Ab initio studies of the protonation of CO,N2 and NO+: Calculation of the minimum energy reaction paths

Ab initio molecular orbital calculations employing a 4-31G basis set have been used to study the minimum energy paths for the formation of HCO⁺, COH⁺, and HCOH²⁺ from CO by protonation. The protonation of N₂ to give NNH⁺ and HNNH²⁺ and of NO⁺ to form HNO²⁺ and NOH²⁺ have also been investigated. All species formed have linear equilibrium geometries and the minimum energy path for approach of the proton is along the line-of-centers of the heavy atoms. Energy barriers to the formation of the various species are given, where appropriate, and changes in geometry, ordering of molecular orbitals and orbital occupancy are discussed.     

Is Bitcoin’s Carbon Footprint Persistent? Multifractal Evidence and Policy Implications

The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of ‘proof-of-work’ with ‘proof-of-space’ or ‘proof-of-stake’, although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.     

A Family of Fitness Landscapes Modeled through Gene Regulatory Networks

Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and “ruggedness”. As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population’s evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies.     

Visible Light-Driven Alkyne-Grafted Ethylene-Bridged Azobenzene Chromophores for Photothermal Utilization

Molecular photoswitches are considered to be important candidates in the field of solar energy storage due to their sensitive and reversible bidirectional optical response. Nevertheless, it is still a daunting challenge to design a molecular photoswitch to improve the low solar spectrum utilization and quantum yields while achieving charging and discharging of heat without solvent assistance. Herein, a series of visible-light-driven ethylene-bridged azobenzene (b-Azo) chromophores with different alkyne substituents which can undergo isomerization reactions promoted in both directions by visible light are reported. Their visible light responsiveness improves their solar spectrum utilization while also having high quantum yields. In addition, as the compounds are liquids, there is no need to dissolve the compounds in order to exploit this switching. The photoisomerization of b-Azo can be adjusted by alkyne-related substituents, and hexyne-substituted b-Azo is able to store and release photothermal energy with a high density of 106.1 J·g⁻¹, and can achieve a temperature increase of 1.8 °C at a low temperature of −1 °C.     

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.     

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.     

Quantum Stream Cipher Based on Holevo–Yuen Theory

In this review paper, we first introduce the basic concept of quantum computer-resistant cryptography, which is the cornerstone of security technology for the network of a new era. Then, we will describe the positioning of mathematical cryptography and quantum cryptography, that are currently being researched and developed. Quantum cryptography includes QKD and quantum stream cipher, but we point out that the latter is expected as the core technology of next-generation communication systems. Various ideas have been proposed for QKD quantum cryptography, but most of them use a single-photon or similar signal. Then, although such technologies are applicable to special situations, these methods still have several difficulties to provide functions that surpass conventional technologies for social systems in the real environment. Thus, the quantum stream cipher has come to be expected as one promising countermeasure, which artificially creates quantum properties using special modulation techniques based on the macroscopic coherent state. In addition, it has the possibility to provide superior security performance than one-time pad cipher. Finally, we introduce detailed research activity aimed at putting the quantum stream cipher into practical use in social network technology.     

氦原子及类氦离子基态的二参数变分法研究

提出了一种计算氦原子及类氦离子基态能量和波函数的二参数变分法,包括试探波函数的设计和基态能量表达式的推导,并用Mathematica 5.0软件的优化计算功能方便快捷地计算出基态能量,将计算结果与实验结果和部分所列文献的结果进行对比.结果表明,本文所得精度较高,变分参数个数较少.同时强调交换对称性和量子态的交缠在双电子原子体系问题中的重要性.     

Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory

Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.     

An Exploration of the Effect of the Kleier Model and Carrier-Mediated Theory to Design Phloem-Mobile Pesticides Based on Researching the N-Alkylated Derivatives of Phenazine-1-Carboxylic Acid-Glycine

The Kleier model and Carrier-mediated theory are effective for molecularly designing pesticides with phloem mobility. However, the single Kleier model or Carrier-mediated theory cannot achieve a reliable explanation of the phloem mobility of all exogenous substances. A detailed investigation of the two models and the scope of their applications can provide a more accurate and highly efficient basis for the guidance of the design and development of phloem-mobile pesticides. In the present paper, a strategy using active ingredient-amino acid conjugates as mode compounds is developed based on Carrier-mediated theory. An N-alkylated amino acid is used to improve the pesticide’s physicochemical properties following the Kleier model, thus allowing the conjugates to fall on the predicted and more accessible transportation region of phloem. Moreover, the influence of this movement on phloem is inspected by the Kleier model and Carrier-mediated theory. To verify this strategy, a series of N-alkylated phenazine-1-carboxylic acid-glycine compounds (PCA-Gly) were designed and synthesized. The results related to the castor bean seeds (R. communis L.) indicated that all the target compounds (4a–4f) had phloem mobility. The capacity for phloem mobility shows that N-alkylated glycine containing small substituents can significantly improve PCA phloem mobility, such as 4c(i-C₃H₇-N) > 4a(CH₃-N) ≈ 4b(C₂H₅-N) > 4d (t-C₄H₉-N) > PCA-Gly > 4e(C₆H₅-N) > 4f(CH₂COOH-N), with an oil–water partition coefficient between 1.2~2.5. In particular, compounds 4a(CH₃-N), 4b(C₂H₅-N), and 4c(i-C₃H₇-N) present better phloem mobility, with the average concentrations in phloem sap of 14.62 μΜ, 13.98 μΜ, and 17.63 μΜ in the first 5 h, which are 8 to 10 times higher than PCA-Gly (1.71 μΜ). The results reveal that the Kleier model and Carrier-mediated theory play a guiding role in the design of phloem-mobile pesticides. However, the single Kleier model or Carrier-mediated theory are not entirely accurate. Still, there is a synergism between Carrier-mediated theory and the Kleier model for promoting the phloem transport of exogenous compounds. Therefore, we suggest the introduction of endogenous plant compounds as a promoiety to improve the phloem mobility of pesticides through Carrier-mediated theory. It is necessary to consider the improvement of physicochemical properties according to the Kleier model, which can contribute to a scientific theory for developing phloem-mobile pesticides.