Stability of electronic nose (e-nose) as determined by considering date-pits heated at different temperatures

Variability and sensitivity of a portable electronic nose (32 sensors) was assessed by considering different variables for measurement (i.e., reference, standard, first and second purges, sample draw time, waiting time for the volatiles released in the headspace, and mass of sample or headspace volume of the jar containing sample). In this study, dried date-pits were used a model sample. The highest stability was achieved when both reference and standard (i.e., dried date-pits, no heating treatment) were used before test sample measurement. Higher sample draw time more than 10 s significantly decreased the stability, whereas optimum second purge was observed at 50 s. Optimum time to generate volatile was observed as 24 h. A sample of 100 g increased the signal intensity compared to the 50 g sample for the 60°C and 100°C treated samples, while an opposite trend was observed for the 150°C treated sample. Finally, the responses of volatile components in date-pits heated at different temperatures (60°C, 100°C, and 150°C) were measured using the optimum operating conditions. Principal component analysis explored the relationships between the volatile features and classified date-pits heated at different temperatures. The results showed that an electronic nose was able to classify date-pits based on their volatile components generated by different degrees of heating (93.3% accuracy).     

A hidden Markov model to detect regime changes in cryptoasset markets

The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well-described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.     

The control of abnormal grain growth in low-voltage SnO2 varistors by microseed addition

In this research, the addition effects of three different quantities of micron-sized seeds (microseeds) to a SnO₂ varistor prepared from nanomaterials on the microstructure and electrical properties were studied. Moreover, surge-withstanding capability of low-voltage SnO₂ varistors was investigated. The X-ray diffraction pattern disclosed a single phase SnO₂ for microseed grains. The morphological features of samples were characterized using scanning electron microscopy. The abnormal distribution of grain size with elongated grains of SnO₂ in fine grains matrix was observed in sintered samples without microseeds. The low content of microseed addition (0.3 wt%) had not controlled abnormal grain growth, however, it increased mean grain size to 37 µm. Although the high content of microseeds (7.5 wt%) stopped abnormal grain growth, it had a negative effect on relative density and mean grain size. The normal grain size distribution with maximum mean grain size (45 µm) was obtained in samples containing 1.5 wt% microseeds. These samples showed the lowest breakdown field (240 V/cm) and the highest surge-withstanding capability (1.5 kA/cm²). Furthermore, the standard deviation of the electrical parameters of these samples was improved due to normal grain-size distribution.     

Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems

Hydropower energy generation depends on the available water resources. Therefore, planning and operation of the water resource systems are paramount tasks for energy management. Since reservoirs are one of the important components of water resources systems, extracting optimal operating policies for proper management of energy generated from these systems is an imperative step. Optimizing reservoir system operation (ORSO) is a non-linear, large-scale, and non-convex problem with a large number of constraints and decision variables. To solve ORSO problem effectively, a robust diversity-based, sine-cosine algorithm (RDB-SCA) is developed in the present study by introducing several strategies to balance the global exploration and local exploitation ability and to achieve accurate and reliable solutions. An efficient linear operation rule is coupled with the RDB-SCA to maximize the energy generation. The proposed method is then applied to a real-world, multi-reservoir system to extract optimal operational policies and, consequently, maximize the energy production. It is shown that the RDB-SCA is able to generate 24, 14, and 6% more energy than the original SCA, respectively for 2-, 3-, and 4-reservoir systems. The present findings are useful to suggest guidelines for efficient operation of hydropower multi-reservoir systems. This paper is supported by https://imanahmadianfar.com/codes.

     

An E-Business Event Stream Mechanism for Improving User Tracing Processes

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.     

Prenatal exposure to the fluoride containing psychiatric drug fluoxetine and anti-oxidative alterations in the neonatal rat brain

Fluoride is a key ingredient of many psychiatric drugs like fluoxetine (Prozac^®, Fluoxetine^®). Pregnant women frequently use this drug as they suffer from depression and anxiety disorders during this period. Fluoxetine is able to reach the fetus through the placenta and passes to the newborn through milk. In the present study, female Wistar rats were treated with 5, 10, and 20 mg/L fluoxetine (containing 94% fluorides) from pregnancy day 10 to day 20. After delivery, the levels of the enzymatic antioxidants in the brain of their offspring at postnatal day 2 were measured. The results showed that, in all fluoxetine exposed groups compared with the control group, there was a significant decrease (P < 0.01) in the glutathione, catalase, glutathione S-transferases and potassium and a non- significant increase (P > 0.05) in the activity of malondialdehyde and creatine kinase. The results suggest that fluoxetine may be a developmental neurotoxicant due to presence of fluoride hence must be used carefully during pregnancy.     

Pricing mechanism for interconnection between phone operators and virtual mobile VoIP operators

Lately software based virtual mobile voice over Internet Protocol (VMVoIP) services have become available to end-users which may endanger profit of regular telephone operators from phone calls. In this situation, telephone operators should decide on a proper mechanism for interconnection with such VMVoIP operators which secures mutual benefit of both operators. In this paper, we study both monopolistic and oligopolistic markets of VMVoIP in which a single VMVoIP and several VMVoIP operators available in the market respectively. We model the interaction among a phone operator, VMVoIP operators and end-users as a leader-follower dynamic game and find a sub-game perfect Nash equilibrium (SPNE) for the game. The SPNE shows the phone operator’s best pricing strategy (i.e., wholesale price for VMVoIP operators and retail price for end-users) and its best decision on the interconnection with VMVoIP operators. We further study economic interpretation of the SPNE. We show that in specific end-users’ demands, the phone operator may increase its total profit by allowing the interconnection and taking right price discrimination strategy.