Hemodialysis (HD) is the most commonly used treatment in patients with end-stage renal failure or disease (ESRD) worldwide. Blood-borne viral diseases are the major causes of mortality and morbidity in patients on HD. This study aims to analyze the prevalence and to concentrate on the key risk factors that are responsible for hepatitis B virus (HBV), hepatitis C virus (HCV), and human immunodeficiency virus (HIV) infection in patients on HD visiting two dialysis centers in the city of Quetta in southwestern Pakistan. The overall incidence of HBV was found to be 16.1%, the overall incidence of HCV was found to be 43.2%, and two patients (1.6%) were found to be positive for both HBV and HCV. HIV was not found among patients seen at both hospitals during the study period. The main risk factors for development of a viral infection were the length of time on HD (p = 0.007), number of sessions (p = 0.001), and level of education (p = 0.092). Biochemical and hematological parameters including urea, creatinine, uric acid, and calcium levels, red blood cell count, white blood cell count, hemoglobin levels, and platelet count were also studied in patients on HD. HD is becoming one of the major factors causing a viral infection because a patient can possibly become infected during an HD session via a blood transfusion, dialysis machines, instruments and/or other contaminated equipment. In order to control the spread of viral infections, increased public awareness, vaccinations, and health education programs for both health care providers and patients are needed, and proper screening programs should be instituted before dialysis is performed.

No abstract

No abstract

The shipping industry is fairly volatile pertaining to shipment pricing. To handle this volatility, two types of pricing strategies are employed in the shipping sector, contract market pricing and spot pricing. The contract market offers a fixed shipment price for a known cargo task over a set period, with secured booking space in periods of high demand. The spot market has a fluctuating shipment price, where one can benefit from lower prices than contract rate shipment prices in the low season, but face escalating shipment prices and less certainty of being able to secure a booking on a particular vessel in peak season, as space is reserved for contracted customers. However, both the pricing strategies followed have no relationship between current shipment demand and available shipping capacity and shipment prices are quoted based on predefined price lists (hard copy). This paper addresses the research gap of optimal spot shipment price calculation based on current shipment demand and available shipping capacity. To do so, we have developed a model that utilizes historical data to calculate spot pricing for container shipments. The proposed model is capable of calculating shipment spot prices based on shipment demand and capacity. Data from various sources was gathered to generate a shipping data set for three years (i.e. from 2016 until 2018). Regression and correlation analysis are used to quantify research outcomes. Results have shown that the proposed model significantly increases the correlation between shipment price and shipment demand from 0.33 to 0.88 and available capacity from -0.12 to 0.35 respectively.

No abstract

Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and marketing data. However, the use of demand forecasting has little been studied in the container shipping industry. Improved visibility into the demand for container shipments has been a long-held objective of industry stakeholders. This paper addresses the shortcomings of both short-term and long-term shipment demand forecasting for the Australian container shipping industry. In this study, we compare three forecasting models, namely, the seasonal auto-regressive integrated moving average (SARIMA), Holt–Winters’ seasonal method and Facebook’s Prophet, to find the best fitting model for short-term and long-term import demand forecasting in the Australian shipping industry. Demand data from three years, i.e., 2016–2018, is used for the Asia–Oceania trade lane. The mean absolute percentage error (MAPE), root mean squared error (RMSE) and 2-fold walk-forward cross-validation are used for the model evaluation. The experiment results observed from the selected metrics suggest that Prophet outperforms the other models in its comparison for container shipment demand forecasting.

No abstract

No abstract