Hydrophobic counter ion effects on the formation of mesh and reversed phases in the perfluorodecanoate/water system

The tetramethylammonium perfluorodecanoate (C10TMA)/water system forms both random, Mh1(0) and correlated mesh, Mh1(R[3 with combining macron]m) phases over a wide range of concentration and temperature. Whilst the random mesh phase is found in the ammonium homologue, the extensive correlated mesh phase seems to be a result of the hydrophobic nature of the tetramethylammonium (TMA) counter ion. In order to explore the reasons for the occurrence of these mesh phases and the effects of hydrophobic counter ions on phase structure the counter ion has been substituted by a series of increasing hydrophobicity namely butyltrimethylammonium (BTMA), dibutyldimethylammonium (DBDMA), and methyltributylphosphonium (MTBP). The phases and their structures were identified by small angle X-ray scattering. Increasing counter ion hydrophobicity causes a change from mesh, to lamellar, and finally to reversed phases. All the hydrophobic counter ions are strongly bound to the water/fluorocarbon interface and, in the case of those with butyl chains, there is penetration of between 50 and 60% of the total number of counter ion methyl groups into the fluorocarbon region of the lamellar phase. These bound counter ions reduce the accessibility of the head group region to solvent water. As the number of butyl chains on the counter ion increases the lamellar phase is progressively lost and is replaced by a reversed micelle phase either as a single phase or as part of an extensive two phase region

Scheduling Based on Interruption Analysis and PSO for Strictly Periodic and Preemptive Partitions in Integrated Modular Avionics

Integrated modular avionics introduces the concept of partition and has been widely used in avionics industry. Partitions share the computing resources together. Partition scheduling plays a key role in guaranteeing correct execution of partitions. In this paper, a strictly periodic and preemptive partition scheduling strategy is investigated. First, we propose a partition scheduling model that allows a partition to be interrupted by other partitions, but minimizes the number of interruptions. The model not only retains the execution reliability of the simple partition sets that can be scheduled without interruptions, but also enhances the schedulability of the complex partition sets that can only be scheduled with some interruptions. Based on the model, we propose an optimization framework. First, an interruption analysis method to decide whether a partition set can be scheduled without interruptions is developed. Then, based on the analysis of the scheduling problem, we use the number of interruptions and the sum of execution time for all partitions in a major time frame as the optimization objective functions and use particle swarm optimization (PSO) to solve the optimization problem when the partition sets cannot be scheduled without interruptions. We improve the update strategy for the particles beyond the search space and round all particles before calculating the fitness value in PSO. Finally, the experiments with different partitions are conducted and the results validate the partition scheduling model and illustrate the effectiveness of the optimization framework. In addition, other optimization algorithms, such as genetic algorithm and neural networks, can also be used to solve the partition problem based on our model and solution framework

The Proximal Operator of the Piece-wise Exponential Function and Its Application in Compressed Sensing

This paper characterizes the proximal operator of the piece-wise exponential function $1\!-\!e^{-|x|/\sigma}$ with a given shape parameter $\sigma\!>\!0$, which is a popular nonconvex surrogate of $\ell_0$-norm in support vector machines, zero-one programming problems, and compressed sensing, etc. Although Malek-Mohammadi et al. [IEEE Transactions on Signal Processing, 64(21):5657--5671, 2016] once worked on this problem, the expressions they derived were regrettably inaccurate. In a sense, it was lacking a case. Using the Lambert W function and an extensive study of the piece-wise exponential function, we have rectified the formulation of the proximal operator of the piece-wise exponential function in light of their work. We have also undertaken a thorough analysis of this operator. Finally, as an application in compressed sensing, an iterative shrinkage and thresholding algorithm (ISTA) for the piece-wise exponential function regularization problem is developed and fully investigated. A comparative study of ISTA with nine popular non-convex penalties in compressed sensing demonstrates the advantage of the piece-wise exponential penalty

Writing Instruction in China: Challenges and Efforts

This qualitative study examined the current writing instruction in 1-12 level education with the data collected in three Chinese cities. The data from the Interviews of teachers and teacher-educators at different levels and from classroom observations at upper elementary to high schools in three metropolitan cities across China provide insights into 1-12 writing instruction in contemporary China. To further reveal the efforts taken by writing teacher under China’s high-stakes testing culture, this paper also presented a case study of an exemplary 10th grade writing teacher, who took tremendous efforts in nurturing true readers and writers in his classroom under the test-obsessive culture in China

Cost-effectiveness of neoadjuvant pembrolizumab plus chemotherapy with adjuvant pembrolizumab for early-stage non-small cell lung cancer in the United States

IntroductionPerioperative (neoadjuvant and adjuvant) pembrolizumab has shown favorable efficacy in patients with early-stage non-small cell lung cancer (NSCLC). This study aims to evaluate the cost-effectiveness of this treatment from the perspective of the United States healthcare payers.MethodsWe established a Markov model to compare the cost-effectiveness of perioperative pembrolizumab with that of neoadjuvant chemotherapy in 21-day cycles, utilizing data from the phase 3 KEYNOTE-671 trial. Additional data were extracted from other publications or online sources. Sensitivity analyses were conducted to evaluate the robustness of the findings. A willingness-to-pay threshold of $150,000 per quality-adjusted life-years (QALYs) gained was established. The main outcomes of this study were the measurement of QALYs, overall costs, incremental cost-effectiveness ratio (ICER), and net monetary benefit (NMB).ResultsDuring a 10-year time horizon, the total costs of perioperative pembrolizumab and the control treatment were$224,779.1 and $110,026.3, respectively. The QALYs were 4.19 and 2.97 for the two treatments, respectively, which led to an ICER of$94,222.29 per QALY gained. The NMB at the WTP threshold at $150,000 per QALY gained was$67,931.3. One-way sensitivity analysis identified the cost of pembrolizumab as the primary factor influencing cost-effectiveness. Probabilistic sensitivity analysis indicated a 97.7% probability of perioperative pembrolizumab being cost-effective at the WTP threshold.ConclusionsFrom the perspective of the United States healthcare payers, perioperative pembrolizumab is a cost-effective treatment for patients with early-stage NSCLC

Exploring Target Representations for Masked Autoencoders

Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to the target representations. In this paper, we first show that a careful choice of the target representation is unnecessary for learning good representations, since different targets tend to derive similarly behaved models. Driven by this observation, we propose a multi-stage masked distillation pipeline and use a randomly initialized model as the teacher, enabling us to effectively train high-capacity models without any efforts to carefully design target representations. Interestingly, we further explore using teachers of larger capacity, obtaining distilled students with remarkable transferring ability. On different tasks of classification, transfer learning, object detection, and semantic segmentation, the proposed method to perform masked knowledge distillation with bootstrapped teachers (dBOT) outperforms previous self-supervised methods by nontrivial margins. We hope our findings, as well as the proposed method, could motivate people to rethink the roles of target representations in pre-training masked autoencoders.The code and pre-trained models are publicly available at https://github.com/liuxingbin/dbot.Comment: The first two authors contributed equall

Nitrogen-doped carbon nanotubes with encapsulated Fe nanoparticles as efficient oxygen reduction catalyst for alkaline membrane direct ethanol fuel cells

Exploring low-cost and highly efficient non-precious metal electrocatalysts toward oxygen reduction reaction is crucial for the development of fuel cells. Herein, we report the synthesis of bamboo-like N-doped carbon nanotubes with encapsulated Fe-nanoparticles through high-temperature pyrolysis of multiple nitrogen complex consisting of benzoguanamine, cyanuric acid, and melamine. As-prepared catalyst exhibits high catalytic activity for oxygen reduction with onset potential of 1.10 V and half-wave potential of 0.93 V, as well as low H2O2 yield (<1%) in alkaline medium. The mass activity of the catalyst at 1.0 V (0.58 A g−1) can reach 43% of state-of-the-art commercial Pt/C. This catalyst also exhibits high durability and ethanol tolerance. When it was applied in alkaline membrane direct ethanol fuel cell, the peak power density could reach to 64 mW cm−2, indicating its attractive application prospect in fuel cells

NOVEL NANOSTRUCTURED HIGH-PERFORMANCE ANION EXCHANGE IONOMERS FOR ANION EXCHANGE MEMBRANE FUEL CELLS

poster abstractA novel block copolymer, styrene-ethylene/butylene-styrene (SEBS), was chosen as the starting material to prepare pendant quaternary ammonium-based ionomers with an ion-exchange-capacity (IEC) of 0.66, 1.30, and 1.54 meq g-1, denoted by QSEBS-L, QSEBS-M, and QSEBS-H, respectively. These QSEBS ionomers were demonstrated to have excellent dimensional stability against hydration without significantly sacrificing the ionic conductivity as compared to the widely studied polysulfone (PSf) based ionomers. The water uptake of the QSEBS-based ionomers depended on their functionality; a higher IEC in the ionomer resulted in more water uptake and a higher ionic conductivity. The MEAs fabricated with the QSEBS-M and QSEBS-H ionomers showed the best H2/O2 fuel cell performance with peak power densities reaching 210 mW cm-2 at 50 °C, which was significantly higher than that of the PSf-based ionomers (~30 mW cm-2). Electrochemical impedance spec-troscopy (EIS) analysis indicated that the superior fuel cell performance ob-served with the QSEBS-based ionomers can be attributed to: (1) the low in-ternal cell resistance due to good comparability of the QSEBS-based ionomers with the membranes and (2) the low mass transport and charge transport in both the anode and the cathode due to the excellent dimension-al stability and balanced conductivity-hydrophobicity originated by the unique morphology of the QSEBS-based ionomers. AFM phase imaging measurements of the QSEBS-based ionomers revealed unique nanostruc-tures containing isolated hydrophobic and continuous anion conducting hy-drophilic domains. By further optimizing the chemistry and morphology of the ionomers and the membranes, the resistance of the anode and cathode of the AEMFCs will be further reduced