Toward Reproducing Network Research Results Using Large Language Models

Reproducing research results in the networking community is important for both academia and industry. The current best practice typically resorts to three approaches: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; and (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private prototypes are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). In particular, we first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report the experiment's observations and lessons and discuss future open research questions of this proposal. This work raises no ethical issue

Measurement of non-equilibrium characteristics of thermoelectric materials

The study of material properties relies on accurate and reliable testing instruments. Sensitively capturing changes in material performance is key to understanding the mechanisms of material properties. The Seebeck coefficient and resistivity testing instrument are essential for research on many functional materials. However, the current instrument is limited in its ability to capture sensitive changes due to testing principles. Here, we prepared three different types of non-equilibrium Zn4Sb3. We found that the existing static testing instrument has problems with testing interruption or inaccuracy when the sample undergoes chemical reactions, rapid atomic diffusion, or phase transitions. The cause of interruption is that the temperature control setting cannot meet the accuracy requirements of the testing instrument. The instrument's large temperature difference setting will cause the determination of the phase transition temperature to become inaccurate. To address the limitations, we have developed a dynamic testing instrument and tested the Seebeck coefficient and resistivity of Zn4Sb3 and Ni in the range of 300 K–800 K. By ensuring the accuracy of each module of the instrument, optimizing the alternating temperature rise time, and improving the data acquisition calculation method, we have achieved accurate capture of sensitive performance changes in the non-equilibrium sample. Comparatively, dynamic testing reduces the measurement time by approximately 300 % compared to static testing. The standard deviation of measured Seebeck coefficient and resistivity is less than ±4 %. This study demonstrates that dynamic testing of the Seebeck coefficient and resistivity is an effective strategy for measuring non-equilibrium functional materials

Double Assurance of Epidural Space Detection Using Fiberoptics-Based Needle Design and Autofluorescence Technologies for Epidural Blockade in Painless Labor

Purpose: Technology of reflectance spectroscopy incorporated with auto-fluorescence spectroscopy were employed to increase the safety of epidural placement in regional anesthesia which is generally used for surgery, epidural anesthesia, post-operative pain control and painless childbirth. Method: Ex vivo study of auto-fluorescence spectroscopy was performed for the para-vertebral tissues contained fat, interspinous ligament, supraspinous ligament and ligamentumflavum by multimode microplate reader at wavelength 405 nm for the purpose of tissue differentiation. A specially designed optic-fiber-embedded needle was employed to incorporate with both reflectance and autofluorescence spectroscopies in order to probe the epidural space as double assurance demands. In vivo study was carried out in a Chinese native swine weighted about 30 kg under intubated general anesthesia with ventilation support. The reflective (405 nm) and autofluorescence signals (λ and λ*) were recorded at 5 different sites by an oscilloscope during the needle puncture procedure from skin to epidural space in the back of the swine. Results: Study of either autofluorescence spectroscopy for tissue samples or ex vivo needle puncture in porcine trunk tissues indicates that ligmentumflavum has at least 10-fold higher fluorescence intensity than the other tissues. In the in vivo study, ligamentumflavum shows a double-peak character for both reflectance and autofluorescence signals. The epidural space is located right after the drop from the double-peak. Both peaks of reflectance and fluorescence are coincident which ensures that the epidural space is correctly detected. Conclusions: The fiber-optical technologies of double-assurance demands for tissue discrimination during epidural needle puncture can not only provide an objective visual information in a real-time fashion but also it can help the operator to achieve much higher success rate in this anesthesia procedure

Direct Structure Determination from Spherulites using 3D Electron Diffraction

The spherulitic morphology is considered to be the most common morphology of crystalline materials and is particularly apparent in melt-crystallized products. Yet, historically, the polycrystalline nature of spherulites has hindered successful crystal structure determination. Here, we report for the first time the direct structure determination of a small molecule organic compound in spherulite form using 3D electron diffraction (3D ED). We employed vemurafenib (VMN), a clinical drug used for the treatment of BRAF-mutant melanoma, as a model compound. VMN has four known polymorphs (α-, β-, γ-, and δ-VMN), three of which were discovered by melt crystallization. We first solved the crystal structures of α-, β-, and γ-VMN from both open and compact spherulite samples using 3D ED, and the resulting structures were highly consistent with those solved by single-crystal X-ray diffraction. We then determined the previously unknown crystal structure of δ-VMN—the least stable polymorph which cannot be cultivated as a single crystal—directly from the spherulite sample resulting from spontaneous nucleation. We unexpectedly discovered a new polymorph during our studies, denoted as Form ε. Single crystals of ε-VMN are extremely thin and are not suitable for study by X-ray diffraction. Again, we determined the structure of ε-VMN from both open and compact spherulite forms. This successful structure elucidation of all five VMN polymorphs demonstrates the possibility of removing the time-consuming step of single crystal growth and directly determining structures from spherulite samples. Thereby, this discovery will improve the efficiency and broaden the scope of polymorphism research, especially within the field of melt-crystallization

Two Dimensional Parity Check with Variable Length Error Detection Code for the Non-Volatile Memory of Smart Data

This paper proposes a novel technology of memory protection for the Non-Volatile Memory (NVM), applied to smart sensors and smart data. Based on the asymmetry of failure rate between the statuses of bit-0 and bit-1 in the non-volatile memory, as a result of the pollution of the radiation of cosmic ray, a two-dimensional parity with variable length error detection code (2D-VLEDC) for memory protection is proposed. 2D-VLEDC has the feature of variable length of redundant bits varied with content of data word in the NVM. The experimental results show that the same error detection quality could be achieved with a 30% redundancy improvement by applying the proposed 2D-VLEDC. The proposed design is particularly suitable for the use of safety-related fields, such as the automotive electronics and industrial non-volatile memories involved in the industrial automation

Design and Implementation of Acoustic Sensing System for Online Early Fault Detection in Industrial Fans

Industrial fans play a critical role in manufacturing facilities, and a sudden shutdown of critical fans can cause significant disruptions. Ensuring early, effective, and accurate detection of fan malfunctions first requires confirming the characteristics of anomalies resulting from initial damage to rotating machinery. In addition, sensing and detection must rely on the use of sensors and sensing characteristics appropriate to various operational abnormalities. This research proposes an online industrial fan monitoring and fault detection technique based on acoustic signals as a physical sensing index. The proposed system detects and assesses anomalies resulting from preliminary damage to rotating machinery, along with improved sensing resolution bandwidth features for microphone sensors as compared to accelerometer sensors. The resulting Intelligent Prediction Integration System with Internet (IPII) is built to analyze rotation performance and predict malfunctions in industrial fans. The system uses an NI cRIO-9065 embedded controller and a real-time signal sensing module. The kernel algorithm is based on an acoustic signal enhancement filter (ASEF) as well as an adaptive Kalman filter (AKF). The proposed scheme uses acoustic signals with adaptive order-tracking technology to perform algorithm analysis and anomaly detection. Experimental results showed that the acoustic signal and adaptive order analysis method could effectively perform real-time early fault detection and prediction in industrial fans

A Programmable High-Voltage Compliance Neural Stimulator for Deep Brain Stimulation in Vivo

Deep brain stimulation (DBS) is one of the most effective therapies for movement and other disorders. The DBS neurosurgical procedure involves the implantation of a DBS device and a battery-operated neurotransmitter, which delivers electrical impulses to treatment targets through implanted electrodes. The DBS modulates the neuronal activities in the brain nucleus for improving physiological responses as long as an electric discharge above the stimulation threshold can be achieved. In an effort to improve the performance of an implanted DBS device, the device size, implementation cost, and power efficiency are among the most important DBS device design aspects. This study aims to present preliminary research results of an efficient stimulator, with emphasis on conversion efficiency. The prototype stimulator features high-voltage compliance, implemented with only a standard semiconductor process, without the use of extra masks in the foundry through our proposed circuit structure. The results of animal experiments, including evaluation of evoked responses induced by thalamic electrical stimuli with our fabricated chip, were shown to demonstrate the proof of concept of our design