Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score.     

Hybrid intelligent techniques for MRI brain images classification

This paper presents a hybrid technique for the classification of the magnetic resonance images (MRI). The proposed hybrid technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier based on feed forward back-propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The classifiers have been used to classify subjects as normal or abnormal MRI human images. A classification with a success of 97% and 98% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.     

Design of dual‐band microstrip patch antenna with right‐angle triangular aperture slot for energy transfer application

This work focusing on the dual‐band antenna design with rectifying circuit for energy transfer system technology for enhancement gain performance. The air gap technique is applied on this microstrip antenna design work to enhance the antenna gain. The work begins with designing and analyzing the antenna via the CST Microwave Studio software. After validation on acceptable performance in simulation side is obtained, the return loss, S₁₁ of the antenna is measured using vector network analyzer equipment. The rectifier circuit is used to convert the captured signal to DC voltage. This projected dual‐band antenna has successfully accomplished the target on return loss of ?44.707 dB and ?32.163 dB at dual resonant frequencies for 1.8 GHz and 2.4 GHz, respectively. This proposed antenna design benefits in low cost fabrication and has achieved high gain of 6.31 dBi and 7.82 dBi for dual‐band functioning frequencies.     

Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.     

A study of A2/O process in El-Berka WWTP by replacing the anaerobic selector with UASB and adopting a hybrid system in the oxic zone

ABSTRACT

A multistage system comprising an upflow anaerobic sludge blanket (UASB) followed by anoxic unit and then oxic activated sludge (AS) with biofilm is studied in El-Berka WWTP, Egypt. Different organic loading wastewaters of chemical oxygen demand (COD) less than 500 mg/L till 3000 mg/L are tested during the study. The hydraulic retention time (HRT) varies for each loading from 7.5 to 10 to 15 h. The UASB reactor accomplishes the removal efficiency of 50%–70% of influent COD. The overall system performs the removal efficiency of 95% of influent COD and NH₄-N. Also, the results are verified by a modified mathematical model.     

An integrated system for hydrogen and methane production during landfill leachate treatment

The patent-pending integrated waste-to-energy system comprises both a novel biohydrogen reactor with a gravity settler (Biohydrogenator), followed by a second stage conventional anaerobic digester for the production of methane gas. This chemical-free process has been tested with a synthetic wastewater/leachate solution, and was operated at 37 °C for 45 d. The biohydrogenator (system (A), stage 1) steadily produced hydrogen with no methane during the experimental period. The maximum hydrogen yield was 400 mL H₂/g glucose with an average of 345 mL H₂/g glucose, as compared to 141 and 118 mL H₂/g glucose for two consecutive runs done in parallel using a conventional continuously stirred tank reactor (CSTR, System (B)). Decoupling of the solids retention time (SRT) from the hydraulic retention time (HRT) using the gravity settler showed a marked improvement in performance, with the maximum and average hydrogen production rates in system (A) of 22 and 19 L H₂/d, as compared with 2–7 L H₂/d in the CSTR resulting in a maximum yield of 2.8 mol H₂/mol glucose much higher than the 1.1–1.3 mol H₂/mol glucose observed in the CSTR. Furthermore, while the CSTR collapsed in 10–15 d due to biomass washout, the biohydrogenator continued stable operation for the 45 d reported here and beyond. The methane yield for the second stage in system (A) approached a maximum value of 426 mL CH₄/gCOD removed, while an overall chemical oxygen demand (COD) removal efficiency of 94% was achieved in system (A).     

Effect of organic loading on a novel hydrogen bioreactor

This study investigated the impact of six organic loading rates (OLR) ranging from 6.5 gCOD/L-d to 206 gCOD/L-d on the performance of a novel integrated biohydrogen reactor clarifier systems (IBRCSs) comprised a continuously stirred reactor (CSTR) for biological hydrogen production, followed by an uncovered gravity settler for decoupling of solids retention time (SRT) from hydraulic retention time (HRT). The system was able to maintain a high molar hydrogen yield of 2.8 mol H₂/mol glucose at OLR ranging from 6.5 to 103 gCOD/L-d, but dropped precipitously to approximately 1.2 and 1.1 mol H₂/mol glucose for the OLRs of 154 and 206 gCOD/L-d, respectively. The optimum OLR at HRT of 8 h for maximizing both hydrogen molar yield and volumetric hydrogen production was 103 gCOD/L-d. A positive statistical correlation was observed between the molar hydrogen production and the molar acetate-to-butyrate ratio. Biomass yield correlated negatively with hydrogen yield, although not linearly. Analyzing the food-to-microorganisms (F/M) data in this study and others revealed that, both molar hydrogen yields and biomass specific hydrogen rates peaked at 2.8 mol H₂/mol glucose and 2.3 L/gVSS-d at F/M ratios ranging from 4.4 to 6.4 gCOD/gVSS-d. Microbial community analysis for OLRs of 6.5 and 25.7 gCOD/L-d showed the predominance of hydrogen producers such as Clostridium acetobutyricum, Klebsiella pneumonia, Clostridium butyricum, Clostridium pasteurianum. While at extremely high OLRs of 154 and 206 gCOD/L-d, a microbial shift was clearly evident due to the coexistence of the non-hydrogen producers such as Lactococcus sp. and Pseudomonas sp.     

Use of low CaO unprocessed steel slag in concrete as fine aggregate

Steel slag, which is produced locally in great amounts, has a negative impact on the environment when disposed. Local steel slag has a low CaO content and has no pozzolanic activity.In this research, local unprocessed steel slag is introduced in concrete mixes. Various mixes with compressive strength ranging from 25 to 45 MPa are studied. The slag is used as fine aggregate replacing the sand in the mixes, partly or totally. Ratios of 0%, 15%, 30%, 50% and 100% are used.Depending on the grade of concrete, the compressive strength is improved when steel slag is used for low sand replacement ratios (up to 30%).When optimum values are used, the 28-day tensile strength of concrete is improved by 1.4–2.4 times and the compressive strength is improved by 1.1–1.3 times depending on the replacement ratio and the grade of concrete. The best results are obtained for replacement ratios of 30–50% for tensile strength and 15–30% for compressive strength.Therefore, the use of steel slag in concrete would enhance the strength of concrete, especially tensile strength, provided the correct ratio is used.