The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms

Large language models (LLMs) have garnered significant attention across various research disciplines, including the wireless communication community. There have been several heated discussions on the intersection of LLMs and wireless technologies. While recent studies have demonstrated the ability of LLMs to generate hardware description language (HDL) code for simple computation tasks, developing wireless prototypes and products via HDL poses far greater challenges because of the more complex computation tasks involved. In this paper, we aim to address this challenge by investigating the role of LLMs in FPGA-based hardware development for advanced wireless signal processing. We begin by exploring LLM-assisted code refactoring, reuse, and validation, using an open-source software-defined radio (SDR) project as a case study. Through the case study, we find that an LLM assistant can potentially yield substantial productivity gains for researchers and developers. We then examine the feasibility of using LLMs to generate HDL code for advanced wireless signal processing, using the Fast Fourier Transform (FFT) algorithm as an example. This task presents two unique challenges: the scheduling of subtasks within the overall task and the multi-step thinking required to solve certain arithmetic problem within the task. To address these challenges, we employ in-context learning (ICL) and Chain-of-Thought (CoT) prompting techniques, culminating in the successful generation of a 64-point Verilog FFT module. Our results demonstrate the potential of LLMs for generalization and imitation, affirming their usefulness in writing HDL code for wireless communication systems. Overall, this work contributes to understanding the role of LLMs in wireless communication and motivates further exploration of their capabilities

A method to monitor IGBT module bond wire failure using on-state voltage separation strategy

On-state voltage is an important thermal parameter for insulated gate bipolar transistor (IGBT) modules. It is employed widely to predict failure in IGBT module bond wires. However, due to restrictions in work environments and measurement methods, it is difficult to ensure the measurement accuracy for the on-state voltage under practical working conditions. To address this problem, an on-state voltage separation strategy is proposed for the IGBT modules with respect to the influence of collector current (Ic) and junction temperature (Tj). This method involves the separation of the on-state voltage into a dependent part and two independent parts during the IGBT module bond wire prediction. Based on the proposed separation strategy, the independent parts in the failure prediction can be removed, making it possible to directly monitor the voltage variations caused by bond wire failure. The experimental results demonstrate that the proposed diagnosis strategy can accurately predict the bond wire failure stage in an IGBT module under different conditions

An Autonomous Large Language Model Agent for Chemical Literature Data Mining

Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging researchers to discern patterns and refine synthesis processes. Artificial intelligence (AI) helps by analyzing data to optimize synthesis and increase yields. However, AI faces challenges in processing literature data due to the unstructured format and diverse writing style of chemical literature. To overcome these difficulties, we introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. This AI agent employs large language models (LLMs) for prompt generation and iterative optimization. It functions as a chemistry assistant, automating data collection and analysis, thereby saving manpower and enhancing performance. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data, and we compared our method with human experts in terms of content correctness and time efficiency. The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry

Assessing COVID-19 Vaccine Hesitancy, Confidence, and Public Engagement: A Global Social Listening Study.

Background Monitoring public confidence and hesitancy is crucial for the COVID-19 vaccine rollout. Social media listening (infoveillance) can not only monitor public attitudes on COVID-19 vaccines but also assess the dissemination of and public engagement with these opinions.ObjectiveThis study aims to assess global hesitancy, confidence, and public engagement toward COVID-19 vaccination. Methods We collected posts mentioning the COVID-19 vaccine between June and July 2020 on Twitter from New York (United States), London (United Kingdom), Mumbai (India), and Sao Paulo (Brazil), and Sina Weibo posts from Beijing (China). In total, we manually coded 12,886 posts from the five global metropolises with high COVID-19 burdens, and after assessment, 7032 posts were included in the analysis. We manually double-coded these posts using a coding framework developed according to the World Health Organization's Confidence, Complacency, and Convenience model of vaccine hesitancy, and conducted engagement analysis to investigate public communication about COVID-19 vaccines on social media.ResultsAmong social media users, 36.4% (571/1568) in New York, 51.3% (738/1440) in London, 67.3% (144/214) in Sao Paulo, 69.8% (726/1040) in Mumbai, and 76.8% (2128/2770) in Beijing indicated that they intended to accept a COVID-19 vaccination. With a high perceived risk of getting COVID-19, more tweeters in New York and London expressed a lack of confidence in vaccine safety, distrust in governments and experts, and widespread misinformation or rumors. Tweeters from Mumbai, Sao Paulo, and Beijing worried more about vaccine production and supply, whereas tweeters from New York and London had more concerns about vaccine distribution and inequity. Negative tweets expressing lack of vaccine confidence and misinformation or rumors had more followers and attracted more public engagement online. Conclusions COVID-19 vaccine hesitancy is prevalent worldwide, and negative tweets attract higher engagement on social media. It is urgent to develop an effective vaccine campaign that boosts public confidence and addresses hesitancy for COVID-19 vaccine rollouts

Community-based lung cancer screening by low-dose computed tomography in China:First round results and a meta-analysis

OBJECTIVE: To evaluate the efficiency of low-dose computed tomography (LDCT) screening for lung cancer in China by analyzing the baseline results of a community-based screening study accompanied with a meta-analysis. METHODS: A first round of community-based lung cancer screening with LDCT was conducted in Tianjin, China, and a systematic literature search was performed to identify LDCT screening and registry-based clinical studies for lung cancer in China. Baseline results in the community-based screening study were described by participant risk level and the lung cancer detection rate was compared with the pooled rate among the screening studies. The percentage of patients per stage was compared between the community-based study and screening and clinical studies. RESULTS: In the community-based study, 5523 participants (43.6% men) underwent LDCT. The lung cancer detection rate was 0.5% (high-risk, 1.2%; low-risk, 0.4%), with stage I disease present in 70.0% (high-risk, 50.0%; low-risk, 83.3%), and the adenocarcinoma present in 84.4% (high-risk, 61.5%; low-risk, 100%). Among all screen-detected lung cancer, women accounted for 8.3% and 66.7% in the high- and low-risk group, respectively. In the screening studies from mainland China, the lung cancer detection rate 0.6% (95 %CI: 0.3%-0.9%) for high-risk populations. The proportions with carcinoma in situ and stage I disease in the screening and clinical studies were 76.4% (95 %CI: 66.3%-85.3%) and 15.2% (95 %CI: 11.8%-18.9%), respectively. CONCLUSIONS: The stage shift of lung cancer due to screening suggests a potential effectiveness of LDCT screening in China. Nearly 70% of screen-detected lung cancers in low-risk populations are identified in women

Recurrent LRP1-SNRNP25 and KCNMB4-CCND3 fusion genes promote tumor cell motility in human osteosarcoma

Background The identification of fusion genes such as SYT-SSX1/SSX2, PAX3-FOXO1, TPM3/TPM4-ALK and EWS-FLI1 in human sarcomas has provided important insight into the diagnosis and targeted therapy of sarcomas. No recurrent fusion has been reported in human osteosarcoma. Methods Transcriptome sequencing was used to characterize the gene fusions and mutations in 11 human osteosarcomas. Results Nine of 11 samples were found to harbor genetic inactivating alterations in the TP53 pathway. Two recurrent fusion genes associated with the 12q locus, LRP1-SNRNP25 and KCNMB4-CCND3, were identified and validated by RT-PCR, Sanger sequencing and fluorescence in situ hybridization, and were found to be osteosarcoma specific in a validation cohort of 240 other sarcomas. Expression of LRP1-SNRNP25 fusion gene promoted SAOS-2 osteosarcoma cell migration and invasion. Expression of KCNMB4-CCND3 fusion gene promoted SAOS-2 cell migration. Conclusions Our study represents the first whole transcriptome analysis of untreated human osteosarcoma. Our discovery of two osteosarcoma specific fusion genes associated with osteosarcoma cellular motility highlights the heterogeneity of osteosarcoma and provides opportunities for new treatment modalities.BioMed Central open acces

Impaired Magnesium Protoporphyrin IX Methyltransferase (ChlM) Impedes Chlorophyll Synthesis and Plant Growth in Rice

Magnesium protoporphyrin IX methyltransferase (ChlM) catalyzes the formation of magnesium protoporphyrin IX monomethylester (MgPME) from magnesium protoporphyrin IX (MgP) in the chlorophyll synthesis pathway. However, no ChlM gene has yet been identified and studied in monocotyledonous plants. In this study, a spontaneous mutant, yellow-green leaf 18 (ygl18), was isolated from rice (Oryza sativa). This mutant showed yellow-green leaves, decreased chlorophyll level, and climate-dependent growth differences. Map-based cloning of this mutant identified the YGL18 gene LOC_Os06g04150. YGL18 is expressed in green tissues, especially in leaf organs, where it functions in chloroplasts. YGL18 showed an amino-acid sequence similarity to that of ChlM from different photosynthetic organisms. In vitro enzymatic assays demonstrated that YGL18 performed ChlM enzymatic activity, but ygl18 had nearly lost all ChlM activity. Correspondingly, the substrate MgP was largely accumulated while the product MgPME was reduced in ygl18 leaves. YGL18 is required for light-dependent and photoperiod-regulated chlorophyll synthesis. The retarded growth of ygl18 mutant plants was caused by the high light intensity. Moreover, the higher light intensity and longer exposure in high light intensity even made the ygl18 plants be more susceptible to death. Based on these results, it is suggested that YGL18 plays essential roles in light-related chlorophyll synthesis and light intensity–involved plant growth

Enrichment and characteristics of ammonia-oxidizing archaea in wastewater treatment process

High purity ammonia-oxidizing archaea (AOA) culture containing a single AOA strain was enriched from the filtering materials of biological aerated filter. The concentration of AOA reached 3.27\ua0×\ua010\ua0copies/mL, while its proportion was 91.40%. The AOA amoA gene sequence belonged to Nitrososphaera cluster. Ammonia concentration significantly influenced the growth of AOA in culture, while total organic carbon (TOC) concentration had no obvious effect. The optimum ammonia concentration, temperature, pH and DO concentration for growth of AOA were 1\ua0mM, 30\ua0°C, 7.5 and 2.65\ua0mg/L, respectively. Under the optimum growth conditions, the AOA abundance and ammonia oxidation rate were 3.53\ua0×\ua010\ua0copies/mL and 2.54\ua0×\ua010\ua0mg/(copies·d)

Association between mobile phone addiction, sleep disorder and the gut microbiota: a short-term prospective observational study

Bidirectional communication between the gut microbiota and the brain has sparked interest in exploring the link between mobile phone addiction (MPA) and sleep disorders (SD) in microbiome research. However, investigating the role of gut microbiota in this relationship using animal models presents challenges due to the unique nature of MPA, and human research in this area is scarce. We recruited 99 healthy college students to evaluate the gut microbiome using 16S rRNA gene amplicon sequencing and assess MPA and SD at baseline and after a two-month follow-up. Multiple covariate-adjusted statistical models, including linear regression, permutational multivariate analysis of variance and so on, were employed to determine microbiome associations with MPA at baseline and changes in SD at follow-up. Our findings revealed negative associations between MPA and three alpha diversity metrics, along with alterations in bacterial composition. MPA showed negative associations with the relative abundance of Bacteroidetes, while displaying positive associations with Actinobacteria and Bifidobacteriales. Conversely, Actinobacteria exhibited a negative association with increased SD. This study has established a significant link between MPA and a decrease in the alpha diversity of the gut microbiota. Actinobacteria was associated with MPA and SD, respectively. Additional investigation is needed to fully comprehend the relationship between comorbid behavioral disorders and the gut microbiota