Search

Introduction/purpose: Cutting is a typical material process. However, an appropriate solution for simultaneous optimization of material machinability and tool life in material cutting processes has not been obtained yet. In this article, probabilistic multi-objective robust design (PMORD) is expounded and the robust design problem in the simultaneous optimization of material machinability and tool life is analyzed by taking the machining of ferrite-bainite dual-phase steel as an example. Methods: According to PMORD, the arithmetic mean and its deviation of various performance and utility indexes of alternative schemes are evaluated as twin independent responses, which respectively contribute a part of the partial preferable probability to the performance indexes. In the evaluation, the arithmetic average of the utility index is taken as the representative of the utility attribute, and the evaluation is made in accordance with the function or preference of the utility index. However, the deviation term is generally used as an unbeneficial type of the index (that is, the smaller the better) to participate in the evaluation. The product of the two parts of partial preferable probability is the actual partial preferable probability of the corresponding performance index. The product of the partial preferable probability of all performance utility indexes gives the total preferable probability of the corresponding scheme, which is the only index for each scheme to participate in the competition in robust design. Results: The optimization result of this example is that the tool life is 1297.3333 s (standard deviation is 2.0817 s) and the surface roughness is 2.22mm (standard deviation is 0.2mm), while the corresponding working conditions are that the heat treatment temperature of the material is 790°C, the cutting speed is 150m/min, the feed speed is 0.15mm/rev, and the cutting depth is 0.2mm. Conclusion: The example of the parameter optimization of cutting of ferrite-bainite dual-phase steel by means of PMORD indicates the rationality of the appropriate solution.

In the present paper, a rational fuzzy multi-objective optimization for material selection is developed in respect of probabilistic method for multi-objective optimization, which agrees with the viewpoint of system theory for the whole optimization of a system and is the novelty of this work. The basic ideas and algorithms of fuzzy theory together with probability theory are taken as the cornerstone to perform the formulation. In the treatment, the intersection of the membership function of fuzzy numbers of alternative material performance and the membership function of fuzzy numbers of desired material performance is used as the utility of the material performance index. Thereafter, the utility of each material performance index is further used to conduct the assessment of its partial preferable probability and formulate the multi-objective optimization by means of probability theory. Moreover, a typical example is presented to provide the rational process of the probabilistic fuzzy multi-objective optimization for material selection.

Material processing involves many factors, and evaluation of final quality of a product also relates to many indexes from different respects. Thus the optimization of qualities and processing of a product is an optimization problem with many objectives (MOO) inevitably. Although there are some approaches proposed to deal with optimization problem with many objectives in nowadays, the inherent shortcomings in these approaches make them puzzled, which include their missing standpoint and use of additive algorithm containing subjective factors. Currently, an "intersection" methodology for optimization with many objectives is proposed by initiating a novel idea of favorable probability to depict the favorite degree of an alternative in optimal option impersonally, which aims to characterize the concurrent optimization of many objectives in a system in spirits of probability theory and set theory. In this article, some regulations are put forward for performing optimal option of material processing parameters in designed experiments of response surface methodology, orthogonal experiment design and uniform experiment design by means of the total / global favorable probability. In the treatment, the total / global favorable probability of an alternative is the decisive indicator in the optimal option uniquely, which transfers the optimization problem with many objectives into a mono-objective one. The result indicates that the novel approach can be employed to deal with the optimal problem of designed experiments for material processing rationally.

Introduction/purpose: Turning is a typical machining process. However, an appropriate solution for a concurrent optimization of minimizing surface roughness, minimizing cutting forces and vibrations, and maximizing the material removal rate in turning processes has not been found yet. This article formulates the rule of separating an independent attribute from multiple attributes by using the linear correlation coefficient in the spirit of the cluster analysis first. Moreover, the evaluation of the concurrent optimization of steel turning by means of probability - based multi - objective optimization (PMOO) is taken as an example to show the procedure including the separation of an independent attribute from multiple attributes by using PMOO. Methods: PMOO is a promising solution for turning processes. It is necessary to have an independent attribute in the evaluation of PMOO to be analogical as an independent event in the view of the probability theory. The separation of an independent attribute from multiple attributes by using the linear correlation coefficient is conducted in the spirit of the cluster analysis. It further assumes that if the linear correlation coefficient of two attributes in the cluster analysis is higher than 0.8, i.e., in case of very strong correlation, then they could be put into one category, and only one of them could be taken as an independent attribute to join the evaluation of PMOO. Results: The formulation reflects the essence of PMOO and its application in material machining rationally, which opens a new way for solving the relevant problem. The example of the parameter optimization of steel turning by means of PMOO indicates the rationality of the appropriate solution. Conclusion: This innovative study has practical significance of making the utilization of PMOO method reasonable by providing a rational rule of separating independent attributes from multiple attributes of PMOO.

Introduction/purpose: In this paper, a new approach to solving the portfolio investment problem is formulated to handle simultaneous optimization of both maximizing the rate of return and minimizing the variance of the rate of return. Probability - based multi – objective optimization is combined with uniform design for experiments with mixtures to conduct processing. Methods: Preliminarily, probability - based multi – objective optimization is employed to synthesize the bi-objective problem of simultaneous optimization of both maximizing the rate of return and minimizing the variance of the rate of return into a single objective one of the total preferable probability of each alternative scenario. The total preferable probability is the product of all partial preferable probabilities of each performance utility; subsequently, the method of uniform design for experiments with mixtures is used to create a set of effective sampling points for the portfolio investment problem to provide discretization in data processing and simplifying treatment, of which the proportion xi follows the constraint condition of xl + x2 + x3．．．+ xs = 1 with the total number of variables s for xi. Results: The new approach is used to deal with the portfolio Investment problem that is, in essence, simultaneous optimization of both maximizing the rate of return and minimizing the variance of the rate of return, which leads to reasonable consequences. The results are with the quality of rationality from the respect of the probability theory for simultaneous optimization of multiple objectives. Conclusion: This method naturally reflects the essence of the portfolio investment problem and opens a new way of solving the relevant problem.

Introduction/purpose: In this paper, a new solution for solving a multiobjective integer programming problem with probabilistic multi – objective optimization is formulated. Furthermore, discretization by means of the good lattice point and sequential optimization are employed for a successive simplifying treatment and deep optimization. Methods: In probabilistic multi – objective optimization, a new concept of preferable probability has been introduced to describe the preference degree of each performance utility of a candidate; each performance utility of a candidate contributes a partial preferable probability and the product of all partial preferable probabilities deduces the total preferable probability of a candidate; the total preferable probability thus transfers a multi-objective problem into a single-objective one. Discretization by means of the good lattice point is employed to conduct discrete sampling for a continuous objective function and sequential optimization is used to perform deep optimization. At first, the requirements of integers in the treatment could be given up so as to simply conduct above procedures. Finally, the optimal solutions of the input variables must be rounded to the nearest integers. Results: This new scheme is used to deal with two production problems, i.e., maximizing profit while minimizing pollution and determining a purchasing plan for spending as little money as possible while getting as large amount of raw materials as possible. Promising results are obtained for the above two problems from the viewpoint of the probability theory for simultaneous optimization of multiple objectives. Conclusion: This method properly considers simultaneous optimization of multiple objectives in multi-objective integer programming, which naturally reflects the essence of multi-objective programming, and opens a new way of solving multi-objective problems.

Introduction/purpose: New robust design by means of probability-based multi-objective optimization takes the arithmetic mean value of the performance indicator and its deviation as twin independent responses of the performance indicator. The aim of this article is to check the applicability of new robust design in optimizing machining process parameters. To conduct the examination in detail, the robust design for optimal cutting parameters to minimize energy consumption during the turning of AISI 1018 steel at a constant material removal rate is applied as well as the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Methods: In the spirit of the probability-based method for multi-objective optimization, the arithmetic mean value of the performance indicator and its deviation are taken as two independent responses of the performance indicator to implement robust design. Each of the above twin responses contributes one part of the partial preferable probabilities to the performance indicator of the alternatives in the treatment. The arithmetic mean value of the performance indicator should be assessed as a representative of the performance indicator according to the function or the preference of the performance indicator, and the deviation is the other index of the performance indicator, which has the characteristic of the 99 smaller-the-better in general. Furthermore, the square root of the product of the above two parts of the partial preferable probability forms the actual preferable probability of the performance indicator. Moreover, the product of partial preferable probabilities gives the total preferable probability of each alternative, which is the overall and unique index of each alternative in the robust optimum. Results: The paper gives the rational optimum cutting parameters for minimizing energy consumption during the turning of AISI 1018 steel at a constant material removal rate and the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Conclusion: The application study indicates its rationality and convenience of new robust optimization in the optimization of machining process parameters.

In this article, the error analysis for designed test and definite integral in employing uniform experimental design is analogically developed on basis of midpoint rule in rectangle method for assessing definite integral through conducting discretized sampling approximately. It concluded that the discrete sampling-point by means of good lattice point in the evaluations of definite integral and maximum value of a function is promised with higher accuracy, and the predicted entire error of this uniform sampling point method for above problems decreases with the number of sampling points significantly.

For multi-objective optimization under condition of complicated objective function, the data processing in the evaluation is sometime tediously long, special algorithm is needed to be adopted. Since the remarkable features of uniform distribution of test points within the test domain and the small number of tests, fully representative of each point, and easy to perform regression analysis, the uniform test design method is hybrid with the “intersection” method for multi-objective optimization to simplify the complicated data process in evaluation first. Furthermore, the "intersection" multi-objective optimization methodology is combined with sequential uniform design so as to get a more precise approximation for solving multi-objective optimization problem, the procedure for searching optimum of the “intersection“ multi-objective optimization methodology with sequential uniform design algorithm is put forward. A multi-objective optimization of linear programming problem with three variables is taken as our example, which involves a maximum for one objective and a minimum for another objective. The result for applying the novel approach to the example indicates the effectiveness of current hybrids.

In this paper, an approach to deal with the multi-objective programming problem is regulated by means of probability-based multi-objective optimization, discrete uniform experimental design, and sequential algorithm for optimization. The probability-based method for multi-objective optimization is used to conduct conversion of the multi-objective optimization problem into a single-objective optimization one in the viewpoint of probability theory. The discrete uniform experimental design is used to supply an efficient sampling to simplify the conversion. The sequential algorithm for optimization is employed to carry out further optimization. The corresponding treatments reveal the essence of the multi-objective programming, and consideration of the simultaneous optimization of each objective of multi-objective programming problem rationally. Two examples are conducted to illuminate the rationality of the approach.

Transportation process or activity can be considered as a multi-objective problem reasonably. However, it is difficult to obtain an absolute shortest path with optimizing the multiple objectives at the same time by means of Pareto approach. In this paper, a novel method for solving multi-objective shortest path problem in respect of probability theory is developed, which aims to get the rational solution of multi-objective shortest path problem. Analogically, each objective of the shortest path problem is taken as an individual event, thus the concurrent optimization of many objectives equals to the joint event of simultaneous occurrence of the multiple events, and therefore the simultaneous optimization of multiple objectives can be solved on basis of probability theory rationally. The partial favorable probability of each objective of every scheme (routine) is evaluated according to the actual preference degree of the utility indicator of the objective. Moreover, the product of all partial favorable probabilities of the utility of objective of each scheme (routine) casts the total favorable probability of the corresponding scheme (routine), which results in the decisively unique indicator of the scheme (routine) in the multi-objective shortest path problem in the point of view of system theory. Thus, the optimum solution of the multi-objective shortest path problem is the scheme (routine) with highest total favorable probability. Finally, an application example is given to illuminate the approach.

Introduction/purpose: In this paper, probability based multi – objective optimization (PMOO) is employed to objectively study the optimization problems of the drug encapsulation of water-soluble chitosan (WSC) / poly – gama - glutamic acid (-PGA) - tanshinone IIA (TA) with a response surface design and glycerosome - triptolide with an orthogonal experimental design. Methods: In PMOO, a concept of preferable probability has been introduced to describe a preference degree of the performance utility. Each beneficial and unbeneficial utility index contributes a partial preferable probability in a linear manner, positively and negatively, respectively and all the performance utility indicators are simultaneously and equally treated. The total preferable probability of a candidate is the product of all partial preferable probabilities, which thus transfers a multi-objective problem into a single-objective one. Results: 1. The optimal encapsulation of WSC / -PGA - TA is for WSC of 5.755 mgml−1, TA of 1.0275 mgml−1,when the ratio of TA to the carrier material is 1: 4.9, and the reaction time is 1.302h. 2. The optimal preparation conditions of glycerosomes – triptolide are a glycerol concentration of 20%, the phospholipid to cholesterol mass ratio of 30:1 and the phospholipid to triptolide mass ratio of 5:1. Conclusion: The results show the applicability of PMOO in the optimization of encapsulation composites with designed tests.

Introduction/purpose: An approximation for assessing a definite integral is continuously an attractive topic owing to its practical needs in scientific and engineering areas. An efficient approach for preliminarily calculating a definite integral with a small number of sampling points was newly developed to get an approximate value for a numerical integral with a complicated integrand. In the present paper, an efficient approach with a small number of sampling points is combined to the novel probability– based multi–objective optimization (PMOO) by means of uniform experimental design so as to simplify the complicated definite integral in the PMOO preliminarily. Methods: The distribution of sampling points within its single peak domain is deterministic and uniform, which follows the rules of the uniform design method and good lattice points; the total preferable probability is the unique and deterministic index in the PMOO. Results: The applications of the efficient approach with finite sampling points in solving typical problems of PMOO indicate its rationality and convenience in the operation. Conclusion: The efficient approach with finite sampling points for assessing a definite integral is successfully combined with PMOO by means of the uniform design method and good lattice points.

Introduction/purpose: The newly developed probability-based multi – objective optimization (MOO) has introduced a novel concept of preferable probability to represent a preferability degree of a candidate in optimization in order to overcome the inherent shortcomings of subjective and “additive” factors in the previous MOO methods. In this paper, the new method is extended to include robust optimization for material engineering. Furthermore, energy consumption in a melting process with orthogonal array design and the robust optimization of four different process schemes in machining an electric globe valve body are taken as examples. Methods: The arithmetic mean value of each performance utility indicator of the candidate contributes to one part of the partial preferable probability, while the deviation of each performance utility indicator from its arithmetic mean value of the candidate contributes to the other part of the partial preferable probability quantitatively. Furthermore, following the procedures of the newly developed probability-based multi–objective optimization (PMOO), the total preferable probability of a candidate is obtained, which thus transfers a multi–objective optimization problem into a single– objective optimization problem. Results: The optimal control factors of lower electric energy consumption with robustness are bundled steel, loose steel, and uncleaned steel of 12.5%, 50% and 37.5% by weight, respectively, in this steel melting process. This case is closely followed by the scenario of 50 wt% bundled steel, 50 wt% loose steel, and 0 wt% uncleaned steel. The robust optimization of four different process schemes for machining an electric globe valve body is scheme No. 1. Conclusion: The extension of probability-based multi-objective optimization while considering robustness is successful, which can be easily used to deal with the optimal problem with dispersion of data to get objectively an optimal result with robustness in material engineering. The extension of probability-based multi-objective optimization while considering robustness will be beneficial to relevant research and process optimization.

Introduction/purpose: An approximate approach to definite integral calculation has been an attractive problem continuously since the creation of integration due to practical needs in scientific and engineering areas. In most practical cases, the integrand is complex, which leads to a difficulty of obtaining an exact value of integration, so an approximate value of the definite integral with certain accuracy is satisfactory for practical applications. In this paper, an efficient approach for calculating a definite integral with a small number of sampling points is proposed based on the uniform design method from the viewpoint of practical application. Methods: The distribution of sampling points in its single peak domain is deterministic and uniform, which follows the rule of the uniform design method and good lattice points. Results: The efficient evaluation of a definite integral for a periodical function in its single peak domain can be obtained by using 11 sampling points in one dimension, 17 sampling points in two dimensions, and 19 sampling points in three dimensions. Conclusion: The efficient approach for a definite integral developed here on the basis of the uniform test design method is promised from the viewpoint of practical application; the sampling points are deterministically and uniformly distributed according to the rule of the uniform design method and “good lattice points”. The efficient approach developed in this article will be beneficial to relevant research and application.

Introduction/purpose: Althought many methods have been proposed to deal with the problem of material selection, there are inherent defects of additive algorithms and subjective factors in such algorithms. Recently, a probability-based multi–objective optimization was developed to solve the inherent shortcomings of the previous methods, which introduces a novel concept of preferable probability to reflect the preference degree of the candidate in the optimization. In this paper, the new method is utilized to conduct an optimal scheme of the switching material of the RF-MEMS shunt capacitive switch, the sintering parameters of natural hydroxyapatite and the optimal design of the connecting claw jig. Methods: All performance utility indicators of candidate materials are divided into two groups, i.e., beneficial or unbeneficial types for the selection process; each performance utility indicator contributes quantitatively to a partial preferable probability and the product of all partial preferable probabilities makes the total preferable probability of a candidate, which transfers a multi–objective optimization problem into a single–objective optimization one and represents a uniquely decisive index in the competitive selection process. Results: Cu is the appropriate material in the material selection for RF - MEMS shunt capacitive switches; the optimal sintering parameters of natural hydroxyapatite are at 1100C and 0 compaction pressure; and the optimal scheme is scheme No 1 for the optimal design of a connecting claw jig. Conclusion: The probability-based multi-objective optimization can be easily used to deal with an optimal problem objectively in material engineering.

This paper aims to develop the extension of intersection method for multi-objective optimization under condition of interval number. Based on the linear correlation of partial favourable probability and the corresponding performance indicator, and the assumption of uniform distribution of the actual value of performance indicator within the range of its lower and upper limits in case of interval number, it derives that the actual partial favourable probability of a performance indicator is the arithmetic mean value of the partial favourable probabilities of the arithmetic mean value and the variation value of the interval index of the corresponding performance indicator for each candidate, or their desired sum. Furthermore, according to the rule of algorithm for the total favourable probability quantitatively, all candidates are ranked according to their total favourable probabilities to complete the multi- objective optimization in case of interval number. As applications, the quantitative assessments of multi-criteria selections for effective dwelling house walls, project managers and contractor for construction works are given in detail, satisfied results are obtained.

Recently, a new "intersection" method for multi-objective optimization was developed in the points of view set theory and probability theory, which introduces a new idea of favorable probability to reflect the favorable degree of the utility of performance indicator in multi-objective optimization, and the product of all partial favorable probabilities of entire utilities of performance indicators makes the overall / total favorable probability of the candidate. Here, in this paper, the new "intersection" algorithm for multi-objective optimization is combined effectively with response surface methodology (RSM) by taking each response as one objective, which transfers the multi-response optimization problem into a single response one with the help of the overall / total favorable probability of each scheme. The overall / total favorable probability is the uniquely decisive index of the scheme in the optimization. Applications of the hybrid approach with two examples in material technology are given, proper predictions are obtained.

This paper aims to conduct applications of intersection method for multi-objective optimization in optimal test design with desirable response variable. The partial favourable probability of the desirable response variable of the test is evaluated according to the type of “one side desirability problem” or “one range desirability problem” and the requirements of desirable response first; then the evaluation of total favourable probability Pt of the multi-objective optimization test design is conducted according to the common procedure of the "intersection" method for multi-object optimization of performance indicators of the test. Finally, regression analysis is performed for the total favourable probability and response variables to get maximum total favourable probability and optimal configuration of the optimal test with desirable response variable. As application examples of the intersection method for the test designs of maximizing yield with constraints of viscosity and molecular weight and the maximizing conversion rate with constraints of desirable thermal activity are given in detail, satisfied results are obtained.

Abstract It is difficult for beginners to learn and use amplicon analysis software because there are so many software tools to choose from, and all of them need multiple steps of operation. Herein, we provide a cross‐platform, open‐source, and community‐supported analysis pipeline EasyAmplicon. EasyAmplicon has most of the modules needed for an amplicon analysis, including data quality control, merging of paired‐end reads, dereplication, clustering or denoising, chimera detection, generation of feature tables, taxonomic diversity analysis, compositional analysis, biomarker discovery, and publication‐quality visualization. EasyAmplicon includes more than 30 cross‐platform modules and R packages commonly used in the field. All steps of the pipeline are integrated into RStudio, which reduces learning costs, keeps the flexibility of the analysis process, and facilitates personalized analysis. The pipeline is maintained and updated by the authors and editors of WeChat official account “Meta‐genome.” Our team will regularly release the latest tutorials both in Chinese and English, read the feedback from users, and provide help to them in the WeChat account and GitHub. The pipeline can be deployed on various platforms, and the installation time is less than half an hour. On an ordinary laptop, the whole analysis process for dozens of samples can be completed within 3 h. The pipeline is available at GitHub (https://github.com/YongxinLiu/EasyAmplicon) and Gitee (https://gitee.com/YongxinLiu/EasyAmplicon).

In the present paper, a new concept of favorable probability is proposed first, and a simplified approach for multi-criteria decision-making is developed on basis of probability theory. It assumes that all the performance indexes of all alternatives can be divided into beneficial and unbeneficial types in the multi-criteria decision-making, and each performance index of all alternative makes its contribution to a partial favorable probability in positively or negatively correlative manners linearly upon its type of beneficial or unbeneficial; the partial favorable probability of each performance index with the same physical meaning is normalized in the alternative group; the product of all partial favorable probability of an alternative makes the total favorable probability of the alternative. As a consequence, all the alternatives can be ranked according to their total favorable probability comparatively in the multi-criteria decision-making. As application examples of the new method, the quantitative assessment of multi-criteria selection for effective dwelling house walls, project managers and contractor for construction works are given in detail, satisfied results are obtained. First published online 10 January 2023

The aerodynamic characteristics of the vertical-axis wind turbine with three, four, five, and six blades are studied numerically. A coupling model of fluid flow and solid turbine blade is established to model the interactions between air and wind turbine. The pressure distribution and blade deformation affected by air are obtained and discussed. For the four wind turbines with different numbers of blades, the maximum pressure in the entire machine structure occurs at the variable angle position of the blades in the windward region under the same wind speed. Mainly due to the rapid airflow variation, complex turbulence, and significant influence of the wind field on the blades in this position, this part of the blades is prone to bending or damage. Under identical external wind field conditions, wind turbines with four and six blades exhibit significantly higher equivalent pressures on their surfaces compared to those with five and three blades. The maximum equivalent pressure of six blades can reach 3.161 × 106 Pa. The maximum deformation of the blade basically occurs at the tip and four sides of the blade. The six-blade wind turbines withstand higher and non-uniform surface pressures on their blades, resulting in the largest deformation of up to 11.658 mm. On the other hand, the four-blade wind turbine exhibits the smallest deformation. The above conclusions provide theoretical guidance for the design and optimization of vertical-axis wind turbines.

Till now the previous methods for multi-objective optimization adopt the "additive" algorithm for the normalized evaluation indexes, which has the inherent shortcoming of taking the form of "union" in the viewpoint of set theory. In fact, "simultaneous optimization of multiple indexes" should be more appropriate to take the form of "intersection" for the normalized evaluation indexes in the respects of set theory and "joint probability" in probability theory. In this paper, a new concept of favorable probability is proposed to reflect the favorable degree of the candidate material in the selection; All material property indicators are divided into beneficial or unbeneficial types to the material selection; Each material property indicator correlates to a partial favorable probability quantitatively, and the total favorable probability of a candidate material is the product of all partial favorable probabilities in the viewpoints of "intersection" of set theory and "joint probability" in probability theory, which is the sole decisive index in the competitive selection process. Results of the application examples indicate the validity of the new method.

Background: Tanshinone IIA(TA) could be used for the treatment of some diseases. However, the clinical application of TA was hindered by its poor water - solubility and low oral bioavailability, etc. The aim of this study was to improve its oral bioavailability and dissolution by encapsulating TA with hydroxypropyl–beta–cyclodextrin (HP-β-CD). Methods: The encapsulation composite of HP-β-CD/TA was prepared through solution method with optimization of response surface test design by taking encapsulation efficiency and drug loading as the goals; the in-vitro dissolution and in-vivo metabolism of the composite in rats were evaluated. Results: The optimal concentration of HP-β-CD in water was 0.4 g/mL; the optimal ratio of TA to HP-β-CD was 1:7 in weight for the encapsulating process at 50°C with stirring for 2h; the encapsulation efficiency and drug loading were 84.06% and 7.38%, respectively; the cumulative release of TA from HP–β -CD TA composite in vitro dissolution reached to 72% within 90 min, which was 3.78 times of that of the original TA substance; the area under the curves of AUC(0-t) and AUC(0-∞) of the HP–β –CD -TA inclusion composite were 3.71 and 3.42 times of the original TA substance under p

Brazing of two dissimilar structural materials; Zircaloy-4 and SS-316L was performed at 900oC under high vacuum conditions. The metallic glass ribbons (Zr55Cu30Al10Ni2Fe3-at. %) of 30 µm thickness, were used as an interlayer. The bonded region was characterized by scanning electron microscope (SEM), energy dispersive spectroscope (EDS) and microhardness testing. The metallurgical bond formation was due to compositional changes in the molten interlayer and later on its subsequent solidification. Assessment of the bonded zone (BZ) revealed three distinct regions (Region-I, Region-II and Region-III). Diffusion transformation was observed in Region-I and Region-III which were interface with base alloys SS-316L and Zircaloy-4 respectively. However, Region-II at the middle of the BZ was composed of isothermally and athermally solidified portions. The highest values of Microhardness were observed in Region-III which was due to the presence of hard phases. Moreover, a crack parallel to BZ was observed in Region-III and was attributed to differential contraction of base alloys during cooling. Maximum shear stress acting on the BZ was calculated and correlated to the brittle phase cracking.

Radioactive wastewater containing high concentration of radionuclides poses severe threats to ecosystem and human health, so efficient removal of these toxic heavy metals is urgently needed. Titanate nanomaterials have been demonstrated good adsorbents for heavy metals due to ion exchange property. In this study, titanate nanorings (TNRs) were synthesized using the facile hydrothermal-cooling method. The TNRs were composed of sodium trititanate, with a chemical formula of Na0.66H1.34Ti3O7•0.27H2O and a Na content of 2.38 mmol/g. The TNRs demonstrated sufficient adsorption performance to radionuclides europium (Eu) and uranium (U) ions. Specifically, even at a high initial concentration of 50 mg/L, 86.5% and 92.6% of the two metal ions can be rapidly adsorbed by the TNRs within 5 min, and equilibrium was reached within 60 min at pH 5. The maximum adsorption capacity (Qmax) obtained by the Langmuir isotherm model was 115.3 mg/g for Eu(III) and 282.5 mg/g for uranium U(VI) at pH 5, respectively. The adsorption capacities of the two metals under various water chemical conditions were highly related to their species. Ion exchange between metal cations and Na+ in the TNR interlayers was the dominant adsorption mechanism, and adsorption of U(VI) was more complicated because of the co-existence of various uranyl (UO22+) and uranyl-hydroxyl species. The spent TNRs were effectively regenerated through an acid-base or ethylenediamine tetraacetic acid (EDTA) treatment and reused. Considering the large adsorption capacity and quick kinetic, TNRs are promising materials to remove radionuclides in environmental purification applications, especially emergent treatment of leaked radionuclides.

Reservoirs play a vital role in the control and management of surface water resources. However, the long water residence time in the reservoir potentially increases the storage and accumulation of antibiotic resistant genes (ARGs). The full profiles and potential health risks of antibiotic resistomes in reservoirs are largely unknown. In this study, we investigated the antibiotic resistomes of water and sediment during different seasons in the Danjiangkou Reservoir, which is one of the largest reservoirs in China, using a metagenomic sequencing approach. A total of 436 ARG subtypes belonging to 20 ARG types were detected from 24 water and 18 sediment samples, with an average abundance of 0.138 copies/cell. The overall ARG abundance in the sediment was higher than that in the water, and bacitracin and vancomycin resistance genes were the predominant ARG types in the water and sediment, respectively. The overall ARG abundance in the dry season was higher than that in the wet season, and a significant difference in ARG subtype compositions was observed in water, but not in the sediment, between the different seasons. The potential horizontal gene transfer frequency in the water was higher than that in the sediment, and the ARGs in water mainly came from the sediment upstream of the reservoir. The metagenomic assembly identified 14 contigs as ARG-carrying pathogens including Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa, and 3 of 14 carried virulence factors. Overall, the potential public health risks posed by resistomes in the water of the Danjiangkou Reservoir were higher in the dry season than in the wet season. Based on these results, strategies including sediment control and pathogen monitoring are suggested for water safety management in drinking water reservoirs. Keywords: Antibiotic resistome, Metagenomic sequencing, Drinking water source, The Danjiangkou Reservoir, ARG host tracking

The corrosion behavior of aluminum 1060 in hydrated salt phase change materials (PCM) melts comprised of Na _2 HPO _4 ·12H _2 O - Na _2 SO _4 ·10H _2 O was investigated through electrochemical tests as a function of pH. The pH of PCM medium was adjusted by adding H _2 SO _4 or NaOH. Electrochemical impedance spectroscopy reveals the existence of concentration difference layer at aluminum/PCM interface ascribed to the adsorption of PCM species and the formation of aluminum oxides. The observed anodic branch of DC polarization exhibits potential independence which is ascribed to the formation of surface oxide film and the accumulation of hydrogen molecules inside surface pores. The oxide film formed at neutral condition is characterized as the most compact and corrosion resistant while as the environmental pH shifts away from 7 the oxides/aluminum dissolution is accelerated and more aluminum 1060 substrate is exposed.

Microbial communities of activated sludge (AS) play a key role in the performance of wastewater treatment processes. However, seasonal variability of microbial population in varying AS-based processes has been poorly correlated with operation of full-scale wastewater treatment systems (WWTSs). In this paper, significant seasonal variability of AS microbial communities in eight WWTSs located in the city of Guangzhou were revealed in terms of 16S rRNA-based Miseq sequencing. Furthermore, variation redundancy analysis (RDA) demonstrated that the microbial community compositions closely correlated with WWTS operation parameters such as temperature, BOD, NH4+-N and TN. Consequently, support vector regression models which reasonably predicted effluent BOD, SS and TN in WWTSs were established based on microbial community compositions. This work provided an alternative tool for rapid assessment on performance of full-scale wastewater treatment plants.

Markowitz's approach could not deal with the overall optimization of both minimizing variance of return rate and maximizing return rate simultaneously due to its short of rational method for handling multi-objective optimization. In this article, a hybrid of the probability-based multi-objective optimization with the uniform design for experiments of mixtures is performed to solve the portfolio investment problem of concurrent optimization of both maximizing return rate and minimizing variance of return rate. The probabilitybased multi-objective optimization is employed to transfer the bi-objective problem of portfolio investment into a mono-objective one with total preferable probability as the goal for the overall optimization of the system in spirit of probability theory, the uniform design for experiments of mixtures is used to perform the subsequent discretization. Project management problem is rather a multi-objective to conduct naturally. The analysis shows the rationality of the hybrid solution. [ABSTRACT FROM AUTHOR]