Design, fabrication and characterization of monolithic embedded parylene microchannels in silicon substrate

This paper presents a novel channel fabrication technology of bulk-micromachined monolithic embedded polymer channels in silicon substrate. The fabrication process favorably obviates the need for sacrifical materials in surface-micromachined channels and wafer-bonding in conventional bulk-micromachined channels. Single-layer-deposited parylene C (poly-para-xylylene C) is selected as a structural material in the microfabricated channels/columns to conduct life science research. High pressure capacity can be obtained in these channels by the assistance of silicon substrate support to meet the needs of high-pressure loading conditions in microfluidic applications. The fabrication technology is completely compatible with further lithographic CMOS/MEMS processes, which enables the fabricated embedded structures to be totally integrated with on-chip micro/nano-sensors/actuators/structures for miniaturized lab-on-a-chip systems. An exemplary process was described to show the feasibility of combining bulk micromachining and surface micromachining techniques in process integration. Embedded channels in versatile cross-section profile designs have been fabricated and characterized to demonstrate their capabilities for various applications. A quasi-hemi-circular-shaped embedded parylene channel has been fabricated and verified to withstand inner pressure loadings higher than 1000 psi without failure for micro-high performance liquid chromatography (µHPLC) analysis. Fabrication of a high-aspect-ratio (internal channel height/internal channel width, greater than 20) quasi-rectangular-shaped embedded parylene channel has also been presented and characterized. Its implementation in a single-mask spiral parylene column longer than 1.1 m in a 3.3 mm × 3.3 mm square size on a chip has been demonstrated for prospective micro-gas chromatography (µGC) and high-density, high-efficiency separations. This proposed monolithic embedded channel technology can be extensively implemented to fabricate microchannels/columns in high-pressure microfludics and high-performance/high-throughput chip-based micro total analysis systems (µTAS)

Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems

Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches' performance, advantages, and disadvantages to deep-Q learning problems, especially on larger-scale maze problems larger than 4x4

Quantum Embedding with Transformer for High-dimensional Data

Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems

Composite type A thymoma and diffuse large B-cell lymphoma

AbstractThe concurrent occurrence of thymoma and diffuse large B-cell lymphoma in the thymus has not been previously reported. We describe a 74-year-old man who presented with general weakness, neck lymphadenopathy, night sweats, and body weight loss. A right anterior mediastinal mass was found on computed tomography of the chest. The immunohistochemical stains AE1/AE3, CD20, CD3, and MUM-1 confirmed the different components of the mediastinal tumor. A heavy-chain gene clonality assay and light-chain gene clonality assay confirmed the B-cell clonality of the mediastinal tumor and neck lymph node. The patient had received a complete course of chemotherapy, and the result of positron emission tomography–computed tomography showed complete remission. The pathologic report of this mass revealed composite type A thymoma and diffuse large B-cell lymphoma. If concurrent or composite thymoma and lymphoma are suspected, a thorough examination of the thymoma with a combination of ancillary studies is recommended to rule out the possibility of concurrent lymphoma

The influence of serotonin transporter polymorphisms on cortical activity: A resting EEG study

<p>Abstract</p> <p>Background</p> <p>The serotonin transporter gene (<it>5-HTT</it>) is a key regulator of serotonergic neurotransmission and has been linked to various psychiatric disorders. Among the genetic variants, polymorphisms in the <it>5-HTT </it>gene-linked polymorphic region (<it>5-HTTLPR</it>) and variable-number-of-tandem-repeat in the second intron (<it>5-HTTVNTR</it>) have functional consequences. However, their genetic impact on cortical oscillation remains unclear. This study examined the modulatory effects of <it>5-HTTLPR </it>(L-allele carriers vs. non-carriers) and <it>5-HTTVNTR </it>(10-repeat allele carriers vs. non-carriers) polymorphism on regional neural activity in a young female population.</p> <p>Methods</p> <p>Blood samples and resting state eyes-closed electroencephalography (EEG) signals were collected from 195 healthy women and stratified into 2 sets of comparisons of 2 groups each: L-allele carriers (<it>N </it>= 91) vs. non-carriers for <it>5-HTTLPR </it>and 10-repeat allele carriers (<it>N </it>= 25) vs. non-carriers for <it>5-HTTVNTR</it>. The mean power of 18 electrodes across theta, alpha, beta, gamma, gamma1, and gamma2 frequencies was analyzed. Between-group statistics were performed by an independent t-test, and global trends of regional power were quantified by non-parametric analyses.</p> <p>Results</p> <p>Among <it>5-HTTVNTR </it>genotypes, 10-repeat allele carriers showed significantly low regional power at gamma frequencies across the brain. We noticed a consistent global trend that carriers with low transcription efficiency of 5-HTT possessed low regional powers, regardless of frequency bands. The non-parametric analyses confirmed this observation, with <it>P </it>values of 3.071 × 10^-8and 1.459 × 10^-12for <it>5-HTTLPR </it>and <it>5-HTTVNTR</it>, respectively.</p> <p>Conclusions and Limitations</p> <p>Our analyses showed that genotypes with low 5-HTT activity are associated with less local neural synchronization during relaxation. The implication with respect to genetic vulnerability of 5-HTT across a broad range of psychiatric disorders is discussed. Given the low frequency of 10-repeat allele of <it>5-HTTVNTR </it>in our research sample, the possibility of false positive findings should also be considered.</p

Preparing random state for quantum financing with quantum walks

In recent years, there has been an emerging trend of combining two innovations in computer science and physics to achieve better computation capability. Exploring the potential of quantum computation to achieve highly efficient performance in various tasks is a vital development in engineering and a valuable question in sciences, as it has a significant potential to provide exponential speedups for technologically complex problems that are specifically advantageous to quantum computers. However, one key issue in unleashing this potential is constructing an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. Therefore, the split-step quantum walks (SSQW) algorithm was proposed to address this limitation. We facilitate SSQW to design parameterized quantum circuits (PQC) that can generate probability distributions and optimize the parameters to achieve the desired distribution using a variational solver. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.Comment: 11 pages, 7 figure

QoS routing with link stability in mobile ad hoc networks

Abstract. In this paper, in accordance with requirements of different users and supplying effective usage of limited network resources, we propose a stable QoS routing mechanism to determine a guaranteed route suited for mobile ad hoc wireless networks. The manner exploits the received signal strength (RSS) techniques to estimate the distance and the signal change of the velocity to evaluate the breakaway. To ensure the QoS it chooses a steady path from the source to the destination and tries to reserve the bandwidth. Ultimately, it is clear to find that the performance never decrease even the growth of the overhead and the movement of users via the simulated by ns-2. Introduction Mobile Ad Hoc Wireless Networks (MANET), also called the Ad hoc network, is lots of moving nodes (mobile hosts) communicating with their adjacent mobile node by radio wave. Every node can contact each other without existence infrastructural network. In the Ad hoc network, it differs from cellular wireless networks that need base stations to deliver and receive the packets. Each node plays the role as a router. When one of them wants to deliver packets to destination out of its coverage, intermediate nodes will forward this packet to the next node till the destination node receive it. In traditional cellular wireless networks, generally we need to establish base stations in advance. Fixed nodes far and near connect to the backbone and become a wireless network environment. In this network the customer who wants to communicate with another must locate in the base station coverage. If user moved out of base station&apos;s service scope, he can&apos;t take the communication. Consequently, we need to establish enough base stations to achieve the objective. Ad hoc networks do not demand fixed network infrastructures and centralized management mechanisms, as well as can be built anytime, anywhere rapidly. Ad hoc networks also have the feature of self-creating, self-organization and elf-management as well as deploy and remove network easily. Ad hoc network has above advantages. However, the Ad hoc network environment has the following restricts [1], including of Network topology instable, Limited energy constrained and Limited network bandwidth-constrained QoS Routing with Link Stability in Mobile A

Game Solving with Online Fine-Tuning

Game solving is a similar, yet more difficult task than mastering a game. Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optionally a full strategy to follow in order to achieve that outcome. The AlphaZero algorithm has demonstrated super-human level play, and its powerful policy and value predictions have also served as heuristics in game solving. However, to solve a game and obtain a full strategy, a winning response must be found for all possible moves by the losing player. This includes very poor lines of play from the losing side, for which the AlphaZero self-play process will not encounter. AlphaZero-based heuristics can be highly inaccurate when evaluating these out-of-distribution positions, which occur throughout the entire search. To address this issue, this paper investigates applying online fine-tuning while searching and proposes two methods to learn tailor-designed heuristics for game solving. Our experiments show that using online fine-tuning can solve a series of challenging 7x7 Killall-Go problems, using only 23.54% of computation time compared to the baseline without online fine-tuning. Results suggest that the savings scale with problem size. Our method can further be extended to any tree search algorithm for problem solving. Our code is available at https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver.Comment: Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Study on the continuous phase evolution and physical properties of gas-atomized high-entropy alloy powders

In this study, AlCoCrFeNi high entropy alloy (HEA) powders were fabricated by gas atomization process, and the effects of annealing heat treatment on phase evolution and mechanical properties were investigated. The as-atomized powders have pure BCC phase with a spherical shape and equal composition distribution, and then the FCC and sigma phase sequentially generated after annealing. The mechanical property such as hardness was evidently enhanced, which was caused by precipitation hardening effect. After the raw powders were annealed at 600 °C, the FCC (Al-Ni) phase began to precipitate, the its phase intensity raised with the annealing temperature. Then, the sigma phase (Fe-Cr) formed as the annealing temperature reached 800 °C. Both mechanical properties and lattice constant were influenced by heating effect. According to the results, the lattice became loose with the increasing temperature. In summary, the mechanical properties and phase constitutions of gas-atomized AlCoCrFeNi HEA powders can be adjusted via annealing process, resulting in precipitation hardening effect

Efficient Prodrug Activator Gene Therapy by Retroviral Replicating Vectors Prolongs Survival in an Immune-Competent Intracerebral Glioma Model.

Prodrug activator gene therapy mediated by murine leukemia virus (MLV)-based retroviral replicating vectors (RRV) was previously shown to be highly effective in killing glioma cells both in culture and in vivo. To avoid receptor interference and enable dual vector co-infection with MLV-RRV, we have developed another RRV based on gibbon ape leukemia virus (GALV) that also shows robust replicative spread in a wide variety of tumor cells. We evaluated the potential of GALV-based RRV as a cancer therapeutic agent by incorporating yeast cytosine deaminase (CD) and E. coli nitroreductase (NTR) prodrug activator genes into the vector. The expression of CD and NTR genes from GALV-RRV achieved highly efficient delivery of these prodrug activator genes to RG-2 glioma cells, resulting in enhanced cytotoxicity after administering their respective prodrugs 5-fluorocytosine and CB1954 in vitro. In an immune-competent intracerebral RG-2 glioma model, GALV-mediated CD and NTR gene therapy both significantly suppressed tumor growth with CB1954 administration after a single injection of vector supernatant. However, NTR showed greater potency than CD, with control animals receiving GALV-NTR vector alone (i.e., without CB1954 prodrug) showing extensive tumor growth with a median survival time of 17.5 days, while animals receiving GALV-NTR and CB1954 showed significantly prolonged survival with a median survival time of 30 days. In conclusion, GALV-RRV enabled high-efficiency gene transfer and persistent expression of NTR, resulting in efficient cell killing, suppression of tumor growth, and prolonged survival upon CB1954 administration. This validates the use of therapeutic strategies employing this prodrug activator gene to arm GALV-RRV, and opens the door to the possibility of future combination gene therapy with CD-armed MLV-RRV, as the latter vector is currently being evaluated in clinical trials