Develop a Hazard Index Using Machine Learning Approach for the Hazard Identification of Chemical Logistic Warehouses

PresentationWith the rapid development of chemical process plants, the safe storage of hazardous chemicals become an essential topic. Several chemical warehouse incidents related to fire and explosion have been reported recently. Therefore, an accurate hazard identification method for the logistic warehouse is needed not only for the facility to develop a proper emergency response plan but also for the residents who live near the facility to have an effective hazard communication. Furthermore, the government can better allocate the resources for first responders to make fire protection strategies, and the stakeholders can lead to improved risk management. Hazard index is a helpful tool to identify and quantify the hazard in a facility or a process unit. The challenge for this research is to improve the current method with the novel technique to implement our purpose. The first objective of this research is to develop a “Storage Hazard Factor” (SHF) to evaluate and rank the inherent hazards of chemicals stored in logistic warehouses. In the factor calculation, the inherent hazard of chemicals is determined by various parameters (e.g., the NFPA rating, the flammability limit, and the protective action criteria values, etc.) and validated by the comparison with other indices. The current criteria for flammable hazard ratings are based on flash point, which is proved to be insufficient. Two machine learning based methods will be used for the classification of liquid flammability considering aerosolization based on DIPPR 801 database. Subsequently, SHF and other warehouse safety penalty factors (e.g., the quantity of the chemicals, the distance to the nearest fire department, etc.) are utilized to identify the Logistic Warehouse Hazard Index (LWHI) of the facilities. In the last chapter, this method is applied to real-time data from Houston Chronicle, and several statistical analyses are used to prove the hazard index is helpful for hazard identification to emergency responders and hazard communication to the public

Prognostic impact of the Controlling Nutritional Status Score in patients with biliary tract cancer: a systematic review and meta-analysis

BackgroundBiliary tract cancer (BTC) is a malignancy associated with unfavorable outcomes. Advanced BTC patients have a propensity to experience compromised immune and nutritional status as a result of obstructive jaundice and biliary inflammation. Currently, there is a lack of consensus on the impact of the Controlling Nutritional Status (CONUT) score in the context of BTC prognosis. The purpose of this study is to conduct a meta-analysis on the association between CONUT and the prognosis of patients suffering from BTC.MethodsA defined search strategy was implemented to search the PubMed, Embase, and Web of Science databases for eligible studies published until March 2023, with a focus on overall survival (OS), relapse-free survival/recurrence-free survival(RFS), and relevant clinical characteristics. The prognostic potential of the CONUT score was evaluated using hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs).ResultsIn this meta-analysis, a total of 1409 patients from China and Japan were involved in 9 studies. The results indicated that the CONUT score was significantly correlated with worse OS (HR=2.13, 95% CI 1.61-2.82, P<0.0001) and RFS (HR=1.83, 95% CI 1.44–2.31, P<0.0001) in patients with BTC. And, the analysis showed that a high CONUT score was significantly associated with clinical characteristics such as jaundice (OR=1.60, 95% CI=1.14–2.25, P=0.006), poorly differentiated tumor (OR=1.43, 95% CI=1.03–1.99, P=0.03), pT3 and 4 stage of the tumor (OR=1.87, 95% CI=1.30–2.68, P=0.0007), and complications of Clavien-Dindo classification grade IIIa or higher (OR=1.79, 95% CI=1.03–3.12, P=0.04).ConclusionThis meta-analysis indicates that a high CONUT score can serve as a significant prognostic indicator for survival outcomes among patients diagnosed with BTC

Dual-Pulse Mode Control of a High-Speed Doubly Salient Electromagnetic Machine for Loss Reduction and Speed Range Extension

In this paper, a dual-pulse mode control of a high-speed doubly salient electromagnetic machine (DSEM) for efficiency improvement over a wide speed range is investigated and implemented. The dual-pulse mode control method and operation principle are introduced. The influence of excitation angles and field current on the operation performance is analyzed by finite element method (FEM) based on the back-electromotive force (EMF) and inductance characteristics. The loss distribution for various speed and load torque requirement is attained, and the control parameters are optimized. The excitation angle can reduce the back-EMF at high speed through transformer-EMF and flux-weakening armature reaction. A prototype of 12/8-pole DSEM drive system is developed and dual-pulse mode control in high-speed operation under low DC-link voltage is implemented. Both the simulated and measured results show that the torque capacity of the DSEM is improved and the loss is reduced over a wide speed range

Non-invasive color imaging through scattering medium under broadband illumination

Due to the complex of mixed spectral point spread function within memory effect range, it is unreliable and slow to use speckle correlation technology for non-invasive imaging through scattering medium under broadband illumination. The contrast of the speckles will drastically drop as the light source's spectrum width increases. Here, we propose a method for producing the optical transfer function with several speckle frames within memory effect range to image under broadband illumination. The method can be applied to image amplitude and color objects under white LED illumination. Compared to other approaches of imaging under broadband illumination, such as deep learning and modified phase retrieval, our method can provide more stable results with faster convergence speed, which can be applied in high speed scattering imaging under natural light illumination

Problems in some food sampling inspection and solutions

Food supervision sampling is an important technical support of food safety supervision. It is the difficult and key point to make correct food classification and make correct judgment according to relevant standards. This paper summarizes the problems in food classification and technical judgment for 3 types of food including tea and its products， candies and grain products， aiming to provide reference for sampling inspection stuff. It can ensure the accuracy of food classification， reduce the risk of false judgment and improve the efficacy of sampling inspection

Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision. Codes will be open to all on https://gaozhen16.github.io/ soo