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A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

Interest in the use of proteolysis-targeting chimeras (PROTACs) in cancer therapy has increased in recent years. Targeting bromodomain and extra terminal domain (BET) proteins, especially bromodomain-containing protein 4 (BRD4), has shown inhibitory effects on basal-like breast cancer (BLBC). However, the bioavailability of BRD4 PROTACs is restricted by their non-selective biodegradability and low tumor-targeting ability. We demonstrated that 6b (BRD4 PROTAC) suppresses BLBC cell growth by targeting BRD4, but not BRD2 and BRD3, for cereblon (CRBN)-mediated ubiquitination and proteasomal degradation. Compound 6b also inhibited expression of Krüppel-like factor 5 (KLF5) transcription factor, a key oncoprotein in BLBC, controlled by BRD4-mediated super-enhancers. Moreover, 6b inhibited HCC1806 tumor growth in a xenograft mouse model. The combination of 6b and KLF5 inhibitors showed additive effects on BLBC. These results suggest that BRD4-specific PROTAC can effectively inhibit BLBC by downregulating KLF5, and that 6b has potential as a novel therapeutic drug for BLBC.

With the rapid development of new energy resources and diversified load, power network topology data have grown swiftly. To meet the needs of the smart grid dispatching system, the power network topology analysis to support the marketing, power distribution, and other businesses in the operation of the grid has become one of the bottlenecks in the development of the smart grid. In the power grid, the outgoing lines of some generators are directly connected to the transformer, and during the busbar analysis, the nodes only adjacent to the transformer components will be identified as independent busbars. Therefore, a new method of node numbering optimization is proposed, in which the nodes adjacent to the switch are numbered preferentially. On this basis, a new abstract description method of adjacency relations and an improved breadth-first search method based on information interaction are proposed. Finally, simulation experiments are carried out in power grids with three different scales. The simulation results show that the algorithm can quickly and accurately realize power network topology analysis in the large-scale power grid, and the operating efficiency is improved by about 20% compared with the traditional algorithm.

Basal-like breast cancer (BLBC) accounts for approximately 15% of all breast cancer cases, and patients with BLBC have a low survival rate. Our previous study demonstrated that the KLF5 transcription factor promotes BLBC cell proliferation and tumor growth. In this study, we demonstrated that the histone deacetylase inhibitors (HDACi), suberoylanilide hydroxamic acid (SAHA), and trichostatin A (TSA), increased KLF5 acetylation at lysine 369 (K369), downregulated KLF5 protein expression levels, and decreased cell viability in BLBC cell lines. HDACi target KLF5 for proteasomal degradation by promoting KLF5 protein ubiquitination. K369 acetylation of KLF5 decreases the binding between KLF5 and its deubiquitinase, BAP1. These findings revealed a novel mechanism by which HDACi suppress BLBC, and a novel crosstalk between KLF5 protein acetylation and ubiquitination.

This thesis studies two aspects of effective field theories arising from string theory. In the first part, a new process of change of number of chiral fields in string theory compactification is proposed. It occurs quite generically, which is demonstrated in two cases: the perturbative heterotic string theory compactification on Calabi-Yau space, and type IIA orientifold construction with intersecting D6-branes. Contrary to common belief, chirality change can proceed smoothly while preserving supersymmetry, with effective field theory description. In the second part, some aspects on noncommutative field theory are studied. The first one studies the non-commutative scalar solitons. An universial critical non-commutative scale in found, below which all the scalar solitons disappear. The second study concerns efforts to generalize non-commutative field theory to curved manifold in the framework of deformation quantization algebra, with the detailed study on non-commutative two-sphere. We found that in general there is topological obstruction, so that the space of quantum states is non-unitary. It can be interpreted as obstruction of Kontesevich deformation quantization algebra being C* algebra.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause the most serious pandemics of Coronavirus Disease 2019 (COVID-19), which threatens human health and public safety. SARS-CoV-2 spike (S) protein uses angiotensin-converting enzyme 2 (ACE2) as recognized receptor for its entry into host cell that contributes to the infection of SARS-CoV-2 to hosts. Using computational modeling approach, this study resolved the evolutionary pattern of bonding affinity of ACE2 in 247 jawed vertebrates to the spike (S) protein of SARS-CoV-2. First, high-or-low binding affinity phenotype divergence of ACE2 to the S protein of SARS-CoV-2 has appeared in two ancient species of jawed vertebrates, Scyliorhinus torazame (low-affinity, Chondrichthyes) and Latimeria chalumnae (high-affinity, Coelacanthimorpha). Second, multiple independent affinity divergence events recur in fishes, amphibians-reptiles, birds, and mammals. Third, high affinity phenotypes go up in mammals, possibly implying the rapid expansion of mammals might accelerate the evolution of coronaviruses. Fourth, we found natural mutations at eight amino acid sites of ACE2 can determine most of phenotype divergences of bonding affinity in 247 vertebrates and resolved their related structural basis. Moreover, we also identified high-affinity or low-affinity-associated concomitant mutation group.The group linked to extremely high affinity may provide novel potentials for the development of human recombinant soluble ACE2 (hrsACE2) in treating patients with COVID-19 or for constructing genetically modified SARS-CoV-2 infection models promoting vaccines studies. These findings would offer potential benefits for the treatment and prevention of SARS-CoV-2. Competing Interest Statement The authors have declared no competing interest.

We study the Ricci flatness condition on generic supermanifolds. It has been found recently that when the fermionic complex dimension of the supermanifold is one the vanishing of the super-Ricci curvature implies the bosonic submanifold has vanishing scalar curvature. We prove that this phenomena is only restricted to fermionic complex dimension one. Further we conjecture that for complex fermionic dimension larger than one the Calabi-Yau theorem holds for supermanifolds.

We study the scalar quantum field theory on a generic noncommutative two-sphere as a special case of noncommutative curved space, which is described by the deformation quantization algebra obtained from symplectic reduction and parametrized by \\(H^2(S^2, )\\). The fuzzy sphere is included as a special case parametrized by the integer two-cohomology class \\(H^2(S^2, )\\), which has finite number of degrees of freedom and the field theory has a well defined Hilbert space. When the two-cohomology class is not integer valued, the scalar quantum field theory based on the deformation algebra is not unitary: the signature of the inner product on the space of functions is indefinite. Hence the existence of deformation quantization does not guarantee a physically acceptable deformed geometric background. For the deformation quantization on a general curved space, this obstruction of unitarity can be given by an explicit topological formula.