Determination of Malignant and Invasive Predictors in Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Suggested Scoring Formula

Prediction of malignancy or invasiveness of branch duct type intraductal papillary mucinous neoplasm (Br-IPMN) is difficult, and proper treatment strategy has not been well established. The authors investigated the characteristics of Br-IPMN and explored its malignancy or invasiveness predicting factors to suggest a scoring formula for predicting pathologic results. From 1994 to 2008, 237 patients who were diagnosed as Br-IPMN at 11 tertiary referral centers in Korea were retrospectively reviewed. The patients' mean age was 63.1 ± 9.2 yr. One hundred ninty-eight (83.5%) patients had nonmalignant IPMN (81 adenoma, 117 borderline atypia), and 39 (16.5%) had malignant IPMN (13 carcinoma in situ, 26 invasive carcinoma). Cyst size and mural nodule were malignancy determining factors by multivariate analysis. Elevated CEA, cyst size and mural nodule were factors determining invasiveness by multivariate analysis. Using the regression coefficient for significant predictors on multivariate analysis, we constructed a malignancy-predicting scoring formula: 22.4 (mural nodule [0 or 1]) + 0.5 (cyst size [mm]). In invasive IPMN, the formula was expressed as invasiveness-predicting score = 36.6 (mural nodule [0 or 1]) + 32.2 (elevated serum CEA [0 or 1]) + 0.6 (cyst size [mm]). Here we present a scoring formula for prediction of malignancy or invasiveness of Br-IPMN which can be used to determine a proper treatment strategy

Comparative evaluation of osmotically-driven cleaning methods for organic-inorganic fouling in pressure retarded osmosis (PRO)

Pressure retarded osmosis (PRO) is a promising technique of desalination techniques. However, one of the major problems is permeate flux decline by fouling. This study investigates the mitigation of organic and inorganic fouling in PRO by osmotically driven cleaning methods. This study comparatively evaluates the effective cleaning methods: (i) Osmotic Backwashing (OB), (ii) Reverse Osmosis Flushing (ROF), and (iii) Pressure Assisted Osmotic Backwashing (PAOB). The results showed that PAOB was a more effective method than the others in terms of cleaning efficiency and permeability. CaCO3 solution of 1000 mg/L and humic acid 100 mg/L were used as a representative inorganic and organic foulant, respectively. After cleaning, cleaning efficiency and flux decline rate were compared. The PAOB method showed the higher performance compared to other cleaning methods, had 92% recovery rate and lower flux decline after cleaning. Also, by using instrumental analysis-scanning electron microscope (SEM), it was proven to find out the proper cleaning method in PRO. Keywords: Membrane fouling, Cleaning method, PAO

Model Predictive Control Strategy for the Degradation of Pharmaceutically Active Compounds by UV/H₂O₂ Oxidation Process

Hydroxyl radical (•OH) scavenging demand can be an indicator that represents the water quality characteristics of raw water. It is one of the key parameters predicting UV/H2O2 system performance and affects the operating parameters. Based on the •OH scavenging demand, we developed a model predictive control strategy to meet the target compound removal efficiency and energy consumption simultaneously. Selected pharmaceutically active compounds (PhACs) were classified into three groups depending on the UV direct photolysis and susceptibility to •OH. Group 1 for photo-susceptible PhACs (acetaminophen, amoxicillin, diclofenac, iopromide, ketoprofen, and sulfamethoxazole); group 2 for PhACs susceptible to both direct photolysis and •OH oxidation (bisphenol A, carbamazepine, ibuprofen, naproxen, ciprofloxacin, and tetracycline); and group 3 for photo-resistant PhACs (atenolol, atrazine, caffeine, and nitrobenzene). The results of modeling to achieve 90% removal of PhACs at N and B plants were as follows. For group 2, the optimized operating parameter ranges were as follow (N plant: UV 510–702 mJ cm−2, H2O2 2.96–3.80 mg L−1, EED 1088–1302 kWh m−3; B plant: UV dose 1179–1397 mJ cm−2, H2O2 dose 3.56–7.44 mg L−1, EED 1712–2085 kWh m−3). It was confirmed that the optimal operating conditions and EED values changed according to the •OH scavenging demand

Maritime Network Analysis Based on Geographic Information System for Water Supply Using Shipboard Seawater Desalination System

Small islands are supplied with water from underground sources, simple seawater desalination facilities, or water supply shipment. However, this water supply can be interrupted because of the sudden depletion of groundwater, as groundwater level prediction is inaccurate. Additionally, seawater desalination facilities are difficult to maintain, resulting in frequent breakdowns. When the water tank capacity is below a certain level, island residents contact the water supply shipment manager to request a shipment from land. In Korea, a seawater desalination plant project using ships was newly attempted to solve the water supply problem for island regions. Through this project, an attempt was made to supply water to many island areas suffering water supply disruptions due to drought. The purpose of this study is to compare water supply routes to multiple island regions using existing water supply shipment with desalination plants on ships through network analysis based on a geographic information system. To optimize sailing route, length (m), road connection type, and name of each road section, actual operation data, distance, etc., were set up on a network dataset and analyzed. In addition, the operational model predicted the stability of water supply using the GoldSim simulator. As a result, when sailing on the optimal route based on network analysis, the existing water supply routes could be reduced (2153 km -> 968 km) by more than 55%, and operational costs can be verified to be reduced. Additionally, the validity of the network analysis results was confirmed through actual travel of the representative route

Development of Data Cleaning and Integration Algorithm for Asset Management of Power System

Asset management technology is rapidly growing in the electric power industry because utilities are paying attention to which of their aged assets should be replaced first. The global trend of asset management follows risk management that comprehensively considers the probability and consequences of failures. In the asset management system, the risk assessment algorithm operates by interfacing digital datasets from various legacy systems. In this study, among the various electric power assets, we consider transmission cable systems as a representative linear asset consisting of different segments. First, the configurations and characteristics of linear asset datasets are analyzed. Second, six types of data cleaning functions are proposed for extracting dirty data from the entire dataset. Third, three types of data integration functions are developed to simulate the risk assessment algorithm. This technique supports the integration of distributed asset data in various legacy systems into one dataset. Finally, an automatic data cleaning and integration system is developed and the algorithm could repeat the cleaning and integration process until data quality is satisfied. To evaluate the performance of the proposed system, an automatic cleaning process is demonstrated using actual legacy datasets

Development of Data Cleaning and Integration Algorithm for Asset Management of Power System

Asset management technology is rapidly growing in the electric power industry because utilities are paying attention to which of their aged assets should be replaced first. The global trend of asset management follows risk management that comprehensively considers the probability and consequences of failures. In the asset management system, the risk assessment algorithm operates by interfacing digital datasets from various legacy systems. In this study, among the various electric power assets, we consider transmission cable systems as a representative linear asset consisting of different segments. First, the configurations and characteristics of linear asset datasets are analyzed. Second, six types of data cleaning functions are proposed for extracting dirty data from the entire dataset. Third, three types of data integration functions are developed to simulate the risk assessment algorithm. This technique supports the integration of distributed asset data in various legacy systems into one dataset. Finally, an automatic data cleaning and integration system is developed and the algorithm could repeat the cleaning and integration process until data quality is satisfied. To evaluate the performance of the proposed system, an automatic cleaning process is demonstrated using actual legacy datasets