Performance evaluation of vehicles emissions prediction models

Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide (NO₂) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for NO₂ to 72% in the case of CO, which suggests that the NO₂ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect.     

Droid-IoT: Detect Android IoT Malicious Applications Using ML and Blockchain

One of the most rapidly growing areas in the last few years is the Internet of Things (IoT), which has been used in widespread fields such as healthcare, smart homes, and industries. Android is one of the most popular operating systems (OS) used by IoT devices for communication and data exchange. Android OS captured more than 70 percent of the market share in 2021. Because of the popularity of the Android OS, it has been targeted by cybercriminals who have introduced a number of issues, such as stealing private information. As reported by one of the recent studies Android malware are developed almost every 10 s. Therefore, due to this huge exploitation an accurate and secure detection system is needed to secure the communication and data exchange in Android IoT devices. This paper introduces Droid-IoT, a collaborative framework to detect Android IoT malicious applications by using the blockchain technology. Droid-IoT consists of four main engines: (i) collaborative reporting engine, (ii) static analysis engine, (iii) detection engine, and (iv) blockchain engine. Each engine contributes to the detection and minimization of the risk of malicious applications and the reporting of any malicious activities. All features are extracted automatically from the inspected applications to be classified by the machine learning model and store the results into the blockchain. The performance of Droid-IoT was evaluated by analyzing more than 6000 Android applications and comparing the detection rate of Droid-IoT with the state-of-the-art tools. Droid-IoT achieved a detection rate of 97.74% with a low false positive rate by using an extreme gradient boosting (XGBoost) classifier.     

Investigating the performance of ammonium sulphate draw solution in fertilizer drawn forward osmosis process

This work investigated the performance of ammonium sulphate as a draw solution in a fertilizer drawn forward osmosis (FDFO) desalination process using thin-film composite (TFC) membrane. Performance has been assessed by the water flux, reverse permeation and the forward rejection of the feed solutes. A logarithmic relation has been determined between flux and ammonium sulphate concentration. As water flux increased, the specific reverse solute flux (SRSF) of NH₄ ⁺ and SO₄ ^2? ions dropped, which is a favourable condition. Reverse permeation values obtained at flux less than 10 L/m²/h are significantly higher than that for flux more than 10 L/m²/h. Thus, it is recommended to operate the process at a flux higher than 10 L/m²/h to avoid loss of draw solute by reverse permeation. SRSF was almost constant irrespective of ammonium sulphate concentration. TFC membrane used in this study exhibited high rejection of feed solution ions for almost all draw solution concentrations except when operated at low ammonium sulphate concentration and high feed solution concentration. In conclusion, ammonium sulphate is an efficient draw solution for FDFO process when run at water flux more than 10 L/m²/h exhibiting high osmotic pressure, low reverse solute permeation and high rejection of feed solute.     

Characterizing the Socio-Economic Driving Forces of Groundwater Abstraction with Artificial Neural Networks and Multivariate Techniques

Integrated groundwater quantity management in the Middle East region must consider appropriate control measures of the socio economic needs. Hence, there is a need for a better knowledge and understanding of the socio economic variables influencing the groundwater quantity. Gaza Strip was chosen as the study area and real data were collected from twenty five municipalities for the reference year 2001. In this paper, the effective variables have been characterized and prioritized using multi-criteria analysis with artificial neural networks (ANN) and expert opinion and judgment. The selected variables were classified and organized using the multivariate techniques of cluster analysis, factor analysis, principal components and classification analysis. There are significant discrepancies between the results of ANN analysis and expert opinion and judgment in terms of ranking and prioritizing the socio-economic variables. Characterization of the priority effective socio-economic driving forces indicates that water managers and planners can introduce demand-based groundwater management in place of the existing supply-based groundwater management. This ensures the success of undertaking responsive technical, managerial and regulatory measures. Income per capita has the highest priority. Efficiency of revenue collection is not a significant socio-economic factor. The models strengthen the integration of preventive approach into groundwater quantity management. In addition to that, they assist decision makers to better assess the socio economic needs and undertake proactive measures to protect the coastal aquifer.     

RETRACTED ARTICLE: Dense stereovision using mono-CCD color cameras

Most stereo algorithms are based only on an analysis of the luminance information. However, with advances in camera technology, in addition to the fact that color information can robustly improve matching, color stereovision is receiving more and more attention. Color stereovision setups are usually based on single-sensor cameras which provide color filter array (CFA) images. In these images, a single color component is sampled at each pixel rather than the three required components red, green, and blue (RGB). We show that standard demosaicing techniques, which are used to interpolate the missing components, are not well adapted when the resulting color pixels are matched in order to estimate image disparities. In order to avoid this problem while exploiting color information, we propose a new matching system designed for dense stereovision based on pairs of CFA images.