A parametric study on the emissions from an HCCI alternative combustion engine resulting from the auto-ignition of primary reference fuels

The homogeneous charge compression ignition is an alternative combustion technology that can reduce automobile pollution, provided that the exhaust emission can be controlled. A parametric study can be useful in order to gain more understanding in the emission reduction possibilities via this new combustion technology. For this purpose, the inlet temperature, the equivalence ratio and the compression ratio are changed, respectively, from 30 to 70 °C, 0.28 to 0.41 and 6 to 14. Also the diluting, thermal and chemical effects of exhaust gas recirculation were studied. The emission of CO, CO₂, O₂ and hydrocarbons has been measured using primary reference fuels. It appears that an increase in the inlet temperature, the EGR temperature, the equivalence ratio and the compression ratio results into a decrease of the emissions of CO and the hydrocarbons of up to 75%. The emission of CO₂ increased, however, by 50%. The chemical parameters showed more complicated effects, resulting into a decrease or increase of the emissions, depending on whether the overall reactivity increased or not. If the reactivity increased, generally, the emissions of CO and hydrocarbons increased, while that of CO₂ increased. The increase of CO₂ emissions could be compensated by altering the compression ratio and the EGR parameters, making it possible to control the emission of the HCCI engine.     

Traffic pattern–based load‐balancing algorithm in software‐defined network using distributed controllers

Programmability and decoupling of the data plane and control plane in software‐defined networking (SDN) make the enterprise's network to focus on this new paradigm and to deploy their applications on it. Furthermore, supporting of distributed controllers in SDN opens the opportunities to address the limitations of centralized controller's architecture, which in turn improves the overall performance of the network. This study proposes a new load‐balancing algorithm to handle the load based on the traffic pattern specifically transmission control protocol (TCP) and user datagram protocol (UDP) traffic. Additionally, this study uses a distributed SDN controller's architecture to host the load balancer application. This study also employs a failover mechanism on the distributed architecture to achieve high‐availability environment and to ensure the redundancy and reliability of the network. The obtained results prove the effectiveness of the proposed algorithm in terms of availability, which is increased by 11%, response time is reduced by 98%, transaction rate is also increased by 258%, throughput is increased by 206%, concurrency is reduced by 63%, and packet loss is reduced by 86% while comparing with random, round‐robin, and weighted round‐robin algorithms in addition to ease the integration and deployment in distributed controllers.     

Biclustering neighborhood-based collaborative filtering method for top-n recommender systems

Knowledge and Information Systems - We propose a novel collaborative filtering method for top- $$n$$ recommendation task using bicustering neighborhood approach. Our method takes advantage of local...     

In-Vitro Catalytic and Antibacterial Potential of Green Synthesized CuO Nanoparticles against Prevalent Multiple Drug Resistant Bovine Mastitogen Staphylococcus aureus

Nanoparticles prepared from bio-reduction agents are of keen interest to researchers around the globe due to their ability to mitigate the harmful effects of chemicals. In this regard, the present study aims to synthesize copper oxide nanoparticles (CuO NPs) by utilizing root extracts of ginger and garlic as reducing agents, followed by the characterization and evaluation of their antimicrobial properties against multiple drug resistant (MDR) S. aureus. In this study, UV-vis spectroscopy revealed a reduced degree of absorption with an increase in the extract amount present in CuO. The maximum absorbance for doped NPs was recorded around 250 nm accompanying redshift. X-ray diffraction analysis revealed the monoclinic crystal phase of the particles. The fabricated NPs exhibited spherical shapes with dense agglomeration when examined with FE-SEM and TEM. The crystallite size measured by using XRD was found to be within a range of 23.38–46.64 nm for ginger-doped CuO and 26–56 nm for garlic-doped CuO. Green synthesized NPs of ginger demonstrated higher bactericidal tendencies against MDR S. aureus. At minimum and maximum concentrations of ginger-doped CuO NPs, substantial inhibition areas for MDR S. aureus were (2.05–3.80 mm) and (3.15–5.65 mm), and they were measured as (1.1–3.55 mm) and (1.25–4.45 mm) for garlic-doped NPs. Conventionally available CuO and crude aqueous extract (CAE) of ginger and garlic roots reduced MB in 12, 21, and 38 min, respectively, in comparison with an efficient (100%) reduction of dye in 1 min and 15 s for ginger and garlic doped CuO NPs.     

Arabic question answering system: a survey

Question answering is a subfield of information retrieval. It is a task of answering a question posted in a natural language. A question answering system (QAS) may be considered a good alternative to search engines that return a set of related documents. The QAS system is composed of three main modules; question analysis, passage retrieval, and answer extraction. Over the years, numerous QASs have been presented for use in different languages. However, the the development of Arabic QASs has been slowed by linguistic challenges and the lack of resources and tools available to researchers. In this survey, we start with the challenges due to the language and how these challenges make the development of new Arabic QAS more difficult. Next, we do a detailed review of several Arabic QASs. This is followed by an in-depth analysis of the techniques and approaches in the three modules of a QAS. We present an overview of important and recent tools that were developed to help the researchers in this field. We also cover the available Arabic and multilingual datasets, and a look at the different measures used to assess QASs. Finally, the survey delves into the future direction of Arabic QAS systems based on the current state-of-the-art techniques developed for question answering in other languages.

     

Fabrication of low cost,green silica based ceramic hollow fibre membrane prepared from waste rice husk for water filtration application

In order to develop low cost ceramic membranes and effectively utilize abundantly and dumped waste agriculture, fabrication of green silica based ceramic hollow fibre membranes from waste rice husk was evaluated. Rice husk was converted into amorphous and crystalline silica based rice husk ash (ARHA and CRHA) by burning process at 600?°C and 1000?°C, respectively. The properties of silica based rice husk ashes were studied by transmission electron microscopy (TEM), x-ray powder diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), BET analysis, thermogravimetry and differential thermal analysis (TG/DTA) and x-ray fluorescence (XRF). Effect of silica content and sintering temperature towards membrane fabrication were investigated and characterized in term of morphological properties, mechanical strength, surface roughness, pore size distribution, porosity and pure water flux (PWF). The ceramic hollow fibre membrane (CHFM) prepared at 37.5?wt% CRHA content and sintered at 1200?°C achieved a good mechanical strength (71.2?MPa) and excellent porosity (50.2%). As a result, high PWF with value ~ 300?L/m² h and stable at 20?min was obtained. Due to the excellent pure water flux, the prepared ceramic membrane from waste rice husk hold promise for water treatment application.     

Optimal experimental design for optimal process design: A trilevel optimization formulation

Typical optimal experimental design (OED) methods aim at minimizing the covariance matrix of the estimated parameters regardless of the intended application of the model that is being estimated. This can unnecessarily increase the experimental costs. Herein, we propose a new OED method, which tailors the designed experiments to the model application. The method is demonstrated for model-based process design and aims at mitigating a worst-case realization of the process design. The proposed formulation results in a min–max–min problem and is based on bounded-error OED. The method is illustrated via an ad hoc solution method using two examples, a simple illustrative example and the van de Vusse reaction, that show the differences between typical and the new tailored OED method: experimental designs can be considered good using the latter method, while the same design would be considered bad with the former methods.     

A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses data mining to advance agricultural developments. Many farmers aren’t getting the most out of their land because they don’t use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two from Case-1 (dataset with many numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction.     

Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer

This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approximating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model's performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).