AviationGPT: A Large Language Model for the Aviation Domain

The advent of ChatGPT and GPT-4 has captivated the world with large language models (LLMs), demonstrating exceptional performance in question-answering, summarization, and content generation. The aviation industry is characterized by an abundance of complex, unstructured text data, replete with technical jargon and specialized terminology. Moreover, labeled data for model building are scarce in this domain, resulting in low usage of aviation text data. The emergence of LLMs presents an opportunity to transform this situation, but there is a lack of LLMs specifically designed for the aviation domain. To address this gap, we propose AviationGPT, which is built on open-source LLaMA-2 and Mistral architectures and continuously trained on a wealth of carefully curated aviation datasets. Experimental results reveal that AviationGPT offers users multiple advantages, including the versatility to tackle diverse natural language processing (NLP) problems (e.g., question-answering, summarization, document writing, information extraction, report querying, data cleaning, and interactive data exploration). It also provides accurate and contextually relevant responses within the aviation domain and significantly improves performance (e.g., over a 40% performance gain in tested cases). With AviationGPT, the aviation industry is better equipped to address more complex research problems and enhance the efficiency and safety of National Airspace System (NAS) operations

Audio-Based Wildfire Detection on Embedded Systems

The occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This work presents a machine learning wildfire detecting data pipeline that can be deployed on embedded systems in remote locations. The proposed data pipeline consists of three main steps: audio preprocessing, feature engineering, and classification. Experiments show that the proposed data pipeline is capable of detecting wildfire effectively with high precision and is capable of detecting wildfire sound over the forest’s background soundscape. When being deployed on a Raspberry Pi 4, the proposed data pipeline takes 66 milliseconds to process a 1 s sound clip. To the knowledge of the author, this is the first edge-computing implementation of an audio-based wildfire detection syste

Audio-Based Wildfire Detection on Embedded Systems

The occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This work presents a machine learning wildfire detecting data pipeline that can be deployed on embedded systems in remote locations. The proposed data pipeline consists of three main steps: audio preprocessing, feature engineering, and classification. Experiments show that the proposed data pipeline is capable of detecting wildfire effectively with high precision and is capable of detecting wildfire sound over the forest’s background soundscape. When being deployed on a Raspberry Pi 4, the proposed data pipeline takes 66 milliseconds to process a 1 s sound clip. To the knowledge of the author, this is the first edge-computing implementation of an audio-based wildfire detection system

Effects of Functional Electrical Stimulation on Peak Torque and Body Composition in Patients with Incomplete Spinal Cord Injury

The aim of this study was to investigate the change in body composition, leg girths, and muscle strength of patients with incomplete spinal cord injury (SCI) after functional electrical stimulation cycling exercises (FESCE). Eighteen subjects with incomplete SCI were recruited. Each patient received FESCE three times per week for 8 weeks. Body composition, thigh and calf girths of bilateral legs, muscle strength of bilateral knee flexors and extensors were measured before and after 4 and 8 weeks of FESCE. A significant increase in bilateral thigh girth after 4 weeks of FESCE and significant increase in muscular peak torque of knee flexion and extension were found after 8 weeks of training. Besides, lean body mass increased significantly after complete treatment. FESCE can increase the thigh girth and muscular peak torque of patients with incomplete spinal cord injury

Tmem16F forms a Ca2+-Activated Cation Channel Required for Lipid Scrambling in Platelets during Blood Coagulation

Collapse of membrane lipid asymmetry is a hallmark of blood coagulation. TMEM16F of the TMEM16 family that includes TMEM16A/B Ca(2+)-activated Cl(−) channels (CaCCs) is linked to Scott syndrome with deficient Ca(2+)-dependent lipid scrambling. We generated TMEM16F knockout mice that exhibit bleeding defects and protection in an arterial thrombosis model associated with platelet deficiency in Ca(2+)-dependent phosphatidylserine exposure and procoagulant activity and lack a Ca(2+)-activated cation current in the platelet precursor megakaryocytes. Heterologous expression of TMEM16F generates a small-conductance Ca(2+)-activated nonselective cation (SCAN) current with subpicosiemens single-channel conductance rather than a CaCC. TMEM16F-SCAN channels permeate both monovalent and divalent cations, including Ca(2+), and exhibit synergistic gating by Ca(2+) and voltage. We further pinpointed a residue in the putative pore region important for the cation versus anion selectivity of TMEM16F-SCAN and TMEM16A-CaCC channels. This study thus identifies a Ca(2+)-activated channel permeable to Ca(2+) and critical for Ca(2+)-dependent scramblase activity during blood coagulation

The Sixth Transmembrane Segment Is a Major Gating Component of the TMEM16A Calcium-Activated Chloride Channel.

Calcium-activated chloride channels (CaCCs) formed by TMEM16A or TMEM16B are broadly expressed in the nervous system, smooth muscles, exocrine glands, and other tissues. With two calcium-binding sites and a pore within each monomer, the dimeric CaCC exhibits voltage-dependent calcium sensitivity. Channel activity also depends on the identity of permeant anions. To understand how CaCC regulates neuronal signaling and how CaCC is, in turn, modulated by neuronal activity, we examined the molecular basis of CaCC gating. Here, we report that voltage modulation of TMEM16A-CaCC involves voltage-dependent occupancy of calcium- and anion-binding site(s) within the membrane electric field as well as a voltage-dependent conformational change intrinsic to the channel protein. These gating modalities all critically depend on the sixth transmembrane segment