Gamma-Ray Sterilization Effects in Silica Nanoparticles/γ-APTES Nanocomposite-Based pH-Sensitive Polysilicon Wire Sensors

In this paper, we report the γ-ray sterilization effects in pH-sensitive polysilicon wire (PSW) sensors using a mixture of 3-aminopropyltriethoxysilane (γ-APTES) and polydimethylsiloxane (PDMS)-treated hydrophobic fumed silica nanoparticles (NPs) as a sensing membrane. pH analyses showed that the γ-ray irradiation-induced sensitivity degradation of the PSW pH sensor covered with γ-APTES/silica NPs nanocomposite (γ-APTES+NPs) could be restored to a condition even better than prior to γ-ray irradiation by 40-min of post-sterilization room-temperature UV annealing. We found that the trapping charges caused by γ-ray sterilization primarily concentrated in the native oxide layer for the pH sensor covered with γ-APTES, but accumulated in the γ-APTES+NPs layer for the γ-APTES+NPs-covered sensor. It is believed that mixing the PDMS-treated silica NPs into γ-APTES provides many γ-APTES/SiO2 interfaces for the accumulation of trapping charges and for post-sterilization UV oxidation, thus restoring γ-ray-induced sensor degradation. The PDMS-treated silica NPs not only enhance the sensitivity of the pH-sensitive PSW sensors but are also able to withstand the two-step sterilization resulting from γ-ray and UV irradiations. This investigation suggests γ-ray irradiation could be used as a highly-efficient sterilization method for γ-APTES-based pH-sensitive biosensors

Droplet Impingement Cooling Experiments on Nano-structured Surfaces

Spray cooling has proven to be efficient in managing thermal load in high power applications. Reliability of electronic products relies on the thermal management and understanding of heat transfer mechanisms including those related to spray cooling. However, to date, several of the key heat transfer mechanisms are still not well understood. An alternative approach for improving the heat transfer performance is to change the film dynamics through surface modification. The main goal of this study is to understand the effects of nano-scale features on flat heater surfaces subjected to spray cooling and to determine the major factors in droplet impingement cooling to estimate their effects in the spray cooling system. Single droplet stream and simultaneous triple droplet stream with two different stream spacings (500 μm and 2000 μm), experiments have been performed to understand the droplet-surface interactions relevant to spray cooling systems. Experiments have been conducted on nano-structured surfaces as well as on flat (smooth) surfaces. It is observed that nano-structured surfaces result in lower minimum wall temperatures, better heat transfer performance, and more uniform temperature distribution. A new variable, effective thermal diameter (de), was defined based on the radial temperature profiles inside the impact zone to quantify the effects of the nano-structured surface in droplet cooling. Results indicate that larger effective cooling area can be achieved using nano-structured surface in the single droplet stream experiments. In triple stream experiments, nano-structured surface also showed an enhanced heat transfer. In single stream experiments, larger outer ring structures (i.e. larger outer diameters) in the impact crater were observed on the nano-structured surfaces which can be used to explain enhanced heat transfer performance. Smaller stream spacing in triple stream experiments reveal that the outer ring structure is disrupted resulting in lower heat transfer. Lower static contact angle on the nano-structured surface has been observed, which implies that changes in surface properties result in enhanced film dynamics and better heat transfer behavior. The results and conclusions of this study should be useful for understanding the physics of spray cooling and in the design of better spray cooling systems

Engineering A Workload-balanced Push-Relabel Algorithm for Massive Graphs on GPUs

The push-relabel algorithm is an efficient algorithm that solves the maximum flow/ minimum cut problems of its affinity to parallelization. As the size of graphs grows exponentially, researchers have used Graphics Processing Units (GPUs) to accelerate the computation of the push-relabel algorithm further. However, prior works need to handle the significant memory consumption to represent a massive residual graph. In addition, the nature of their algorithms has inherently imbalanced workload distribution on GPUs. This paper first identifies the two challenges with the memory and computational models. Based on the analysis of these models, we propose a workload-balanced push-relabel algorithm (WBPR) with two enhanced compressed sparse representations (CSR) and a vertex-centric approach. The enhanced CSR significantly reduces memory consumption, while the vertex-centric approach alleviates the workload imbalance and improves the utilization of the GPU. In the experiment, our approach reduces the memory consumption from O(V^2) to O(V + E). Moreover, we can achieve up to 7.31x and 2.29x runtime speedup compared to the state-of-the-art on real-world graphs in maximum flow and bipartite matching tasks, respectively. Our code will be open-sourced for further research on accelerating the push-relabel algorithm

Time-Delayed Magnetic Control and Narrowing of X-Ray frequency Spectra in Two-Target Nuclear Forward Scattering

Controlling and narrowing x-ray frequency spectra in magnetically perturbed two-target nuclear forward scattering is theoretically studied. We show that different hard-x-ray spectral redistributions can be achieved by single or multiple switching of magnetic field in nuclear targets. Our scheme can generate x-ray spectral lines with tenfold intensity enhancement and spectral width narrower than four times the nuclear natural linewidth. The present results pave the way towards a brighter and flexible x-ray source for precision spectroscopy of nuclear resonances using modern synchrotron radiation.Comment: 5 pages, 5 figure

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Molecular population genetics and gene expression analysis of duplicated CBF genes of Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p><it>CBF/DREB </it>duplicate genes are widely distributed in higher plants and encode transcriptional factors, or CBFs, which bind a DNA regulatory element and impart responsiveness to low temperatures and dehydration.</p> <p>Results</p> <p>We explored patterns of genetic variations of <it>CBF1, -2</it>, and -<it>3 </it>from 34 accessions of <it>Arabidopsis thaliana</it>. Molecular population genetic analyses of these genes indicated that <it>CBF2 </it>has much reduced nucleotide diversity in the transcriptional unit and promoter, suggesting that <it>CBF2 </it>has been subjected to a recent adaptive sweep, which agrees with reports of a regulatory protein of <it>CBF2</it>. Investigating the ratios of K_a/K_sbetween all paired <it>CBF </it>paralogus genes, high conservation of the AP2 domain was observed, and the major divergence of proteins was the result of relaxation in two regions within the transcriptional activation domain which was under positive selection after <it>CBF </it>duplication. With respect to the level of <it>CBF </it>gene expression, several mutated nucleotides in the promoters of <it>CBF3 </it>and <it>-1 </it>of specific ecotypes might be responsible for its consistently low expression.</p> <p>Conclusion</p> <p>We concluded from our data that important evolutionary changes in <it>CBF1, -2</it>, and -<it>3 </it>may have primarily occurred at the level of gene regulation as well as in protein function.</p

Measuring Taiwanese Mandarin Language Understanding

The evaluation of large language models (LLMs) has drawn substantial attention in the field recently. This work focuses on evaluating LLMs in a Chinese context, specifically, for Traditional Chinese which has been largely underrepresented in existing benchmarks. We present TMLU, a holistic evaluation suit tailored for assessing the advanced knowledge and reasoning capability in LLMs, under the context of Taiwanese Mandarin. TMLU consists of an array of 37 subjects across social science, STEM, humanities, Taiwan-specific content, and others, ranging from middle school to professional levels. In addition, we curate chain-of-thought-like few-shot explanations for each subject to facilitate the evaluation of complex reasoning skills. To establish a comprehensive baseline, we conduct extensive experiments and analysis on 24 advanced LLMs. The results suggest that Chinese open-weight models demonstrate inferior performance comparing to multilingual proprietary ones, and open-weight models tailored for Taiwanese Mandarin lag behind the Simplified-Chinese counterparts. The findings indicate great headrooms for improvement, and emphasize the goal of TMLU to foster the development of localized Taiwanese-Mandarin LLMs. We release the benchmark and evaluation scripts for the community to promote future research.Comment: Preprint. Under revie