A grey approach for forecasting in a supply chain during intermittentdisruptions

The supply chains today have become vulnerable to frequent disruptions, and with continuing emphasis on efficiency, lacks robustness to deal with them. A part of the solution lies in forecasting the disruption beforehand and the other part in knowing which policies will suit such disrupted conditions best. Accurate and immediate forecasts are a must in a supply chain and hence play a huge role in stabilizing. This study compares the performance of three established forecasting methods (moving average, weighted moving average and exponential smoothing) as well as grey prediction method, during disruptions and stable situations. The experiments are performed in the form of discrete event simulation, on a four stage beer game settings. The results show that moving average and weighted moving average methods become incompetent during disruptions, and are useful only during stable times, when the demand hovers around a predefined mean value. Exponential smoothing and grey method seems to give better results during disruptions and also during stable times in upstream tiers. Grey prediction method in particular is the best method when the disruption frequency is high and also when the disruption impact is gradual rather than sudden.     

A multi-label ensemble predicting model to service recommendation from social media contents

The Journal of Supercomputing - Consumer sentiment is one of the essential measures of predictive recommendations in travel and tourism. Nowadays, a massive amount of data is available on the...     

Neural network based non-invasive method to detect anemia from images of eye conjunctiva

Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time-consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non-invasive method, but color perception may vary from person to person. This study aims to develop a computerized non-invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non-invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.     

Significance of interface barrier at electrode of hematite hydroelectric cell for generating ecopower by water splitting

Recent increase in energy demand and associated environmental degradation concern has triggered more research towards alternative green energy sources. Eco‐friendly energy in facile way has been generated from abundantly available iron oxides using only few microliters of water without any external energy source. Hydroelectric cell (HEC) compatible to environment benign, low cost oxygen‐deficient mesoporous hematite nanoparticles has been used for splitting water molecules spontaneously to generate green electricity. Hematite nanoparticles have been synthesized by coprecipitation method. Chemidissociated hydroxyl group presence on hematite surface has been confirmed by infrared spectroscopy (IR) and X‐ray photoelectron spectroscopy (XPS). Surface oxygen vacancies in nanostructured hematite have been identified by transmission electron microscopy (TEM), XPS, and photoluminescence (PL) measurement. Hematite‐based HEC delivers 30 mA current with 0.92 V emf using approximately 500 μL water. Maximum off‐load output power 27.6 mW delivered by 4.84 cm² area hematite‐based HEC is 3.52 times higher than reported 7.84 mW power generated by Li‐magnesium ferrite HEC. Electrochemistry of HEC in different irreversible polarization loss regions has been estimated by applying empirical modeling on V‐I polarization curve revealing the reaction and charge transport mechanism of cell. Tafel slope 22.7 mV has been calculated by modeling of activation polarization overvoltage region of 0.11 V. Low activation polarization indicated easy charge/ion diffusion and faster reaction kinetics of Ag/Zn electrode owing to lesser energy barrier at interface. Dissociated H₃O⁺ ions diffuse through surface via proton hopping, while OH^? ions migrate through interconnected defective crystallite boundaries resulting into high output cell current.     

Structural and Luminescence Studies on Barium Sodium Borosilicate Glasses Containing Uranium Oxides

Barium sodium borosilicate glasses containing different amounts of uranium oxides were prepared by conventional melt quench method and investigated for their structural aspects by ²⁹Si and ¹¹B MAS NMR technique combined with steady‐state luminescence and lifetime measurements. Based on MAS NMR studies, it is confirmed that uranium ions act as network modifier up to 15 wt% and beyond which a separate uranium containing phase is formed. From the luminescence studies, it is inferred that uranyl species is in a highly distorted environment. For more than 15 wt% uranium oxide incorporation, weaker U–O–U linkages are formed at the expense stronger U–O–Si/B linkages, as suggested by the excited state lifetime value of the uranyl species as well as red shift in emission peak maximum. For glass samples containing more than 25 wt% uranium oxides, crystalline barium uranium silicate gets phase separated from glass matrix as confirmed by XRD studies.     

Fabrication and Circuit Modeling of NMOS Inverter Based on Quantum Dot Gate Field-Effect Transistors

This paper presents the fabrication of a negative-channel metal–oxide–semiconductor (NMOS) inverter based on quantum dot gate field-effect transistors (QDG-FETs). A QDG-FET produces one intermediate state in its transfer characteristic. NMOS inverters based on a QDG-FET produce three states in their transfer characteristic. The generation of the third state in the inverter characteristic makes this a promising circuit element for multivalued logic implementation. A circuit simulation result based on the Berkley simulation (BSIM) circuit model of the QDG-FET is also presented in this paper, predicting the fabricated device characteristic.     

Non-dendritic Structural Changes in Al–7Si Alloy Cast Through Rapid Slurry Formation (RSF) Process

Rapid slurry formation (RSF) is a semi-solid metal forming technique, which is based on a so-called solid enthalpy exchange material. The beauty of the process is that the slurry is controlled by relative added amounts of the alloy system used. It is not necessary to accurately control the heat flux to surrounding media such as, crucible or mould. In the present investigation 15?% solid fraction in Al?C7Si alloy slurry has been produced by RSF method. The slurry so prepared was cast in gravity die cast mould with water cooling arrangement. Thus ingot of size 30?cm?×?5?cm?×?2.5?cm could be produced. The microstructure showed rosette and globular type ??-Al morphology. Tensile properties evaluated were higher than as cast Al?C7Si alloy. During slurry formation coarsening of particles was experimentally investigated and also theoretically described by Lifshitz-Slyozov-Wagner (LSW) theory and convective mass flow equations.