Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes

In this paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company. To accomplish this, we utilize a modified CUSUM (Cumulative Sum) procedure on the company's spatial-temporal order data as well as a GLR (Generalized Likelihood Ratio) based method. We model the order data using the Hawkes Process Network, a multi-dimensional self and mutually exciting point process, by discretizing the spatial data and treating each order as an event that has a corresponding node and time. We apply the methodologies on the company's most ordered item on a national scale and perform a deep dive into a single state. Because the item was ordered infrequently in the state compared to the nation, this approach allows us to show efficacy upon different degrees of data sparsity. Furthermore, it showcases use potential across differing levels of spatial detail.Comment: Accepted to AAAI 2023 Workshop on Graphs and more Complex structures for Learning and Reasonin

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods

Dacarbazine-Loaded Hollow Mesoporous Silica Nanoparticles Grafted with Folic Acid for Enhancing Antimetastatic Melanoma Response

Dacarbazine (DTIC) is one of the most important chemotherapeutic agents for the treatment of melanoma; however, its poor solubility, photosensitivity, instability, and serious toxicity to normal cells limit its clinical applications. In this article, we present a rationally designed nanocarrier based on hollow mesoporous silica nanoparticles (HMSNs) for the encapsulation and targeted release of DTIC for eradicating melanoma. The nanocarrier (DTIC@HMLBFs) is prepared by modifying HMSNs with carboxyl groups to enhance the loading of DTIC, followed by further enveloping of folic acid-grafted liposomes, which act as a melanoma active target for controlled and targeted drug release. In vitro, DTIC@HMLBFs exhibited the strongest cytotoxicity to melanoma cells compared with DTIC@HMSNs and free DTIC. The in vivo investigations demonstrate that the rationally designed nanocarrier loaded with DTIC achieves significant improvement against lung metastasis of melanoma via targeting melanoma cells and tumor-associated macrophages. This study provides a promising platform for the design and fabrication of multifunctional nanomedicines, which are potentially useful for the treatment of melanoma