Recurrence of odontogenic keratocysts and possible prognostic factors : review of 455 patients

To describe epidemiological features of 565 Chinese patients with odontogenic keratocysts (OKC), to investigate possible prognostic factors related to recurrence, and to analyse features of recurrent OKC (rOKC). A retrospective chart review of 565 cases of OKC treated between 2003 and 2015 was undertaken. The probability of recurrence related to prognostic factors including large size, cortical perforation combined with involved teeth in the lumen of the cyst, inflammation, sites of the involved lesion, sex, and daughter cyst variables were analysed. The subsequent relapse of each OKC was compared. Patients ranged in age from 7 to 81 years (mean age, 28.4 years) and, of those affected, 66.9% were male and 33.1% were female. Mandibular OKC occurred in 63.01% and 36.99% occurred in the maxilla, 80.53% of patients had non-rOKC, 10.44% rOKC, and 9.03% had multiple OKC lesions. Enucleation with preservation of the involved teeth in the cystic lesion combined with cortical perforation was statistically associated with high recurrence rate, as were daughter cysts, and multilocular lesions. The number of recurrences and the average time (in years) to relapse decreased from the first relapse of OKC to the third relapse, and the difference was significant (P<.05). Preservation of the involved teeth combined with cortical perforation appeared to be a potential prognostic factor associated with high recurrence. The follow-up evaluation period for rOKC with ? 2 previous treatments should be shorter than for first-time rOKC. The decreasing average duration (years postoperatively) to relapse was related to the number of rOKCs, timing of relapse, and rOKC type

Electric Bus Charging Scheduling Strategy with Stochastic Arrival Time and State of Charge

International audienceTo alleviate the range anxiety of drivers and time-consuming charging for electric buses (eBuses), opportunity fast-charging has gradually been utilized. Considering that eBuses have operational tasks, identifying an optimal charging scheduling will be needed. However, in the real world, arrival time and state of charge (SOC) of eBuses are uncertain. Therefore, it is challenging for the charging station to efficiently schedule charging tasks. To solve the problem, this paper develops a two-stage stochastic eBus charging scheduling model. In the first stage, eBuses are assigned to designated chargers. After the arrival time and SOC are realized, the second stage determines the charging sequence of eBuses on each charger. The objective is to minimize the penalty cost of tardiness by determining the charging start time and the corresponding charging duration time. Then, a sample average approximation (SAA) algorithm is applied. Additional numerical experiments are performed to verify the efficiency of the stochastic programming model and algorithm

Minimax Relative Regret Approach for Resilient Supply Chain Design

International audienceUncertain disruption risks negatively influence the performance of supply chains by reducing the facilities' capacity. Proactive and recovery strategies for building resilient supply chains have received increasing attention from academia. Therefore, in this work, we study a resilient supply chain designing problem considering the uncertain occurrence and extent of disruptions, and the stochastic demand during time periods. For the problem, a two-stage min-max relative regret robust model is developed. The first stage is to determine the optimal facility locations and investments in protection systems, and the second stage decides the recovery actions and the quantity of products transported between different facilities and customers. The objective is to minimise the worst-case total relative regret cost. Finally, a case study is conducted, and some insights are given through the results of sensitivity analysis

Robust actions for improving supply chain resilience and viability

International audienceIt is vital for supply chains (SCs) to survive the dramatic and long-term impacts from severe disruptive events, such as COVID-19 pandemic. SC viability, an extension of SC resilience, is increasingly attracting attention from both academics and practitioners. To improve SC viability, the government can perform a series of costly interventions on SCs. Due to data scarcity on unpredictable disruptive events, especially under the pandemic, the information related to SC partners may not be accurately obtained. In this paper, we investigate a novel SC resilience and viability improving problem under severe disruptive events, in which only the probability intervals of SC partners’ states are known. The problem consists of the selection of appropriate intervention actions, respecting a limited capital budget. The objective is to minimize the worst-case disruption risk of the manufacturer. Specifically, Causal Bayesian Network (CBN) is applied to quantify the SC ripple effects; Do-calculus technique is used to measure the benefits of government intervention actions; and robust optimization is employed to minimize the disruption risk under the worst-case condition. For the problem, a new robust optimization model that combines the CBN and the Do-calculus is constructed. Based on analyses of problem features, an efficient problem-specific branch-and-bound (PS-BAB) algorithm is proposed to solve the problem exactly. Experimental results show the efficiency of our methodology and managerial insights are drawn

A signomial programming-based approach for multi-echelon supply chain disruption risk assessment with robust dynamic Bayesian network

International audienceDisruption risk assessment is a primary and crucial step before taking measures to mitigate the negative impact of disruptions propagating along supply chains (SCs). Recently, robust dynamic Bayesian network (DBN) provides a valid tool for disruption risk estimation under the ripple effect in a data-scarce environment. However, existing literature has not considered such disruption risk assessment for multi-echelon SCs that are usually structurally complicated and thus vulnerable to disruptions with ripple effects. Motivated by this fact, we study the disruption risk assessment problem under the ripple effect for a multi-echelon SC with several suppliers and one manufacturer, in which only probability intervals of the suppliers’ states and those of the related disruption propagations are known. The aim is to acquire a robust risk estimation, measured by the worst-case total weighted probabilities for the manufacturer in the disrupted state over a time horizon. For the problem, a nonconvex nonlinear programming model is established to obtain the worst-case risk estimation. To efficiently solve the problem, a novel signomial programming (SP)-based approach is developed for finding near-optimal solutions. Numerical experiments on instances in the literature and our randomly generated instances are conducted to evaluate the efficiency of the proposed method. Besides, managerial insights are drawn

Bi-objective optimization for supply chain ripple effect management under disruption risks with supplier actions

International audienceIn practice, supplier actions are often taken to reduce the impact of disruption propagation in the supply chain and ensure continuity of material flows. However, these actions can be very costly. The selection of appropriate supplier actions to reduce the disruption risk is of great interest to both academics and practitioners. However, there is no study on optimally selecting supplier actions to find the best balance between the cost of these actions and the disruption risk. This work investigates a new bi-objective supply chain ripple effect management problem, considering supplier actions. The two objectives are to minimize the manufacturer's disruption risk and the expected total action cost. To efficiently address the problem, an integrated approach that combines Markov decision process (MDP), dynamic Bayesian network (DBN), and bi-objective nonconvex mixed-integer programming model, along with optimization techniques, is designed. From this study, the following managerial insights can be drawn: (i) for different desired risk reductions, cost-effective supplier actions are different and can be identified by the proposed approach to support decision-making; (ii) the risk decreases with the increase of the total action cost before the risk threshold is achieved, and the disruption risk cannot be smaller than the risk threshold, even if more costly actions are taken; (iii) the costs of supplier actions have no impact on the risk threshold, while the state probability distributions of suppliers and the manufacturer affect the risk threshold

Huanglian-Hongqu herb pair improves nonalcoholic fatty liver disease via NF-κB/NLRP3 pathway in mice: network pharmacology, molecular docking and experimental validation

Abstract The Huanglian-Hongqu herb pair (HH) is a carefully crafted traditional Chinese herbal compound designed to address disorders related to glucose and lipid metabolism. Its primary application lies in treating hyperlipidemia and fatty liver conditions. This study explored the potential mechanism of HH in treating non-alcoholic fatty liver disease (NAFLD) through network pharmacology, molecular docking, and in vivo animal experiments. Ultrahigh performanceliquid chromatography-quadrupole/orbitrapmass spectrometry (UPLC-Q-TOF-MS) was employed to identify the chemical composition of HH. Network pharmacology was used to analyze the related signaling pathways affected by HH. Subsequently, the prediction was verified by animal experiment. Finally, we identified 29 components within HH. Network pharmacology unveiled interactions between HH and 153 NAFLD-related targets, highlighting HH’s potential to alleviate NAFLD through NF-κB signaling pathway. Molecular docking analyses illuminated the binding interactions between HH components and key regulatory proteins, including NF-κB, NLRP3, ASC, and Caspase-1. In vivo experiments demonstrated that HH alleviated NAFLD by reducing serum and liver lipid levels, improving liver function, and lowering inflammatory cytokine levels in the serum. Moreover, HH administration downregulated mRNA and protein levels of the NF-κB/NLRP3 pathway. In conclusion, our findings demonstrated that HH has potential therapeutic benefits in ameliorating NAFLD by targeting the NF-κB/NLRP3 pathway, facilitating the broader application of HH in the field of NAFLD

A Review of Research on Cavity Growth in the Context of Underground Coal Gasification

Underground Coal Gasification (UCG) is a leading-edge technology for clean and effective utilization of coal resources, especially for deep coal seams with a depth of more than 1000 m. Since the core operation place of UCG is the cavity, mastering the cavity growth pattern is a prerequisite to ensure the efficient and economic development of UCG. At present, scholars have conducted numerous research works on cavity growth, but the simulation conditions limit the research results. Hence, it is necessary to summarize and sort out the research results of cavity growth patterns, which contribute to deepening the understanding of UCG and pointing out the direction for subsequent research. First of all, this paper summarizes the development history of UCG technology and describes the cavity growth mechanism from chemical reactions and thermo-mechanical failure. Then, the research methods of cavity growth are summarized from three aspects: a field test, laboratory experiment, and numerical simulation. The results show that the appearance of the cavity is teardrop-shaped, and its growth direction is obviously related to the gas injection method, including the injection direction and rate. Subsequently, the factors affecting the cavity growth process are expounded from the geological factors (permeability, moisture content, and coal rank) and operating factors (temperature, pressure, gasification agent’s composition, and gasification agent’s flow pattern). Finally, the existing problems and development trends in the cavity growth are discussed. The follow-up research direction should focus on clarifying the cavity growth mechanism of the controlled-retractable-injection-point (CRIP) method of UCG in the deep coal seam and ascertain the influence of the moisture content in the coal seam on cavity growth

A Review of Research on Cavity Growth in the Context of Underground Coal Gasification

Underground Coal Gasification (UCG) is a leading-edge technology for clean and effective utilization of coal resources, especially for deep coal seams with a depth of more than 1000 m. Since the core operation place of UCG is the cavity, mastering the cavity growth pattern is a prerequisite to ensure the efficient and economic development of UCG. At present, scholars have conducted numerous research works on cavity growth, but the simulation conditions limit the research results. Hence, it is necessary to summarize and sort out the research results of cavity growth patterns, which contribute to deepening the understanding of UCG and pointing out the direction for subsequent research. First of all, this paper summarizes the development history of UCG technology and describes the cavity growth mechanism from chemical reactions and thermo-mechanical failure. Then, the research methods of cavity growth are summarized from three aspects: a field test, laboratory experiment, and numerical simulation. The results show that the appearance of the cavity is teardrop-shaped, and its growth direction is obviously related to the gas injection method, including the injection direction and rate. Subsequently, the factors affecting the cavity growth process are expounded from the geological factors (permeability, moisture content, and coal rank) and operating factors (temperature, pressure, gasification agent's composition, and gasification agent's flow pattern). Finally, the existing problems and development trends in the cavity growth are discussed. The follow-up research direction should focus on clarifying the cavity growth mechanism of the controlled-retractable-injection-point (CRIP) method of UCG in the deep coal seam and ascertain the influence of the moisture content in the coal seam on cavity growth

A Review of Research on Cavity Growth in the Context of Underground Coal Gasification

Underground Coal Gasification (UCG) is a leading-edge technology for clean and effective utilization of coal resources, especially for deep coal seams with a depth of more than 1000 m. Since the core operation place of UCG is the cavity, mastering the cavity growth pattern is a prerequisite to ensure the efficient and economic development of UCG. At present, scholars have conducted numerous research works on cavity growth, but the simulation conditions limit the research results. Hence, it is necessary to summarize and sort out the research results of cavity growth patterns, which contribute to deepening the understanding of UCG and pointing out the direction for subsequent research. First of all, this paper summarizes the development history of UCG technology and describes the cavity growth mechanism from chemical reactions and thermo-mechanical failure. Then, the research methods of cavity growth are summarized from three aspects: a field test, laboratory experiment, and numerical simulation. The results show that the appearance of the cavity is teardrop-shaped, and its growth direction is obviously related to the gas injection method, including the injection direction and rate. Subsequently, the factors affecting the cavity growth process are expounded from the geological factors (permeability, moisture content, and coal rank) and operating factors (temperature, pressure, gasification agent's composition, and gasification agent's flow pattern). Finally, the existing problems and development trends in the cavity growth are discussed. The follow-up research direction should focus on clarifying the cavity growth mechanism of the controlled-retractable-injection-point (CRIP) method of UCG in the deep coal seam and ascertain the influence of the moisture content in the coal seam on cavity growth