A technique for increasing the accuracy of the FFT-based method of numerical inversion of Laplace transforms

AbstractThe FFT-based methods of numerical inversion of Laplace transforms use the trapezoidal rule to the Bromwich integral. We present in this paper a technique for reducing the truncation error in evaluating the Bromwich integral. The technique employs the differentiation property of the Laplace transform and performs the inversion on F(n)(s), the nth order derivative of the Laplace transform of a time function f(t). The improvement in the solution accuracy by incorporating the presented technique into the FFT-based numerical Laplace inversion method is demonstrated via numerical examples

Achievable Sum Rate Optimization on NOMA-aided Cell-Free Massive MIMO with Finite Blocklength Coding

Non-orthogonal multiple access (NOMA)-aided cell-free massive multiple-input multiple-output (CFmMIMO) has been considered as a promising technology to fulfill strict quality of service requirements for ultra-reliable low-latency communications (URLLC). However, finite blocklength coding (FBC) in URLLC makes it challenging to achieve the optimal performance in the NOMA-aided CFmMIMO system. In this paper, we investigate the performance of the NOMA-aided CFmMIMO system with FBC in terms of achievable sum rate (ASR). Firstly, we derive a lower bound (LB) on the ergodic data rate. Then, we formulate an ASR maximization problem by jointly considering power allocation and user equipment (UE) clustering. To tackle such an intractable problem, we decompose it into two sub-problems, i.e., the power allocation problem and the UE clustering problem. A successive convex approximation (SCA) algorithm is proposed to solve the power allocation problem by transforming it into a series of geometric programming problems. Meanwhile, two algorithms based on graph theory are proposed to solve the UE clustering problem by identifying negative loops. Finally, alternative optimization is performed to find the maximum ASR of the NOMA-aided CFmMIMO system with FBC. The simulation results demonstrate that the proposed algorithms significantly outperform the benchmark algorithms in terms of ASR under various scenarios

Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts

Large Language Models (LLMs) have achieved significant success across various natural language processing (NLP) tasks, encompassing question-answering, summarization, and machine translation, among others. While LLMs excel in general tasks, their efficacy in domain-specific applications remains under exploration. Additionally, LLM-generated text sometimes exhibits issues like hallucination and disinformation. In this study, we assess LLMs' capability of producing concise survey articles within the computer science-NLP domain, focusing on 20 chosen topics. Automated evaluations indicate that GPT-4 outperforms GPT-3.5 when benchmarked against the ground truth. Furthermore, four human evaluators provide insights from six perspectives across four model configurations. Through case studies, we demonstrate that while GPT often yields commendable results, there are instances of shortcomings, such as incomplete information and the exhibition of lapses in factual accuracy

Investigation of Low-Cost Surface Processing Techniques for Large-Size Multicrystalline Silicon Solar Cells

The subject of the present work is to develop a simple and effective method of enhancing conversion efficiency in large-size solar cells using multicrystalline silicon (mc-Si) wafer. In this work, industrial-type mc-Si solar cells with area of 125×125 mm2 were acid etched to produce simultaneously POCl3 emitters and silicon nitride deposition by plasma-enhanced chemical vapor deposited (PECVD). The study of surface morphology and reflectivity of different mc-Si etched surfaces has also been discussed in this research. Using our optimal acid etching solution ratio, we are able to fabricate mc-Si solar cells of 16.34% conversion efficiency with double layers silicon nitride (Si3N4) coating. From our experiment, we find that depositing double layers silicon nitride coating on mc-Si solar cells can get the optimal performance parameters. Open circuit (Voc) is 616 mV, short circuit current (Jsc) is 34.1 mA/cm2, and minority carrier diffusion length is 474.16 μm. The isotropic texturing and silicon nitride layers coating approach contribute to lowering cost and achieving high efficiency in mass production

Farnesylthiosalicylic Acid-derivatized PEI-based Nanocarrier for Improved Tumor Vaccination

Vaccines hold huge potential for cancer immunotherapy by activating and stimulating our immune system. Cancer vaccines that make use of mutant tumor antigens represent a promising therapeutic strategy by stimulating immune responses against tumors to generate long-term anti-tumor immunity. However, vaccines have shown limited clinical efficacy in high risk cancer patients, which mostly due to the inefficient delivery. In this study, we will focus on vaccine delivery assisted by nanoparticles for cancer immunotherapy. Nanoparticle-mediated vaccination can efficiently deliver neoantigenic nucleic acids into lymphoid organs and antigen presenting cells. The intracellular release of vaccine and cross-presentation of antigens can be fine-tuned via polymer engineering. Polyethylenimine (PEI) was conjugated with farnesylthiosalicylic acid (FTS), an inhibitor of RAS signaling and the resulting amphiphilic conjugate could self-assemble to form micelles. Subsequent interaction with nucleic acids led to formation of polymer/nucleic acid complexes of well-controlled structure. Tumor transfection via PEI-FTS was much more effective than that by PEI, other PEI variants or naked DNA alone. Significant transfection was also observed in draining lymph nodes. In vivo delivery of an ovalbumin (OVA, a model antigen) expression plasmid by PEI-FTS led to a significant growth inhibition of OVA-expressing B16F10 melanoma. PEI-FTS represents a promising transfection agent for effective gene delivery to tumors and draining lymph nodes to mediate neoantigen vaccination

Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods

Advanced Phase Change Materials from Natural Perspectives: Structural Design and Functional Applications

Abstract Phase change materials have garnered extensive interest in heat harvesting and utilization owing to their high energy storage density and isothermal phase transition. Nevertheless, inherent leakage problems and low heat storage efficiencies hinder their widespread utilization. Nature has served as a great source of inspiration for addressing these challenges. Natural strategies are proposed to achieve advanced thermal energy management systems, and breakthroughs are made in recent years. This review focuses on recent advances in the structural design and functions of phase change materials from a natural perspective. By highlighting the structure–function relationship, advanced applications including human motion, medicine, and intelligent thermal management devices are discussed in detail. Finally, the views on the remaining challenges and future prospects are also provided, that is, phase change materials are advancing around the biomimicry design spiral

Towards hierarchical cluster based cache coherence for large-scale network-on-chip

Abstract—We introduce a novel hierarchical cluster based cache coherence scheme for large-scale NoC based distributed memory architectures. We describe the hierarchical memory organization. We show analytically that the proposed scheme has better performance than traditional counterparts both in memory overhead and communication cost