Study on NO Heterogeneous Reduction Mechanism under Gasification Condition

Chemisorption of NO and successive heterogeneous reduction mechanisms on the well-defined char models under carbon/char-CO2 gasification condition were investigated using density functional theory at the B3LYP/6-31G (d) level of theory. The characteristics of gasification process were concluded and incorporated into the theoretical calculations by establishing three gasification char models and taking into account the presence of CO in ambient gas pool. The results indicate that both the configuration of char model and adsorption mode have significant influence on the NO adsorption energy. Intensive gasification surface is likely to be thermally unfavorable and the O-down mode is regarded as the most inactive approach for NO’s adsorbing. Finally, NO heterogeneous reduction mechanisms on the three char models under gasification are proposed based on detailed analysis on thermodynamic data and atomic bond populations

Modeling and Optimal Control of a Class of Warfare Hybrid Dynamic Systems Based on Lanchester (

For the particularity of warfare hybrid dynamic process, a class of warfare hybrid dynamic systems is established based on Lanchester equation in a (n,1) battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation), and discrete event systems (described by variable tactics). Furthermore, an expository discussion is presented on an optimal variable tactics control problem for warfare hybrid dynamic system. The optimal control strategies are designed based on dynamic programming and differential game theory. As an example of the consequences of this optimal control problem, we take the (2, 1) case and solve the optimal strategies in a (2, 1) case. Simulation results show the feasibility of warfare hybrid system model and the effectiveness of the optimal control strategies designed

Bisphosphonates Suppress Insulin-Like Growth Factor 1-Induced Angiogenesis Via the HIF-1α/VEGF Signaling Pathways in Human Breast Cancer Cells

Adjunctive chemotherapy with bisphosphonates has been reported to delay bone metastasis and improve overall survival in breast cancer. Aside from its antiresorptive effect, bisphosphonates exhibit antitumor activities, in vitro and in vivo, via several mechanisms, including antiangiogenesis. In this study, we investigated the potential molecular mechanisms underlying the antiangiogenic effect of non-nitrogen-containing and nitrogen-containing bisphosphonates, clodronate and pamidronate, respectively, in insulin-like growth factor (IGF)-1 responsive human breast cancer cells. We tested whether bisphosphonates had any effects on hypoxia-inducible factor (HIF)-1α/vascular endothelial growth factor (VEGF) axis that plays a pivotal role in tumor angiogenesis, and our results showed that both pamidronate and clodronate significantly suppressed IGF-1-induced HIF-1α protein accumulation and VEGF expression in MCF-7 cells. Mechanistically, we found that either pamidronate or clodronate did not affect mRNA expression of HIF-1α, but they apparently promoted the degradation of IGF-1-induced HIF-1α protein. Meanwhile, we found that the presence of pamidronate and clodronate led to a dose-dependent decease in the newly-synthesized HIF-1α protein induced by IGF-1 in breast cancer cells after proteasomal inhibition, thus, indirectly reflecting the inhibition of protein synthesis. In addition, our results indicated that the inhibitory effects of bisphosphonates on the HIF-1α/VEGF axis are associated with the inhibition of the phosphoinositide 3-kinase/AKT/ mammalian target of rapamycin signaling pathways. Consistently, we demonstrated that pamidronate and clodronate functionally abrogated both in vitro and in vivo tumor angiogenesis induced by IGF-1-stimulated MCF-7 cells. These findings have highlighted an important mechanism of the pharmacological action of bisphosphonates in the inhibition of tumor angiogenesis in breast cancer cells. © 2009 UICC

On the Relationships between Different Methods for Degree Evaluation (Full Version)

In this paper, we compare several non-tight degree evaluation methods i.e., Boura and Canteaut\u27s formula, Carlet\u27s formula as well as Liu\u27s numeric mapping and division property proposed by Todo, and hope to find the best one from these methods for practical applications. Specifically, for the substitution-permutation-network (SPN) ciphers, we first deeply explore the relationships between division property of an Sbox and its algebraic properties (e.g., the algebraic degree of its inverse). Based on these findings, we can prove theoretically that division property is never worse than Boura and Canteaut\u27s and Carlet\u27s formulas, and we also experimentally verified that the division property can indeed give a better bound than the latter two methods. In addition, for the nonlinear feedback shift registers (NFSR) based ciphers, according to the propagation of division property and the core idea of numeric mapping, we give a strict proof that the estimated degree using division property is never greater than that of numeric mapping. Moreover, our experimental results on Trivium and Kreyvium indicate the division property actually derives a much better bound than the numeric mapping. To the best of our knowledge, this is the first time to give a formal discussion on the relationships between division property and other degree evaluation methods, and we present the first theoretical proof and give the experimental verification to illustrate that division property is the optimal one among these methods in terms of the accuracy of the upper bounds on algebraic degree

Optimized Quantum Implementation of AES

In this paper, we research the implementation of the AES family with Pauli-X gates, CNOT gates and Toffoli gates as the underlying quantum logic gate set. First, we investigate the properties of quantum circuits and the influence of Pauli-X gates, CNOT gates and Toffoli gates on the performance of the circuits constructed with those gates. Based on the properties of quantum circuits as well as our observations on the classical ones built by Boyar \emph{et al.} and Zou \emph{et al.}, we research the construction of reversible circuits for AES\u27s Substitution-box (S-box) and its inverse (S-box$^{-1}$) by rearranging the classical implementation to three parts. Since the second part is treated as a 4-bit S-box in this paper and can be dealt with by existing tools, we propose a heuristic to search optimized reversible circuits for the first part and the third part. The application of our method reveals that the reversible circuits constructed for AES S-box and its inverse consume fewer qubits with optimized CNOT gate consumption and Toffoli depth. In addition, we study the construction of reversible circuits for the key schedule and the round function of AES by applying various number of S-boxes in parallel. As a result, we report quantum circuits of AES-128, AES-192 and AES-256 with 269, 333 and 397 qubits, respectively. If more qubits are allowed, quantum circuits that outperform state-of-the-art schemes in the metric of $T\cdot M$ value for the AES family can be reported, and it needs only 474, 538 and 602 qubits for AES-128, AES-192 and AES-256, respectively

A Novel Automatic Technique Based on MILP to Search for Impossible Differentials

The Mixed Integer Linear Programming (MILP) is a common method of searching for impossible differentials (IDs). However, the optimality of the distinguisher should be confirmed by an exhaustive search of all input and output differences, which is clearly computationally infeasible due to the huge search space. In this paper, we propose a new technique that uses two-dimensional binary variables to model the input and output differences and characterize contradictions with constraints. In our model, the existence of IDs can be directly obtained by checking whether the model has a solution. In addition, our tool can also detect any contradictions between input and output differences by changing the position of the contradictions. Our method is confirmed by applying it to several block ciphers, and our results show that we can find 6-, 13-, and 12-round IDs for Midori-64, CRAFT, and SKINNY-64 within a few seconds, respectively. Moreover, by carefully analyzing the key schedule of Midori-64, we propose an equivalent key transform technique and construct a complete MILP model for an 11-round impossible differential attack (IDA) on Midori-64 to search for the minimum number of keys to be guessed. Based on our automatic technique, we present a new 11-round IDA on Midori-64, where 23 nibbles of keys need to be guessed, which reduces the time complexity compared to previous work. The time and data complexity of our attack are $2^{116.59}$ and $2^{60}$, respectively. To the best of our knowledge, this is the best IDA on Midori-64 at present

Rotational-XOR Differential Rectangle Cryptanalysis on Simon-like Ciphers

In this paper, we propose a rectangle-like method called \textit{rotational-XOR differential rectangle} attack to search for better distinguishers. It is a combination of the rotational-XOR cryptanalysis and differential cryptanalysis in the rectangle-based way. In particular, we put a rotational-XOR characteristic before a differential characteristic to construct a rectangle structure. By choosing some appropriate rotational-XOR and differential characteristics as well as considering multiple differentials, some longer distinguishers that have the probability greater than $2^{-2n}$ can be constructed effectively where $n$ is the block size of a block cipher. We apply this new method to some versions of \textsc{Simon} and \textsc{Simeck} block ciphers. As a result, we obtain rotational-XOR differential rectangle distinguishers up to 16, 16, 17, 16 and 21 rounds for \textsc{Simon}32/64, \textsc{Simon}48/72, \textsc{Simon}48/96, \textsc{Simeck}32 and \textsc{Simeck}48, respectively. Our distinguishers for \textsc{Simon}32/64 is longer than the best differential and rotational-XOR distinguishers. As for \textsc{Simon}48/96, the distinguisher is longer than the rotational-XOR distinguisher and as long as the best differential distinguisher. Also, our distinguisher for \textsc{Simeck}32 is longer than the best differential distinguisher (14 rounds) and has the full weak key space (i.e., $2^{64}$) whereas the 16-round rotational-XOR distinguisher has a weak key class of $2^{36}$. In addition, our distinguisher for \textsc{Simeck}48 has a better weak key class ($2^{72}$ weak keys) than the 21-round rotational-XOR distinguisher ($2^{60}$ weak keys). To the best of our knowledge, this is the first time to consider the combinational cryptanalysis based on rotational-XOR and differential cryptanalysis using the rectangle structure

Predicting 3-year all-cause mortality in rectal cancer patients based on body composition and machine learning

ObjectivesThe composition of abdominal adipose tissue and muscle mass has been strongly correlated with the prognosis of rectal cancer. This study aimed to develop and validate a machine learning (ML) predictive model for 3-year all-cause mortality after laparoscopic total mesorectal excision (LaTME).MethodsPatients who underwent LaTME surgery between January 2018 and December 2020 were included and randomly divided into training and validation cohorts. Preoperative computed tomography (CT) image parameters and clinical characteristics were collected to establish seven ML models for predicting 3-year survival post-LaTME. The optimal model was determined based on the area under the receiver operating characteristic curve (AUROC). The SHAPley Additive exPlanations (SHAP) values were utilized to interpret the optimal model.ResultsA total of 186 patients were recruited and divided into a training cohort (70%, n = 131) and a validation cohort (30%, n = 55). In the training cohort, the AUROCs of the seven ML models ranged from 0.894 to 0.949. In the validation cohort, the AUROCs ranged from 0.727 to 0.911, with the XGBoost model demonstrating the best predictive performance: AUROC = 0.911. SHAP values revealed that subcutaneous adipose tissue index (SAI), visceral adipose tissue index (VAI), skeletal muscle density (SMD), visceral-to-subcutaneous adipose tissue ratio (VSR), and subcutaneous adipose tissue density (SAD) were the five most important variables influencing all-cause mortality post-LaTME.ConclusionBy integrating body composition, multiple ML predictive models were developed and validated for predicting all-cause mortality after rectal cancer surgery, with the XGBoost model exhibiting the best performance