Methods of Comparison of the Economic Order Quantity and Just-In-Time Restocking Technologies. The Case Study

Estimation of the optimal size of order is one of the key tasks in determining the parameters of the urban freight restocking system. The existing analytical models and methods are considering each technology separately and they do not compare the Economic Order Quantity (EOQ) and Justin-tme (JIT) restocking technologies. The purpose of this research was to evaluate efficiency of the JIT and EOQ restocking technologies. The research would help in selecting the delivery model, analyzing functioning of existing JIT and EOQ models. The article presents an approach to determining the comparison in organizing supplies to the retailer. For this, the two supply models were compared. The Just-in-Time model is characterised by costs that are spend on transportation. The Economic Order Quantity model includes costs of transportation and storage in a warehouse. After calculations, application of the Just-in-Time model in the given conditions was determined.

Research on Real-Time Model of Turboshaft Engine with Surge Process

The main data source for the verification of surge detection methods still rely on test rigs of the compressor or the whole engine, which makes the development of models of the whole engine surge process an urgent need to replace the high-cost and high-risk surge test. In this paper, a novel real-time surge model based on the surge mechanism is proposed. Firstly, the turboshaft engine component level model (CLM) and the classic surge dynamic model, Moore-Greitzer (MG) model is established. Then the stability of the MG model is analyzed and the compressor characteristics in the classical MG model are extended to establish the extended MG model. Finally, this paper considers the coupling relationship of the compressor’s rotor speed, mass flow and pressure between CLM and the extended MG model to establish the real-time model of the turboshaft engine with surge process. The simulation results show that this model can realize the whole surge process of the turboshaft engine under multiple operating states. The change characteristics of the rotor speed, compressor outlet pressure, mass flow, exhaust gas temperature and other parameters are consistent with the test data, which means that the model proposed can be further applied to the research of surge detection and anti-surge control.

A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging, particularly under nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space–time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal dependence of high flows. We develop a space–time model to project seasonal streamflow extremes for several lead times up to 2 months, using a Bayesian hierarchical modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May–June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation – as potential covariates for 3 d spring maximum streamflow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space–time variability in extreme streamflow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations, thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.

A Lightweight Passive Human Tracking Method Using Wi-Fi

Target tracking is a critical technique for localization in an indoor environment. Current target-tracking methods suffer from high overhead, high latency, and blind spots issues due to a large amount of data needing to be collected or trained. On the other hand, a lightweight tracking method is preferred in many cases instead of just pursuing accuracy. For this reason, in this paper, we propose a Wi-Fi-enabled Infrared-like Device-free (WIDE) method for target tracking to realize a lightweight target-tracking method. We first analyze the impact of target movement on the physical layer of the wireless link and establish a near real-time model between the Channel State Information (CSI) and human motion. Secondly, we make full use of the network structure formed by a large number of wireless devices already deployed in reality to achieve the goal. We validate the WIDE method in different environments. Extensive evaluation results show that the WIDE method is lightweight and can track targets rapidly as well as achieve satisfactory tracking results.

The Efficacy of the WRF-ARW Model in the Genesis and Intensity Forecast of Tropical Cyclone Fani over the Bay of Bengal

An attempt has been carried out to assess the efficacy of the Weather Research and Forecasting (WRF) model in predicting the genesis and intensification events of Very Severe Cyclonic Storm (VSCS) Fani (26 April – 04 May 2019) over the Bay of Bengal (BoB). WRF model has been conducted on a single domain of 10 km horizontal resolution using the Global Data Assimilation System (GDAS) FNL (final) data (0.250 × 0.250). According to the model simulated outcome analysis, the model is capable of predicting the Minimum Sea Level Pressure (MSLP) and Maximum Sustainable Wind Speed (MSWS) pattern reasonably well, despite some deviations. The model has forecasted the Lowest Central Pressure (LCP) of 919 hPa and the MSWS of 70 ms-1 based on 0000 UTC of 26 April. Except for the model run based on 0000 UTC of 26 April, the simulated values of LCP are relatively higher than the observations. According to the statistical analysis, MSLP and MSWS at 850 hPa level demonstrate a significantly greater influence on Tropical Cyclone (TC) formation and intensification process than any other parameters. The model can predict the intensity features well enough, despite some uncertainty regarding the proper lead time of the model run. Reduced lead time model run, particularly 24 to 48 hr, can be chosen to forecast the genesis and intensification events of TC with minimum uncertainty. Journal of Engineering Science 12(3), 2021, 85-100

Dynamic Changes in Groundwater Level under Climate Changes in the Gnangara Region, Western Australia

The groundwater-dependent ecosystem in the Gnangara region is confronted with great threats due to the decline in groundwater level since the 1970s. The aim of this study is to apply multiple trend analysis methods at 351 monitoring bores to detect the trends in groundwater level using spatial, temporal and Hydrograph Analysis: Rainfall and Time Trend models, which were applied to evaluate the impacts of rainfall on the groundwater level in the Gnangara region, Western Australia. In the period of 1977–2017, the groundwater level decreased from the Gnangara’s edge to the central-north area, with a maximum trend magnitude of −0.28 m/year. The groundwater level in 1998–2017 exhibited an increasing trend in December–March and a decreasing trend in April–November with the exception of September when compared to 1978–1997. The rainfall + time model based on the cumulative annual residual rainfall technique with a one-month lag during 1990–2017 was determined as the best model. Rainfall had great impacts on the groundwater level in central Gnangara, with the highest impact coefficient being 0.00473, and the impacts reduced gradually from the central area to the boundary region. Other factors such as pine plantation, the topography and landforms, the Tamala Limestone formation, and aquifer groundwater abstraction also had important influences on the groundwater level.

Continuous Fixed-Bed Column Studies of Textile Effluent Treatment using Multi-Walled Carbon Nanotubes Originated from Rosmarinus officinalis Oil

A continuous adsorption study in a fixed-bed column was carried out using Multi-walled Carbon Nanotubes derived from Rosmarinus officinalis oil as an adsorbent for removing the textile dye Acid blue 40 from an aqueous solution. The adsorbent, MWNTs were prepared from Rosmarinus officinalis oil as a precursor to Fe/Mo catalyst supported on silica at 650 ºC under N2 atmosphere by spray pyrolysis process characterized by scanning electron microscopy, Transmission Electron microscopy, and Raman spectroscopy. The effects of adsorbent bed height (2–6 cm), initial ion concentration (20– 60 mg/L), and flow rate (10–30 mL/min) on the column performance were analyzed. The breakthrough curve was analyzed using the mathematical models of Thomas, Yoon-Nelson, and bed depth service time. The Thomas model at different conditions defined the behaviors of the breakthrough curves. The bed depth service time model showed good agreement with the experimental data. The high values of correlation coefficients (R2 0.9875) obtained indicate the validity of the bed depth service time model for the present column system.