total voltage components can be obtained using the extended Baum–Liu–Tesche (BLT) equation. Three different types of heterotypic enclosures are proposed to validate the method. Compared with numerical simulation, the proposed method can reduce the computation time from 1000 s for numerical algorithms to a few hundred milliseconds while maintaining a very close accuracy. [ABSTRACT FROM AUTHOR]

Traditional texture cluster algorithms are frequently used in engineering; however, despite their widespread application, these algorithms continue to suffer from drawbacks including excessive complexity and limited universality. This study will focus primarily on the analysis of the performance of a number of different texture clustering algorithms. In addition, the performance of traditional texture classification algorithms will be compared in terms of image size, clustering number, running time, and accuracy. Finally, the performance boundaries of various algorithms will be determined in order to determine where future improvements to these algorithms should be concentrated. In the experiment, some traditional clustering algorithms are used as comparative tools for performance analysis. The qualitative and quantitative data both show that there is a significant difference in performance between the different algorithms. It is only possible to achieve better performance by selecting the appropriate algorithm based on the characteristics of the texture image. [ABSTRACT FROM AUTHOR]

The segmentation of brain tissue by MRI not only contributes to the study of the function and anatomical structure of the brain, but it also offers a theoretical foundation for the diagnosis and treatment of brain illnesses. When discussing the anatomy of the brain in a clinical setting, the terms "white matter," "gray matter," and "cerebrospinal fluid" are the ones most frequently used (CSF). However, due to the fact that the human brain is highly complicated in its structure and that there are many different types of brain tissues, the human brain structure of each individual has its own set of distinctive qualities. Because of these several circumstances, the process of segmenting brain tissue will be challenging. In this article, several different clustering algorithms will be discussed, and their performance and effects will be compared to one another. The goal of this comparison is to determine which algorithm is most suited for segmenting MRI brain tissue. Based on the clustering method, the primary emphasis of this research is placed on the segmentation approach that is appropriate for medical brain imaging. The qualitative and quantitative findings of the experiment reveal that the FCM algorithm has more steady performance and better universality, but it is necessary to include the additional auxiliary conditions in order to achieve more ideal outcomes. [ABSTRACT FROM AUTHOR]

While geothermal energy is typically associated with beneficial environmental effects, it is crucial to acknowledge that it can lead to greenhouse gas emissions. Therefore, the implementation of geothermal systems should include an environmental assessment. This study presents and evaluates an innovative geothermal-powered bi-evaporator poly-generation system from seven perspectives: thermodynamic, extended-environmental, exergoenvironmental, exergoeconomic, net present value, and bi-objective optimization. The designed ploy-generation system comprises a desalination unit, a unit for extracting hydrogen, and a modified organic flash cycle, all integrated with a single geothermal flash cycle. These subsystems work in unison to create a comprehensive and integrated system for simultaneously generating purified water, green hydrogen, electricity, and meeting refrigeration load requirements. Regarding the findings at the base mode, the designed system is able to generate net power, refrigeration load, distilled water, and H 2 production rates of 244.9 kW, 741.4 kW, 0.4443 kg/s, and 1.062 kg/h, respectively. By employing NSGA-II optimization, the values of commodities are substantially increased, which enhanced the poly-generation system product cost and energetic efficiency by 7.13 % and 30.48 %. Also, the net electricity selling price significantly affects NPV, so the payback period reduces to 8.56 years. • Introducing a Bi-Evaporator Poly-Generation System (BEPGS) powered by geothermal energy. • Using extended- and exergo-environmental aspects is an innovative method first applied to BEPGS. • Selling price of the net electricity has a significant effect on the net present value (NPV). • With increasing booster pressure ratio, cooling capacity enhances by 195.40%. • The optimum value for the Cost of Poly-Generation System (CPGS) was 48.84 $/GJ. [ABSTRACT FROM AUTHOR]

Noncompliance, a common problem in randomized clinical trials (RCTs), can bias estimation of the effect of treatment receipt using a standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that would comply with their assigned treatment. Although several methods have been developed to estimate the CACE in analyzing a single RCT, methods for estimating the CACE in a meta-analysis of RCTs with noncompliance await further development. This article reviews the assumptions needed to estimate the CACE in a single RCT and proposes a frequentist alternative for estimating the CACE in a meta-analysis, using a generalized linear latent and mixed model with SAS software (SAS Institute, Inc.). The method accounts for between-study heterogeneity using random effects. We implement the methods and describe an illustrative example of a meta-analysis of 10 RCTs evaluating the effect of receiving epidural analgesia in labor on cesarean delivery, where noncompliance varies dramatically between studies. Simulation studies are used to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Noncompliance with assigned treatments is a common challenge in analyzing and interpreting randomized clinical trials (RCTs). One way to handle noncompliance is to estimate the complier-average causal effect (CACE), the intervention's efficacy in the subpopulation that complies with assigned treatment. In a two-step meta-analysis, one could first estimate CACE for each study, then combine them to estimate the population-averaged CACE. However, when some trials do not report noncompliance data, the two-step meta-analysis can be less efficient and potentially biased by excluding these trials. This article proposes a flexible Bayesian hierarchical CACE framework to simultaneously account for heterogeneous and incomplete noncompliance data in a meta-analysis of RCTs. The models are motivated by and used for a meta-analysis estimating the CACE of epidural analgesia on cesarean section, in which only 10 of 27 trials reported complete noncompliance data. The new analysis includes all 27 studies and the results present new insights on the causal effect after accounting for noncompliance. Compared to the estimated risk difference of 0.8% (95% CI: –0.3%, 1.9%) given by the two-step intention-to-treat meta-analysis, the estimated CACE is 4.1% (95% CrI: –0.3%, 10.5%). We also report simulation studies to evaluate the performance of the proposed method. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Thus for trial I i i with complete noncompliance data HT ht , one can separately estimate HT ht and obtain a standard error. If, instead, full compliance data were available for trial I i i randomization group I r i , then all observed counts HT ht follow a single multinomial distribution with probability HT ht , HT ht . [Extracted from the article]

Noncompliance to assigned treatment is a common challenge in analysis and interpretation of randomized clinical trials. The complier average causal effect (CACE) approach provides a useful tool for addressing noncompliance, where CACE is defined as the average difference in potential outcomes for the response in the subpopulation of subjects who comply with their assigned treatments. In this article, we present a Bayesian hierarchical model to estimate the CACE in a meta‐analysis of randomized clinical trials where compliance may be heterogeneous between studies. Between‐study heterogeneity is taken into account with study‐specific random effects. The results are illustrated by a re‐analysis of a meta‐analysis comparing the effect of epidural analgesia in labor versus no or other analgesia in labor on the outcome cesarean section, where noncompliance varied between studies. Finally, we present simulations evaluating the performance of the proposed approach and illustrate the importance of including appropriate random effects and the impact of over‐ and under‐fitting. [ABSTRACT FROM AUTHOR]

The correspondence between the decision space and the objective space is often many-to-one in multi-objective optimization problems. Therefore, a class of problems with such mapping relationships is defined as a MMOPs. For these problems, how to ensure the final solution converges to each Pareto solution set and guarantees the diversity of the algorithm is an urgent problem. The method of the paper with opposition-based strategy, a multimodal multi-objective optimization algorithm, is proposed. The algorithm proposed is called MMODE_OP, which is framed by a differential evolutionary algorithm, and opposition-based learning is applied to the initialization phase and generation-hopping phase to filter out the more promising individuals in the population for iteration to enhance the global search capability and the diversity of population. In addition, different Gaussian perturbation strategies are adopted with iteration to achieve the search of the neighborhood, which can further not only improve the quality of the Pareto solution set but also enable the convergence of the Pareto solution set quickly. This method improves the algorithm’s local and global search ability, and enables multiple the Pareto solution set and improving the convergence. In the meantime, adaptive scaling factors and crossover factors are designed in this paper to enhance the improved search capability. Finally, the experiment results of MMODE_OP and other excellent algorithms on 13 test problems corroborate the proposed methods have superior performance. [ABSTRACT FROM AUTHOR]

Background: Metarhizium rileyi is an important entomopathogenic fungus which has a wide range of host pests. In this work, the virulence of M. rileyi EFNL-08 against 3rd instar larvae of the fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), was quantified. Additionally, the infection process and aspects of the host immunological response were described in the laboratory. Results: Bioassay results proved that M. rileyi was highly virulent to 3rd instar S. frugiperda larvae with 98.33% mortality at 1 × 108 conidia/ml. Scanning electron microscopy was used to monitor morphological aspects of the infection process. When hyphal bodies (Hbs) of M. rileyi were injected into the hemocoel of host, they were not recognized by the host's immune system as invaders. There were nonsignificant differences in the hemocytes and phenoloxidase activity between the infected and control larvae at the initial 30 h, indicating that fungus successfully avoids the attack from the immune system. However, it inhibited the enzyme activity at the latest stage when Hbs reached a high density. After infection with M. rileyi, the activity of glutathione-S-transferase (GST) of S. frugiperda larvae presented a significant upward trend, while the activity of acetylcholinesterase (AchE) initially increased, followed by a decrease, and eventually stabilized. However, the activity of both detoxification enzymes in the control group showed slight fluctuations in the initial stage, and then stabilized at a low level. Conclusion: Obtained results provided new insights into the virulence and potential physiological responses of M. rileyi. This provides ideas for further development of novel and efficient entomopathogenic fungi (EPF) for pest control. [ABSTRACT FROM AUTHOR]

• Considering renewable energy resources in the presence electric vehicle. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. • Proposing an economic and environmental model for optimal energy management. • Considering demand side management base microgird. This paper suggests an optimal management for a microgrid containing renewable sources and electrical vehicles (EVs) with responsive loads for minimizing operation costs and emissions. The role of EVs is coping with peak load conditions while responsive loads can deal with uncertainties of renewable-based sources. A two-stage model is implemented for clearing the energy and reserve markets. The costs of generation and reserve power are minimized first, and then the costs of variations in the management owing to the changes in wind turbine and photovoltaic (PV) are optimized. To optimize the objective function, an improved shuffled frog leaping algorithm (ISFLA) is used. Simulation results are provided for different case studies in a microgrid and the outcomes confirm the superiority of the recommended algorithm in comparison with other conventional approaches. The use of EVs and responsive loads provides advantages such as minimizing the operational cost, emissions and the changing behavior of the PV and wind turbines. [ABSTRACT FROM AUTHOR]

• A novel tri-generation system of power, distilled water and hot water was proposed. • Groundnut shell biomass was co-combusted with synthesis gases exhausted from SOFC. • Central composite design was used to evaluate the system performance. • System performance was tri-objectively optimized by response surface methodology. Developing tri-generation energy systems is of interest, and in this regard, a novel tri-generation system of electrical power, distilled water and hot water was proposed in this study. The system composed of a solid oxide fuel cell, a combustion chamber, a supercritical carbon dioxide Brayton cycle, a desalination unit and a heat recovery system. Groundnut shell biomass was co-combusted with the synthesis gases exhausted from the solid oxide fuel cell and the resulted stream drove the supercritical carbon dioxide Brayton cycle and the desalination unit. Its waste heat was recovered to produce the hot water. Central composite design and analysis of variance were used to evaluate the system performance. Current density most contributed on the electrical power and the distilled water and the mass flow rate of biomass had the first rank on the hot water production. The electrical power and the distilled water were promoted when the current density increased and increasing mass flow rate of biomass boosted the hot water. The system performance was tri-objectively optimized using response surface methodology and the tri-generation system has 27.4% of electrical energy efficiency and 69.08% of thermal energy efficiency in the optimal conditions.The environmental assessment shows that the CO 2 emission rate of the system is 2653 kg/KWh. [ABSTRACT FROM AUTHOR]

[Display omitted] • The wheat yield sensitivity to different stages of growth and various variables has been investigated. • Growing degree days (GDD) and crop coefficient (Kc) were introduced as crop-specific variables. • The mid-season stage was identified as the best time window. • By using the proposed approach, wheat yield can be achieved before the harvest season. Cereals, especially wheat, have always been one of the main bases of the global food chain. Food security in the future will depend on more production or production with higher yields. So, it is critical to provide approaches to identify factors affecting wheat yield. Prior research has partially overcome the limitations of previous methods (field surveys, empirical statistical models, and crop models). A combination of climate and satellite inputs has been considered in previous research. But the main missing part is insufficient attention to crop-specific variables. A user-friendly interface is another issue that has been neglected because using satellite images requires time and expert effort. Here, to cover these gaps, the variables affecting the wheat yield in different stages of growth were identified and analyzed. The data of 17 years (2001–2017) from 31 provinces of Iran were used to evaluate the proposed approach. Six climatic variables (maximum and minimum temperature, relative humidity, wind speed, precipitation, and sunshine hours) from 238 weather stations and one variable for geographic yield bias (Y baseline) were considered. Also, growing degree days (GDD) and crop coefficient (K c) were introduced as crop-specific variables. First, the relative contribution of each variable was determined based on grey systems theory (GST) in different growth stages. Finally, the sensitivity analysis of input variables in each growth stage was performed based on eight scenarios and machine learning models, including artificial neural network (ANN), least squares support vector regression (LS-SVR), and adaptive neuro fuzzy inference system (NF). The results revealed that the mid-season stage suitable time window and the K c , maximum temperature, relative humidity, Y baseline , precipitation, and GDD with R2 = 0.958, WI = 0.989, and SI = 0.051 were the most influential variables in wheat yield estimation with ANN model. Estimating wheat yield before the harvest season can confirm the ability of the applied models in real-time applications. [ABSTRACT FROM AUTHOR]

Climatic changes, exhaustion of resources, air and water pollution are the detrimental consequences of mismanaging the use of fossil fuels. Likewise, the low performance of the traditional energy-conversion plants encourages scientists to replace these plants with hybrid systems. Accordingly, this study suggests a biomass-based combined system encompassing a gasifier, a gas turbine, a S–CO 2 unit, along with a hot water heater. This system will be analyzed from exergy, and economic point of view also using the genetic algorithm optimization tool multi-objective optimization is carried out. The parametric analysis to evaluate the influences of various variables on the plant operation. It was found that the changing r p of the compressor have a small impact on the efficiencies while it increases the total exergy efficiency and cost. The largest exergy destruction rate of the plant was for the compressor unit with 7391 kW. After that the combustion chamber with 2124 kW represents the worst performance from exergy destruction rate point of view. The optimization is done according to five decision parameters of moisture content, compressor pressure ratio, T 9 , T 14 , and P 36. The objective functions were energy efficiency, exergy destruction rate, and total product cost rate. According to the results of multi-aspect optimization in the optimum point the energy efficiency, exergy destruction and cost of electricity are 33.44%, 12448.1 kw and 130.57 $/h, respectively. • Introduce a multi-generation plant integrated with biomass-feed gasifier. • Gasifier among all components has the highest exergy destruction with 7391 kW. • ANN optimization of system suggests different optimize states for introduced plant. • The optimized system efficiency and cost rate determined as 33.44% and 130.57 $/h. [ABSTRACT FROM AUTHOR]

Graphene-like BC3 monolayer is a new two-dimensional nanomaterial with many unique properties, but is still largely unknown. First, we investigated the adsorption behaviors for different metal adatoms (Mo, Pd, Pt and Au) absorbed on pristine and defective BC3 (pri- and D-BC3) using first-principles calculations. It was found that the small barriers of the metal adatoms are more likely to diffuse on the pri-BC3 sheet; yet, the metal atoms doped BC3 systems (MD-BC3) exhibit high stability. Second, as O2 molecule is adsorbed more stably than CO, it regulates the electronic structures and magnetic properties of the MD-BC3 systems. Moreover, the possible reaction processes for CO oxidation on the Mo-BC3 substrate were systematically investigated using the Eley–Rideal (ER) and Langmuir–Hinshelwood (LH) mechanisms. For the ER mechanism, the preadsorbed O2 reacts with CO to form CO3 or the OOCO complex with smaller energy barrier (＜0.30 eV) than that of the LH reaction. In comparison, the dissociative adsorption of O2 on the Mo-BC3 sheet as the starting step is an energetically more favorable process and the generated Oads atoms easily interact with two CO molecules via the ER reactions. This study provides insights into the physical properties and chemical reactivity of BC3-based nanomaterials. [ABSTRACT FROM AUTHOR]

In recent decades, as a result of diminishing fossil fuel reserves and rising social concern, electric power systems as one of the largest sectors for environmental emissions, have had no choice but to search for alternatives to alleviate the environmental problems due to conventional generation system. Plug-in electric vehicles (PEVs) can cause difficulties in the electrical grid and system operation. To circumvent this issue, an efficacious stochastic optimization model is developed in this paper to enable the control entity to manage generation and storage assets by controlling the charging behavior of PEVs. To achieve the lowest total cost, a new strategy for reducing reliability costs is proposed. In this regard, vehicle-to-grid (V2G) tool is used to reduce the overall system cost. The presented energy management model for the MG in the grid-connected mode takes into consideration the uncertainty of output power of the wind turbine (WT) and the photovoltaic (PV), as well as the PEVs' charging and discharging. In this study, an innovative and effective optimization algorithm known as the modified fluid search optimization algorithm (MFSO) is utilized to optimize the MG operation problem. The results show that the suggested model incorporating electric vehicles (EVs) can enable MG operation at the minimum cost and reliability. As can be observed, the amount of time required by the presented method to solve the first case problem is 6.64s, which is significantly less than the amount of time required by the other methods. In a similar vein, the MFSO method is noticeably quicker than both GA and PSO when applied to case studies 2 and 3. [Display omitted] • Proposing economic model for optimal energy management. • Considering renewable energy resources in the presence of Electric Vehicles. • Presenting modified fluid search optimization to solve the proposed model. • Reporting superior solutions in Short-Term Scheduling of Micro-grid. • Proposing optimal size of Fuel cell with electricity price. [ABSTRACT FROM AUTHOR]

• A novel poly-generation system using solar and biogas heat sources. • Exergy-economic and environmental analyses are done through a parametric study. • The solar power tower is the most influential subsystem in terms of exergy destruction and cost. • The two-objective optimization results in the exergy efficiency and total cost rate of 27.96%, and 5686.23 $/h, correspondingly. In the present theoretical study, a steam Rankine cycle (SRC) and a modified organic flash cycle (MOFC) are used as bottoming systems for a gas turbine cycle (GTC). Then, the waste energy of the compression stage of the GTC is recuperated by a hot water unit, and the waste energy of the SRC and MOFC is recovered in a single effect LiCl-H 2 O absorption chiller and a thermoelectric generator, correspondingly. Furthermore, the power produced in the SRC and MOFC is transmitted to a proton exchange membrane electrolyzer and a reverse osmosis desalination unit to produce hydrogen and freshwater. Thus, the system is converted into a ploy-generation layout. In the parametric study section, the effects of changes in the pressure ratio of the GTC, inlet temperature of the solar power tower, number of heliostat mirrors, methane content of biogas, inlet temperature of the gas turbine, and direct normal irradiance on the energy, exergy, exergy-economic, and environmental output parameters are investigated. The results show that solar power tower is the most influential component in the exergy destruction and cost rates. Furthermore, the exergy efficiency, total cost rate, the unit cost of products, and payback period are obtained as 24.34 %, 6390 $/h, 20.17 $/GJ, and 0.687 years in the base case. Considering exergy efficiency and total cost rate as the objective functions, these two parameters improved by 14.87 % and 11.01 % in the optimum case, respectively. [ABSTRACT FROM AUTHOR]

World energy markets are gradually shifting in favor of wind-based power plants. A decrease in global warming can be attributed to the reduction of dependence on fossil fuels as a result of wind energy. In this regard, utilizing the waste heat from the generator of a wind turbine has recently been considered and only a few studies have been done about it. This shortage has made clear the need for further studies. In the present research, a novel poly-generation system is introduced based on the waste heat recovery of a wind turbine to fill this research gap because the waste energy of a wind turbine has not been utilized for the poly-generation purpose. A novel power-cooling production absorption cycle and a domestic hot water heat exchanger are utilized for this purpose. Also, the produced power in the absorption cycle is sent to a reverse osmosis desalination unit for freshwater production. The considered system is comprehensively studied from energy, exergy, environmental, and exergo-economic standpoints. In the average wind speed of 10 m/s, the exergy efficiency, unit cost of products, reduction in CO 2 emission rate, and payback period are seen to be 36.76%, 20.25 $/GJ, 2452801 kg/year, and 2.301 years, which shows its performance is better than the previous studies based on the waste heat recovery of a single wind turbine. • Waste heat recovery of a wind turbine for poly-generation purpose. • Energy and exergo-economic analyses are done through parametric study. • Wind turbine is the major contributor in exergy destruction and cost rates. • This waste heat recovery produces 73.25 kW heating, 45.86 kW cooling, and 0.274 kg/s of freshwater. [ABSTRACT FROM AUTHOR]

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Photovoltaic and battery. • Presenting Hybrid Whale and Pattern Search Algorithm for optimizing Micro-grid. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. Energy resource scheduling is one of the major problems in power systems. This issue has become even more significant as renewable sources with intermittent power output are becoming more and more prevalent. Due to their low environmental impact and low operating costs, renewable energy sources (RESs) have attracted interest. The excess power generated by such generation methods may be a negative that affects power systems, hence issues relating to such systems should be properly handled. The optimal operation of a microgrid (MG) with several distributed generation (DG) units and uncertain behavior of RESs is suggested in this research using a stochastic optimization approach. So, for an MG fitted with a solar photovoltaic (PV) unit, this research proposes a day-ahead scheduling paradigm. In this regard, the effects of various climatic circumstances on the power production of the PV unit and the optimal scheduling of the MG have been examined in this research. In order to achieve this, statistics on solar irradiance were extracted from four distinct days from each of the four seasons. The single-objective optimization framework used to design the scheduling problem states that the objective function should be to minimize the total operating cost across the scheduling period. The aforementioned day-ahead scheduling problem can be solved by the "hybrid whale optimization algorithm and pattern search (HWOA-PS)" optimization algorithm, while both renewable and nonrenewable generating units, as well as an energy storage system are present. In order to confirm the higher performance of the recommended approach, a thorough comparison between the Hybrid WOA-PS algorithm and a few well-known optimization algorithms has also been conducted. [ABSTRACT FROM AUTHOR]

Intuitive context-aware algorithms called "Next Basket recommender systems" improve consumers' decision-making by offering suggestions for potential products they might like to buy next based on their past behaviour. Even though it is now well-known, this field is still young. Many different industries, such as e-commerce and healthcare, employ recommender systems. Even though these datasets frequently contain sensitive data, most recommender systems place a greater emphasis on the models' accuracy than on their security and privacy. We investigate this concept in the context of the sequential recommendation job known as Next Basket Recommendation (NBR), whose objective is to provide a user with a selection of goods based on their purchasing behaviour. A recent state-of-the-art technology is blockchain. Blockchain creates a trusted environment without the involvement of a third party, thus ensuring privacy and security. It is designed in such a way that it is enough in itself to create trust. In this paper, we propose and assimilate an authentic blockchain privacy system for a fortified user recommendation system for the Next Basket Recommendation. With next basket proposals based on blockchain for safe transactions and distributed context-based processing, this suggested system enables the development of decentralized RSs. Through the use of atomic swaps on the blockchain, this effort aims to provide viable solutions that can lead to fair procedures. It places a focus on effective data deletion procedures that preserve user privacy and transfers the issue of decremental learning to the Next Basket system, a more secure and wise recommendation. By incorporating blockchain into recommender systems (RSs), which contain smart contracts in the main blockchain-based RS protocol, it is feasible to create safe trust-based systems with the benefit of multi-party computation backed by blockchain. Additionally, it aids in protecting user information since blockchain enables the secure processing of customer data in online portals. [ABSTRACT FROM AUTHOR]

Due to its technical characteristics and economic benefits, the use of the photovoltaic (PV) system has become very common in the world. Nevertheless, the intermittent behavior of solar systems has created challenges for power grids due to their strong dependence on radiation and temperature. Moreover, PV systems must be operated at their maximum power so that they can return the investment costs in less time and also have higher efficiency. As a result, it is crucial to propose new maximum power point tracking (MPPT) methods to track the maximum power in conditions of uncertainty. To cope with this challenge, the fuzzy logic controller (FLC) is very effective approach. This study suggests the FCL for the MPPT algorithm for an isolated PV system using a push-pull converter. The suggested technique is optimized using the improved shuffled frog leaping algorithm (ISFLA) to improve the current MPPT controllers. To deal with the intermittent behavior of solar systems during peak load conditions, Grid is used to respond to the essential load. Fast convergence and low power fluctuations are the advantages of the recommended algorithm in tracing the MPP. The MATLAB/SIMULINK software is used to confirm the effectiveness of the recommended scheme. According to the simulation results, the PV system efficiency is increased to 99.7%. [ABSTRACT FROM AUTHOR]

To estimate causal effects of vaccine on post-infection outcomes, Hudgens and Halloran (2006) defined a post-infection causal vaccine efficacy estimand VEI based on the principal stratification framework. They also derived closed forms for the maximum likelihood estimators of the causal estimand under some assumptions. Extending their research, we propose a Bayesian approach to estimating the causal vaccine effects on binary post-infection outcomes. The identifiability of the causal vaccine effect VEI is discussed under different assumptions on selection bias. The performance of the proposed Bayesian method is compared with the maximum likelihood method through simulation studies and two case studies - a clinical trial of a rotavirus vaccine candidate and a field study of pertussis vaccination. For both case studies, the Bayesian approach provided similar inference as the frequentist analysis. However, simulation studies with small sample sizes suggest that the Bayesian approach provides smaller bias and shorter confidence interval length. [ABSTRACT FROM AUTHOR]

Simple Summary: Bemisia tabaci is a globally notorious agricultural pest due to its highly polyphagous characteristic, its composition as a cryptic species, and its transmission of plant viruses. The whitefly has endosymbiotic bacteria in cells inside their bodies that have strong effects on the host biology. Generally, the geographical distribution of these cryptic species and their endosymbionts' infection patterns are related to ecological factors. In this study, we determined eight cryptic species (MED, MEAM1, Asia I, Asia II 1, Asia II 2, Asia II 6, China 1, and China 6) from 29 geographical localities across China. Native cryptic species were still widespread in the Yangtze River Valley and eastern coastal areas. In addition, we found that the cryptic species distribution and their facultative endosymbionts' infection patterns were closely associated with the geographical and environmental factors, i.e., latitude and annual mean temperature. This work highlights that range expansion of invasive species and host–endosymbiont interactions need to be studied within an environmental and geographic context. The sweetpotato whitefly, Bemisia tabaci species complex, comprises at least 44 morphologically indistinguishable cryptic species, whose endosymbiont infection patterns often varied at the spatial and temporal dimension. However, the effects of ecological factors (e.g., climatic or geographical factors) on the distribution of whitefly and the infection frequencies of their endosymbionts have not been fully elucidated. We, here, analyzed the associations between ecological factors and the distribution of whitefly and their three facultative endosymbionts (Candidatus Cardinium hertigii, Candidatus Hamiltonella defensa, and Rickettsia sp.) by screening 665 individuals collected from 29 geographical localities across China. The study identified eight B. tabaci species via mitochondrial cytochrome oxidase I (mtCOI) gene sequence alignment: two invasive species, MED (66.9%) and MEAM1 (12.2%), and six native cryptic species (20.9%), which differed in distribution patterns, ecological niches, and high suitability areas. The infection frequencies of the three endosymbionts in different cryptic species were distinct and multiple infections were relatively common in B. tabaci MED populations. Furthermore, the annual mean temperature positively affected Cardinium sp. and Rickettsia sp. infection frequencies in B. tabaci MED but negatively affected the quantitative distribution of B. tabaci MED, which indicates that Cardinium sp. and Rickettsia sp. maybe play a crucial role in the thermotolerance of B. tabaci MED, although the host whitefly per se exhibits no resistance to high temperature. Our findings revealed the complex effects of ecological factors on the expansion of the invasive whitefly. [ABSTRACT FROM AUTHOR]

Power generation of a fuel cell (FC) is mostly dependent upon operational variables such as cell temperature and membrane water content. There is an individual maximum power point (MPP) on the P-I curve of the FC. The location of the MPP varies with respect to the MPP position. Thus, an MPP tracking (MPPT) system should exist to guarantee that the FC works at the MPP in order to maximize the functionality. Due to their straightforward structure, prevalent MPPT methods had strong functionality. However, their primary limitations include fluctuations around the MPP and inefficiency under abrupt variations of operating conditions. The primary objective of this paper is to maintain the PEMFCs operation at an efficient power point. To this purpose, the efficiency of PEM-FC is tested and enhanced using a variety of MPPT-based smart controller techniques. To determine the appropriate MPPT controller parameters, the modified fluid search optimization (MFSO) approach and fuzzy logic controller (FLC) are employed. Furthermore, the MFSO method is deployed to adjust the membership functions (MFs) of the FLC. The MFSO is an excellent approach for coping with the stochastic behavior of the PEM-FC system when the temperature and water content of the membrane change. In terms of improved dynamic behavior, better convergence rate, reduced oscillations, and better tracking of the MPP, the results obtained by employing the suggested strategy demonstrate the superior functionality of the system compared to case using other methods. Moreover, the power generated by the PEMFC system is less than the nominal capacity for the temperature's rated capacity. Therefore, the deficit in power would be covered by transacting power with the grid. • A new design is derived to fine-tune the member functions (MFs) of PEMFC corresponding to MPP. • A novel strategy based on Modified Fluid Search Optimization Algorithm and FLC for extracting the MPP of PEMFC is developed. • Comparative analysis is made between proposed Modified Fluid Search Optimization Algorithm than the other methods. • Increasing the precision and response speed of the method in different conditions. [ABSTRACT FROM AUTHOR]

As a biocontrol agent of aphids and coccids, the harlequin ladybird, Harmonia axyridis (Pallas) (Coleoptera: Coccinellidae), was introduced from its native Asian range into North America. There, it has spread quickly and widely, potentially contributing to the decline of endemic ladybirds. This study examined microbial communities residing in H. axyridis in its native and introduced or invaded regions. We collected ladybirds at 11 localities across mainland China (native range) and at seven localities across the USA (invaded range) and applied next‐generation amplicon sequencing to examine microbial communities. We found that the diversity of the microbial community of H. axyridis in the invaded range declined compared to the native range. The microbial community differed markedly in composition between native and invaded ranges. Among all identified 22047 amplicon sequence variants, only 4.2% were shared by the two ranges, whereas 4967 were unique to the invaded and 16159 to the native range. Our findings suggest that the composition of the microbial community of H. axyridis is largely restructured in the invaded North American region compared to the native Asian region. [ABSTRACT FROM AUTHOR]

The formation and evolution of the Jiangnan orogen at the southeastern margin of the Yangtze Block, South China, are an important and debatable topic. The Meso- to Neoproterozoic basic–acid rocks from Hunan Province record the history of the western Jiangnan orogen in the area. The Mesoproterozoic basalts and diabases from Nanqiao are the typical N-MORB, having very low K2O, low incompatible HFSE and REE, and depleted εNd (T) values (+6.86 to +8.98). They may represent the fragments of an obducted oceanic crust or the relicts of the oceanic crust in a “forearc basin” along an ancient subduction zone, which provides new evidence for the existence of the Jiuling Old Island Arc. The Mesoproterozoic komatiitic basalts from Yiyang are high in Al2O3/TiO2, MgO, Ni and Cr, and are depleted in Nb and Ti. These plume-derived magmas are associated with island-arc tholeiites and exhibit the geochemical characteristics of the arc magma, suggesting that the Mesoproterozoic komatiitic basalts might be the products of the plume–arc interaction. The Neoproterozoic andesitic rocks from Baolinchong, with strong depletions of Nb, Ti and enrichment of LILEs, are of typical island–arc volcanic origin. The Neoproterozoic granites from northeastern Hunan are strongly peraluminous (SP) granites, with high ASI (>1.1) and high CaO/Na2O ratio (>0.3), suggesting that they might be derived from the partial melting of the psammitic sources in the Mesoproterozoic Lengjiaxi Group. The field geological, chronological, petrographical, geochemical, and isotopic features of these granites indicate that they are the products of post-collisional magmatism related to the breakoff of subducted slab, similar to the andesitic rocks from Baolinchong. The Neoproterozoic basic rocks from the western and central Hunan have the geochemical signatures of rifting basalts, with normalized patterns similar to ocean island basalts (OIB). Their occurrence in intraplate extensional environments is considered as indicators of the rifting of the area. Both the distribution style and field geological evidence of these Neoproterozoic basic rocks are not in line with a mantle plume model. Based on geochemical and petrological studies of the Meso- to Neoproterozoic basic–acid rocks, a preliminary model for the formation and evolution history of the western Jiangnan orogen in the area was put forward. [Copyright &y& Elsevier]

Explores the mineralogical, petrological and geochemical evidence about the Mesozoic crust-mantle interaction in the coastal area of Zhejiang-Fujian, China. Lithogeochemical features of composite lavas, dikes and plutons; Degree of enrichment of upper mantle source for late Mesozoic basalts; Exchange and recycle of materials between crust and mantle.

Cracks are one of the most common types of imperfections that can be found in concrete pavement, and they have a significant influence on the structural strength. The purpose of this study is to investigate the performance differences of various spatial clustering algorithms for pavement crack segmentation and to provide some reference for the work that is being done to maintain pavement currently. This is done by comparing and analyzing the performance of complex crack photos in different settings. For the purpose of evaluating how well the comparison method works, the indices of evaluation of NMI and RI have been selected. The experiment also includes a detailed analysis and comparison of the noisy photographs. According to the results of the experiments, the segmentation effect of these cluster algorithms is significantly worse after adding Gaussian noise; based on the NMI value, the mean-shift clustering algorithm has the best de-noise effect, whereas the performance of some clustering algorithms significantly decreases after adding noise. [ABSTRACT FROM AUTHOR]