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With Ring Model, the experimental less thermo-stability of Au22(SAdm)16 over Au21(SAdm)21 was explained and two new Au22(SCH3)16 isomers with lower energies are predicted. This work enriches the knowledge on structural transformation via Ring Model.

Precise mono-doping of metal atom into metal particles at a specific particle position (e.g., the central site) in a highly controllable manner is still a challenge. In this work, we develop a highly controllable strategy for exchanging a single Ag atom into the central gold site of Au13Ag12(PPh3)10Cl8 (Ph = phenyl) nanoclusters. Interestingly, a “pigeon-pair” cluster of {[Au13Ag12(PPh3)10Cl8]·[Au12Ag13(PPh3)10Cl8]}2+ is obtained and confirmed by electrospray ionization mass spectrometry (ESI-MS), thermogravimetric analysis (TGA) and single crystal X-ray diffraction (SCXRD) analysis. The experimental results and density functional theory (DFT) calculations suggest that the single-metal-atom exchanging from [Au13Ag12(PPh3)10Cl8]+ to [Au12Ag13(PPh3)10Cl8]+ occurs at the central position through the side entry of the μ3-bridging Cl atoms. Finally, the effects on the electronic structure and properties caused by the single-atom exchange at the central site are shown by the enhancement of fluorescence and catalytic activity in the photocatalytic oxidation of ethanol.

Structural prediction of thiolate-protected gold nanoclusters (AuNCs) with diverse charge states can enrich the understanding of this species. Untill now, the number of anion AuNCs is still deficient. In this work, a series of gold nanoclusters with negative total charge, including [Au

Understanding the excited state behavior of isomeric structures of thiolate-protected gold nanoclusters is still a challenging task. In this paper, based on grand unified model and ring model for describing thiolate-protected gold nanoclusters, we have predicted four isomers of Au

We present a [Au7(SR)7] ring as a new type of protection ligand in a new atomic structure of Au15(SR)13 nanocluster for the first time based on the ring model developed to understand how interfacial interaction dictates the structures of protection motifs and gold cores in thiolate-protected gold nanoclusters. This new Au15(SR)13 model shows a tetrahedral Au4 core protected by one [Au7(SR)7] ring and two [Au2(SR)3] "staple" motifs. Density functional theory (DFT) calculations show that the newly predicted Au15(SR)13 (R = CH3/Ph) has a lower energy of 0.24/0.68 eV than previously proposed isomers. By comparing calculated optical absorption spectra (UV), circular dichroism (CD) spectra, and powder X-ray diffraction (XRD) patterns with related experimental spectra, the calculated CD spectra of the newly predicted Au15(SR)13 (R = CH3/Ph) cannot reproduce the experimental results, indicating that the newly predicted Au15(SR)13 is a new structure that needs to be confirmed by experiment. In addition, DFT calculations also show that the newly predicted Au15(SR)13 (R = CH3/Ph) exhibits a large HOMO-LUMO gap, suggesting its high chemical stability. The proposition of the [Au7(SR)7] ring as a protection ligand in the newly predicted Au15(SR)13 not only enriches the types of protection ligands in thiolate-protected gold nanoclusters but also further confirms the effectiveness and rationality of the ring model for understanding the interfacial interaction between the protection motifs and gold cores in thiolate-protected gold nanoclusters.

The atomic structures of 10-electron (10e) thiolate-protected gold nanoclusters have not received extensive attention both experimentally and theoretically. In this paper, five new atomic structures of 10e thiolate-protected gold nanoclusters, including three Au32(SR)22 isomers, one Au28(SR)18, and one Au33(SR)23, are theoretically predicted. Based on grand unified model (GUM), four Au17 cores with different morphologies can be obtained via three different packing modes of five tetrahedral Au4 units. Then, five complete structures of three Au32(SR)22 isomers, one Au28(SR)18, and one Au33(SR)23 isomers can be formed by adding the thiolate ligands to three Au17 cores based on the interfacial interaction between thiolate ligands and gold core in known gold nanoclusters. Density functional theory calculations show that the relative energies of three newly predicted Au32(SR)22 isomers are quite close to two previously reported isomers. In addition, five new 10e gold nanoclusters have large highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gaps and all-positive harmonic vibration frequencies, indicating their high stabilities.

The charge states of thiolate-protected gold nanoclusters (AuNCs) are vital to their stabilities through affecting the number of the valence electrons. However, the origin of the charge states of AuNCs has not been fully understood yet. Herein, through fulfilling the duet-rule derived Au

Power demand side management (DSM) regulations stipulate that electric power grid enterprises of cities and provinces must complete their target of power saving through efficient power plant (EPP) programs. This investigation studies power DSM and establishes a supervision and evaluation system for the EPP program. The proposed system can be used to evaluate the performance of an EPP program such as electricity saving and investment benefit. A model of EPP is established. A cuckoo search algorithm is applied for the model optimization and its result gives an optimal decision scheme. Finally, a fuzzy comprehensive evaluation method is used to assess an EPP program. The results show that the established supervision and evaluation system and the model of an EPP program are effective and applicable for power grid enterprises.

This paper discusses the origin and evolution of saline springs in north and central Laos, based on chemical and stable isotopes (δD, δ18O, δ11B, and δ37Cl). All the saline springs in this study are of the Na–Cl geochemical type. The geochemical and water isotope values suggest that the saline springs in this study are mainly derived from meteoric water and/or ice and snow melt from the surrounding mountains and that they also experienced strong evaporation and intense rock–water interactions. The ionic ratios, characteristic coefficients, ternary Ca–SO4–HCO3 phase diagrams, and saturation indices of minerals show that the dissolution of halite, sulfate, and carbonate rocks may be the solute sources for saline springs in this study, whereas the underground brines in the Thakhek potash mining area are geochemically influenced by the dissolution of carnallite and sylvite. The global geothermal δ11B–Cl/B relationship and δ11B values (5.50 to 36.01‰) of saline springs suggest a continental origin of B. This B is most likely derived from marine carbonate rocks and marine evaporates (gypsum and halite) of the late Cretaceous, which is similar to the saline springs of the Nangqen–Qamdo–Simao Salt Basin. The δ37Cl value (−0.12 to +0.79) and the Cl/Br ratio (4076 to 9853) show that dissolution of late cretaceous marine halite layers, atmospheric precipitation, and water–rock interactions between volcanic rocks, mudstones, and sandstone can restrict the δ37Cl values in saline springs. Results from silica geothermometry and multi–mineral equilibrium diagrams indicate that the reservoir temperatures for the saline springs range from 87–137 °C and experience deep circulation. Hydrochemical characteristic coefficients suggest that saline springs in the Muang Say basin may have leached sylvinite and carnallite and that the potash exploration prospect in this area is relatively good.

Due to the large number of participants in PPP projects, long operation period, large investment and other factors, the project risk factors have a high degree of ambiguity and randomness. In order to accurately carry out the risk evaluation of PPP projects, this paper proposes a risk evaluation method based on cloud model. Firstly, select the main risk factors and establish a risk factor identification system. At the same time, according to the expert evaluation method and principles of comment index, establish the main risk evaluation index system, and then use improved fuzzy analytic hierarchy process method and entropy weight method to obtain the weight of the risk from the subjective and objective perspectives. According to the reverse cloud model algorithm to obtain the digital characteristics of the secondary risk. The digital characteristics of the secondary risk and the weight are combined to finally obtain the digital characteristics of the total risk of the PPP project, and the forward cloud model generator is used to obtain the cloud model of the total risk. Then according to the cloud model, the risk level standard cloud is given, and the risk level is determined by comparing the similarity of the two cloud images. This paper takes the PPP waste incineration power generation project as an example to verify the proposed method. The results show that the method is effective and scientific, which can provide a reference for future risk assessment of PPP projects.

Fair and reasonable risk sharing mechanism is beneficial to reduce the risk of investment and improve the economic benefits of Waste-To-Energy incineration projects project investment. In order to quantitatively study the risk allocation ratio of PPP Waste-To-Energy incineration projects, an intelligent optimization algorithm is used to solve the problem. Based on the utility model of government and enterprise, the risk sharing model is established by introducing the altruistic preference coefficient. In this paper the relationship between the altruistic preference coefficient, the risk-sharing ratio and the maximum utility value of government and firm qualitatively are studied. PSO algorithm is used to determine the altruistic preference coefficient, the government’s risk-sharing ratio and the enterprise’s effort.

Optimizing Internet Data Center (IDC) room environment is crucial to eliminate operational risks and achieve energy-saving goals. In this paper, an IDC room of a company in Shanghai is taken as the research object, and its an accurate 3D model is built by using Computational Fluid Dynamics (CFD) simulation software. The analysis of measured data and simulated data shows that there is turbulence phenomenon under the air supply floor of the IDC room and the mixed flow of hot and cold airflow in the cabinet. These two problems will reduce the heat dissipation efficiency of the equipment and increase the energy consumption of the IDC room. A feasible optimization scheme is given.

In this paper, six new atomic structures of thiolate-protected gold nanoclusters, i.e. Au32(SR)20, Au40(SR)26, Au48(SR)30, two Au56(SR)34, and Au60(SR)36, are predicted. Considering these six newly predicted structures and six previously predicted or crystallized Au28(SR)20, Au36(SR)24, Au44(SR)28, Au52(SR)32, and Au60(SR)36 altogether, the two-dimensional (2D) growth mode of Au28+4n(SR)20+2n (n = 0–8) nanoclusters is completely presented to compare with their one-dimensional (1D) growth mode. In Au28+4n(SR)20+2n (n = 0–8) nanoclusters with both 1D and 2D growth modes, the same number of gold-core atoms with different morphologies can be seen. Furthermore, the growth of the gold cores occurs via sequential fusion of one tetrahedral Au4 unit by sharing one gold atom. In addition, density functional theory calculations show that these six newly predicted gold nanoclusters following the 2D growth mode have relative energies very close to those of their isomeric structures following the 1D growth mode, large highest occupied molecular orbital−lowest unoccupied molecular orbital (HOMO–LUMO) gaps, and all-positive harmonic vibration frequencies, indicating their high stabilities. Therefore, the complete presentation of the 2D growth mode of Au28+4n(SR)20+2n (n = 0–8) is beneficial not only for a better understanding of the structural growth of gold nanoclusters, but also for a theoretical guidance on the prediction of new stable structures for experimental confirmation.

We present a [Au

Understanding the effect of interfacial interactions between the protection motifs and gold cores on the stabilities of thiolate-protected gold nanoclusters is still a challenging task. Based on analyses of 95 experimentally crystallized and theoretically predicted thiolate-protected gold nanoclusters, we present a ring model to offer a deeper insight into the interfacial interactions for this class of nanoclusters. In the ring model, all the gold nanoclusters can be generically viewed as a fusion or interlocking of several [Aum(SR)n] (m = 4-8, 10, and 12 and 0 ≤ n ≤ m) rings. Guided by the ring model and the grand unified model, a new Au42(SR)26 isomer is predicted, whose total energy is lower than those of two previously crystallized isomers. The ring model offers a mechanistic understanding of the interactions between the protection ligands and gold cores and practical guidance on predicting new gold nanoclusters for future experimental synthesis and confirmation.

In the process of oilfield development, the reservoir of oil wells were polluted due to well flushing and pump inspection, which resulted in long recovery period of oil well production after operation, or even difficult recovery of production. According to the states mentioned above, researchers developed XST and XSK series multifunctional flushing fluid, which had the function of anti-swelling, anti-pollution, as well as cleaning functions. The laboratory experiments showed that the oil washing efficiency ≥80%, anti-swelling rate ≥70%, as well as the damage value of reservoir permeability ≤30%. The above formula was applied in J902 oil well, which achieved great success. Not only the fishing tools went in smoothly, but also the recovery period after the well flushing operation was shortened to 4 days, which effectively reduced the impact of the well flushing operation on the well production.

In this paper, six new atomic structures of thiolate-protected gold nanoclusters, i.e. Au32(SR)20, Au40(SR)26, Au48(SR)30, two Au56(SR)34, and Au60(SR)36, are predicted. Considering these six newly predicted structures and six previously predicted or crystallized Au28(SR)20, Au36(SR)24, Au44(SR)28, Au52(SR)32, and Au60(SR)36 altogether, the two-dimensional (2D) growth mode of Au28+4n(SR)20+2n (n = 0-8) nanoclusters is completely presented to compare with their one-dimensional (1D) growth mode. In Au28+4n(SR)20+2n (n = 0-8) nanoclusters with both 1D and 2D growth modes, the same number of gold-core atoms with different morphologies can be seen. Furthermore, the growth of the gold cores occurs via sequential fusion of one tetrahedral Au4 unit by sharing one gold atom. In addition, density functional theory calculations show that these six newly predicted gold nanoclusters following the 2D growth mode have relative energies very close to those of their isomeric structures following the 1D growth mode, large highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gaps, and all-positive harmonic vibration frequencies, indicating their high stabilities. Therefore, the complete presentation of the 2D growth mode of Au28+4n(SR)20+2n (n = 0-8) is beneficial not only for a better understanding of the structural growth of gold nanoclusters, but also for a theoretical guidance on the prediction of new stable structures for experimental confirmation.

Understanding the role of interfacial interactions between the protection motifs SR[Au(SR)]x (x = 0, 1, 2, 3, ...) and gold cores on the stabilities of the thiolate-protected gold nanoclusters is still a challenging task. Although several theoretical models, including the superatom complex, super valence bond, and superatom network, have provided insights into the stabilities of either the overall structures or the gold cores, a model that can illuminate how the interfacial interaction dictates the unique structures of the protection motifs and gold cores is still lacking. Herein, we present a ring model, on the basis of comprehensive analyses of all 95 previous experimentally crystallized and theoretically predicted thiolate-protected gold nanoclusters, to offer a deeper insight into the structure-interaction relationship for this class of clusters. In the ring model, all the thiolate-protected gold nanoclusters can be generically viewed as fusion or interlocking of several [Aum(SR)n] (m = 4 - 8, 10, 12, and 0 ≤ n ≤ m) rings. The aurophilic interactions among these rings are expected to play a key role in the stabilization of the thiolate-protected gold nanoclusters. Guided by the ring model and the grand unified model (for understanding the structures of gold cores), a new Au42(SR)26 isomer is predicted, whose total energy is even lower than those of two previously crystallized isomers, thereby giving credence to the predictability of the ring model. The ring model provides not only a mechanistic understanding of the interactions between the protection ligands and gold cores in the thiolate-protected gold nanoclusters, but also a practical guidance on predicting new thiolate-protected gold nanoclusters for experimental synthesis and confirmation.

The search of ground-state structures (GSSs) of gold (Au) clusters is a formidable challenge due to the complexity of potential energy surface (PES). In this work, we have built a high-dimensional artificial neural network (ANN) potential to describe the PES of Au20 clusters. The ANN potential is trained through learning the GSS search process of Au20 by the combination of density functional theory (DFT) method and genetic algorithm. The root mean square errors of energy and force are 7.72 meV atom−1 and 217.02 meV Å−1, respectively. As a result, it can find the lowest-energy structure (LES) of Au20 clusters that is consistent with previous results. Furthermore, the scalability test shows that it can predict the energy of smaller size Au16–19 clusters with errors less than 22.85 meV atom−1, and for larger size Au21–25 clusters, the errors are below 36.94 meV atom−1. Extra attention should be paid to its accuracy for Au21–25 clusters. Applying the ANN to search the GSSs of Au16–25, we discover two new structures of Au16 and Au21 that are not reported before and several candidate LESs of Au16–18. In summary, this work proves that an ANN potential trained for specific size clusters could reproduce the GSS search process by DFT and be applied in the GSS search of smaller size clusters nearby. Therefore, we claim that building ANN potential based on DFT data is one of the most promising ways to effectively accelerate the GSS pre-screening of clusters.

This paper discusses the origin and evolution of saline springs in north and central Laos, based on chemical and stable isotopes (δD, δ18O, δ11B, and δ37Cl). All the saline springs in this study are of the Na–Cl geochemical type. The geochemical and water isotope values suggest that the saline springs in this study are mainly derived from meteoric water and/or ice and snow melt from the surrounding mountains and that they also experienced strong evaporation and intense rock–water interactions. The ionic ratios, characteristic coefficients, ternary Ca–SO4–HCO3 phase diagrams, and saturation indices of minerals show that the dissolution of halite, sulfate, and carbonate rocks may be the solute sources for saline springs in this study, whereas the underground brines in the Thakhek potash mining area are geochemically influenced by the dissolution of carnallite and sylvite. The global geothermal δ11B–Cl/B relationship and δ11B values (5.50 to 36.01‰) of saline springs suggest a continental origin of B. This B is most likely derived from marine carbonate rocks and marine evaporates (gypsum and halite) of the late Cretaceous, which is similar to the saline springs of the Nangqen–Qamdo–Simao Salt Basin. The δ37Cl value (−0.12 to +0.79) and the Cl/Br ratio (4076 to 9853) show that dissolution of late cretaceous marine halite layers, atmospheric precipitation, and water–rock interactions between volcanic rocks, mudstones, and sandstone can restrict the δ37Cl values in saline springs. Results from silica geothermometry and multi–mineral equilibrium diagrams indicate that the reservoir temperatures for the saline springs range from 87–137 °C and experience deep circulation. Hydrochemical characteristic coefficients suggest that saline springs in the Muang Say basin may have leached sylvinite and carnallite and that the potash exploration prospect in this area is relatively good.

We present the application of grand unified model (GUM) and ring model in understanding the isomeric structures of six Au28(SR)20 nanoclusters. In light of GUM, three Au14 cores could be attributed to the different packing modes of four tetrahedral Au4 units. The binding of the protection ligands with three Au14 cores can be guided by the ring model, in which the protection ligands and some gold core atoms can form the rings with different radii. Therefore the structural isomerism of six isomers can be understood by different types of ring structures packing, fusing, or interlocking together.

In this paper, an improved efficient population utilization strategy for particle swarm optimization (IEPUS-PSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signals. In the IEPUS-PSO, a mutation probability is proposed to distinguish local version and global version in particle change model and a self-adapted mutation operator, which is used to update the particles’ positions randomly, is introduced into EPUS-PSO. The IEPUS-PSO- based inversing technique is used to estimate the defect profiles. The estimated defect profiles of simulation signals demonstrate that the inversing technique based on the IEPUS-PSO outperforms the one based on EPUS-PSO. The results estimated from real MFL signals by the IEPUS-PSO-based inversing technique indicate that the algorithm is capable of decreasing the computation time. The results show that the IEPUS-PSO-based inversing technique could improve the reconstruction precision by two orders of magnitude for the MFL simulation signals, and for the real MFL signals, the computation time is reduced by about 30% nearly under the same reconstruction precision.

In order to satisfy the high accuracy and high security requirements of time synchronization in smart grid, this paper has conducted a research on high-precision time synchronization control algorithm based on IEEE 1588 protocol. A slave clock model is built considering both phase offset and frequency drift. A novel approach using proportional–integral–derivative (PID) controller in cooperation with the wolf colony algorithm (WCA) is proposed to eliminate the offset of the slave clock with respect to the master clock. As a new swarm-intelligence algorithm, WCA is introduced for self-tuning the optimal parameters of the PID controller, which simulates the intelligent predatory behaviors and prey distribution rule of the wolf colony. The WCA-based PID control approach is compared with the method based on standard Particle Swarm Optimization (PSO) and Back-Propagation (BP) neural network. The experimental results show that the proposed WCA-based PID control approach in time synchronization for smart grid has higher precision, can adaptively adjust compensation and keep the master–slave clock synchronization effectively.

Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latter’s robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.

In the process of oilfield development, the reservoir can be polluted by well cleaning, pump inspection and other operations. And that will lead to a long recovery period of oil well production after operation, and even the production is difficult to recover. In recent years, unplugging transformation and other technologies have been researched and applied, and certain results have been achieved. However, there is a lack of systematic evaluation and analysis on the applicability, advantages and disadvantages of various technologies. For this reason, the application effect of the existing technology is systematically analyzed and evaluated in terms of the formula of unplugging agent, unplugging process, damage mechanism, oil-water well relationship, production layer and other aspects by means of combination of geology and engineering. The adaptability of this technology is obtained, and it is used as the principle of well selection for subsequent technical application to further improve the effect of measures.

Dempster's combination rule can only be applied to independent bodies of evidence. One occurrence of dependence between two bodies of evidence is when they result from a common source. This paper proposes an improved method for combining dependent bodies of evidence which takes the significance of the common information sources into consideration. The method is based on the significance weighting operation and the "decombination" operation. A numerical example is illustrated to show the use and effectiveness of the proposed method.

In order to meet the higher accuracy and system reliability requirements, the information fusion for multi-sensor systems is an increasing concern. Dempster–Shafer evidence theory (D–S theory) has been investigated for many applications in multi-sensor information fusion due to its flexibility in uncertainty modeling. However, classical evidence theory assumes that the evidence is independent of each other, which is often unrealistic. Ignoring the relationship between the evidence may lead to unreasonable fusion results, and even lead to wrong decisions. This assumption severely prevents D–S evidence theory from practical application and further development. In this paper, an innovative evidence fusion model to deal with dependent evidence based on rank correlation coefficient is proposed. The model first uses rank correlation coefficient to measure the dependence degree between different evidence. Then, total discount coefficient is obtained based on the dependence degree, which also considers the impact of the reliability of evidence. Finally, the discount evidence fusion model is presented. An example is illustrated to show the use and effectiveness of the proposed method.

In this paper, efficient managing particle swarm optimization (EMPSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signal. In the proposed EMPSO, in order to strengthen exchange of information among particles, particle pair model was built. For more efficient searching when facing different landscapes of problems, velocity updating scheme including three velocity updating models was also proposed. In addition, for more chances to search optimum solution out, automatic particle selection for re-initialization was implemented. The optimization results of six benchmark functions show EMPSO performs well when optimizing 100-D problems. The defect simulation results demonstrate that the inversing technique based on EMPSO outperforms the one based on self-learning particle swarm optimizer (SLPSO), and the estimated profiles are still close to the desired profiles with the presence of low noise in MFL signal. The results estimated from real MFL signal by EMPSO-based inversing technique also indicate that the algorithm is capable of providing an accurate solution of the defect profile with real signal. Both the simulation results and experiment results show the computing time of the EMPSO-based inversing technique is reduced by 20%–30% than that of the SLPSO-based inversing technique.