Intelligent Virtual Assistants with LLM-based Process Automation

While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous in modern life, they still face limitations in their ability to follow multi-step instructions and accomplish complex goals articulated in natural language. However, recent breakthroughs in large language models (LLMs) show promise for overcoming existing barriers by enhancing natural language processing and reasoning capabilities. Though promising, applying LLMs to create more advanced virtual assistants still faces challenges like ensuring robust performance and handling variability in real-world user commands. This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests. The system represents an advance in assistants by providing an end-to-end solution for parsing instructions, reasoning about goals, and executing actions. LLM-based Process Automation (LLMPA) has modules for decomposing instructions, generating descriptions, detecting interface elements, predicting next actions, and error checking. Experiments demonstrate the system completing complex mobile operation tasks in Alipay based on natural language instructions. This showcases how large language models can enable automated assistants to accomplish real-world tasks. The main contributions are the novel LLMPA architecture optimized for app process automation, the methodology for applying LLMs to mobile apps, and demonstrations of multi-step task completion in a real-world environment. Notably, this work represents the first real-world deployment and extensive evaluation of a large language model-based virtual assistant in a widely used mobile application with an enormous user base numbering in the hundreds of millions

Spatio-temporal analysis of malaria incidence at the village level in a malaria-endemic area in Hainan, China

<p>Abstract</p> <p>Background</p> <p>Malaria incidence in China's Hainan province has dropped significantly, since Malaria Programme of China Global Fund Round 1 was launched. To lay a foundation for further studies to evaluate the efficacy of Malaria Programme and to help with public health planning and resource allocation in the future, the temporal and spatial variations of malaria epidemic are analysed and areas and seasons with a higher risk are identified at a fine geographic scale within a malaria endemic county in Hainan.</p> <p>Methods</p> <p>Malaria cases among the residents in each of 37 villages within hyper-endemic areas of Wanning county in southeast Hainan from 2005 to 2009 were geo-coded at village level based on residence once the patients were diagnosed. Based on data so obtained, purely temporal, purely spatial and space-time scan statistics and geographic information systems (GIS) were employed to identify clusters of time, space and space-time with elevated proportions of malaria cases.</p> <p>Results</p> <p>Purely temporal scan statistics suggested clusters in 2005,2006 and 2007 and no cluster in 2008 and 2009. Purely spatial clustering analyses pinpointed the most likely cluster as including three villages in 2005 and 2006 respectively, sixteen villages in 2007, nine villages in 2008, and five villages in 2009, and the south area of Nanqiao town as the most likely to have a significantly high occurrence of malaria. The space-time clustering analysis found the most likely cluster as including three villages in the south of Nanqiao town with a time frame from January 2005 to May 2007.</p> <p>Conclusions</p> <p>Even in a small traditional malaria endemic area, malaria incidence has a significant spatial and temporal heterogeneity on the finer spatial and temporal scales. The scan statistics enable the description of this spatiotemporal heterogeneity, helping with clarifying the epidemiology of malaria and prioritizing the resource assignment and investigation of malaria on a finer geographical scale in endemic areas.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Bike-sharing inventory management for market expansion

Over the past two decades, bike-sharing systems have grown significantly worldwide. Compared with that of other business sectors, the means by which revenue is obtained in the bike-sharing industry is unique. When the market in a region or city is saturated, an efficient way for a firm to increase revenue is to enter a new market. In this study, we design a revenue maximization-oriented decision tool to support the operational decisions of a new competitor firm entering into competition against a local firm. It is assumed that operational-level information from the local firm is unknown to the competitor firm. A new multi-stage max–min–max robust maximization model is proposed. It aims to optimize the dynamic bike inventory to maximize the worst-case revenue that the competitor firm may achieve. The worst-case revenue is treated as the baseline revenue for the competitor firm according to which rational decisions can be made. Specifically, we construct an uncertainty set to capture the uncertainty in the bike distribution of the local firm. To work with this nonconvex model, we design a myopic method, inspired by a special two-stage model that can be solved with a customized constraint-and-column approach, for obtaining the upper and lower bounds of the potential optimal revenue in our multistage model. The results of numerical experiments illustrate that the approximation approach has satisfactory computational efficiency and generates a tight bound of the optimal baseline revenue. Sensitivity analyses show that the new competitor firm should not increase its investment in bikes when the local firm increases its quantity of bikes because of high depreciation costs. Moreover, the two firms would allocate a similar proportion of bikes to each zone when either of them provides a large number of bikes. When the firms both provide a small number of bikes, the allocation of bikes by the new competitor firm is widely dispersed, while the local firm tends to allocate bikes across several zones with high demand. Furthermore, on the premise of a given target, increasing the bike acquisition can improve the robustness of revenue estimation for the competitor firm. © 2022 Elsevier Lt

Qing-Xin-Jie-Yu Granule attenuates myocardial infarction-induced inflammatory response by regulating the MK2/TTP pathway

Context Qing-Xin-Jie-Yu Granule (QXJYG) has shown promise in the treatment of myocardial infarction. However, the mechanism of action of QXJYG underlying its anti-inflammation remain unknown.Objective The study aimed to evaluate the effectiveness and mechanism of QXJYG in a mouse model of myocardial infarction and hypoxia-induced H9C2 cells.Materials and methods Myocardial infarction was induced in mice via left anterior descending coronary artery ligation, and hypoxia-induced H9C2 cells was served as the in vitro model. The cardiac function was evaluated by echocardiography, while myocardial tissue pathology was examined using HE and Masson’s trichrome staining. Changes in serum markers of cardiac injury were measured using ELISA kits. The levels of inflammatory cytokines in both the serum and cardiac tissue were quantified using the Bio-Plex Pro Mouse Chemokine assay, and hypoxia-induced inflammatory factors in H9C2 cells were assessed by RT-qPCR. Additionally, western blot analysis was conducted to evaluate the expression of proteins related to the MK2/TTP signaling pathway both in vivo and in vitro experiments.Results QXJYG significantly enhanced cardiac function in mice with myocardial infarction, as evidenced by improved myocardial tissue structure, reduced collagen fiber deposition, and lowered serum levels of creatine kinase isoenzyme MB (CK-MB), cardiac Troponin T (cTnT), and brain Natriuretic Peptide (BNP). QXJYG may reduce the expression of inflammatory factors in both the heart and serum of myocardial infarction-induced mice and attenuate hypoxia-induced levels of inflammatory factors in cardiomyocytes by decreasing the ratio of p-MK2/MK2 and increasing the protein expression of TTP.Discussion and conclusions QXJYG improved cardiac function and reduced injury, fibrosis, and inflammation after myocardial infarction, likely through modulation of the MK2/TTP signaling pathway