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In the evolving landscape of portfolio management (PM), the fusion of advanced machine learning techniques with traditional financial methodologies has opened new avenues for innovation. Our study introduces a cutting-edge model combining deep reinforcement learning (DRL) with a non-stationary transformer architecture. This model is designed to decode complex patterns in financial time-series data, enhancing portfolio management strategies with deeper insights and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing the model's ability to navigate the complexities of asset management. Rigorous testing demonstrates the model's efficacy, highlighting the benefits of blending diverse data sources and sophisticated algorithmic approaches in mastering the nuances of PM. [ABSTRACT FROM AUTHOR]

Cross-axis sensitivity is generally undesirable, and lower values are required for the accurate performance of a thermal accelerometer. In this study, errors in devices are utilized to simultaneously measure two physical quantities of an unmanned aerial vehicle (UAV) in the X-, Y-, and Z-directions, i.e., where three accelerations and three rotations can also be simultaneously measured using a single motion sensor. The 3D structures of thermal accelerometers were designed and simulated in a FEM simulator using commercially available FLUENT 18.2 software Obtained temperature responses were correlated with input physical quantities, and a graphical relationship was created between peak temperature values and input accelerations and rotations. Using this graphical representation, any values of acceleration from 1g to 4g and rotational speed from 200 to 1000°/s can be simultaneously measured in all three directions. [ABSTRACT FROM AUTHOR]

The advent of the Internet and portable devices, including smartphones and watches, has brought unprecedented opportunities for embedded application systems developments. Along with these developments, there is an increasing need for embedded devices to handle important services, such as the ability to pay bills or manage bank accounts remotely via mobile phones. Such applications and developments have also highlighted the issues of cyberattacks and computing network security--these developments have made mobile phones a potential target for malware, trojans, and viruses, so it is critical to design a set of security technologies for embedded devices. In fact, security has become an essential requirement in the process of embedded system design. Thus, ARM has proposed system-level security solutions based on TrustZone technology. TrustZone technology is tightly integrated with Cortex™-A processors and extends the system through the AMBA® AXI bus and specific TrustZone system IP blocks to protect peripherals such as secure memory, encryption blocks, keyboards, and screens from software attacks. It divides the system into TEE (Trusted Execution Environment) and REE (Rich Execution Environment) by hardware and provides intrinsic software security services and interfaces. More precisely, it has built system security by combining hardware and software. It is worth noting that it does not influence performance, power consumption, and area as much as possible. Owing to such characteristics, the technology has gained the wide attention of researchers worldwide. There is lack of systematic documentation of the technology. Therefore, this paper documents the significant progress achieved in the field. In particular, this article mainly analyses the primary mechanism implementation, and how to build the Trusted Execution Environment in different environments. Then, this paper discusses the related research works in the academic field and business applications of the technology. Furthermore, the advantages and weaknesses of the TrustZone technology as well as the proposed possible solutions aiming at the deficiency are outlined. Finally, a comparison of TrustZone technology with another mainstream commercial SGX, and future directions are presented. [ABSTRACT FROM AUTHOR]

The expensive cost and small capacity of cache has made prefetching a major method to close the performance gap between main memory and CPU. Prefetching techniques have increasingly shown their importance as a mean to improve cache hit rates. And Metadata is a kind of significant data, especially in the information age, where massive internet productions based on it are created and used in daily life. In this paper, we first narrate the development of prefetching technologies existed to provide a general impression. And two types of prefetching schemes or algorithms, metadata prefetching and adaptive prefetching are introduced from different perspectives. We then compare and analyze their advantages and present some of their limitations. Then, the further discussion shows how to use the technologies depending on different factors in the real situation, which may be the size of data. Besides, the self-learning, as a popular theory is emphasized here for the choice to be combined together with prefetching technology. [ABSTRACT FROM AUTHOR]

Logisim is a digital logic circuit design and simulation software that is an open source, free of charge, secondary development, installation-free, easy to use and intuitive. This paper examines how Logisim can be used to design the data path and combine it with Verilog for FPGA design of single cycle CPUs. [ABSTRACT FROM AUTHOR]

Cloud computing is a computing paradigm that provides various services and computing resources to users through Cloud Service Providers over the Internet. However, storing data in an unsecure cloud may lead to security issues such as privacy issues and data leakage. Therefore, it is necessary for encryption schemes to be implemented in clouds to provide a secure environment for the users. One of the cryptographic schemes is the Attribute Based Encryption (ABE), which provides privacy and access control in cloud and can be implemented in a Trusted Real-Time Execution Environment to achieve stronger security. This paper first outlines the various existing encryption techniques, which can be categorized into symmetric and asymmetric algorithms. It then comprehensively explore the key policy and the access policy attribute based encryption. Furthermore, this paper examines the various schemes of ABE and compares the schemes based on its access structure, advantages and disadvantages. Lastly, this paper discusses the applications of key policy and ciphertext policy ABE. [ABSTRACT FROM AUTHOR]

When conducting a cloud migration in a Trusted Real-Time Execution Environment, it is always important to conduct the migration by following certain standard and constraint while modifying it according to the needs of the migration. Migration framework is introduced to provide such a standard and steps to be followed for migration. However, current migration frameworks no longer essentially satisfy the cloud migration efficiently. Resources are increasing and the usage of current migration frameworks are not proficiently supporting the demands. Thus, researchers are developing more automation cloud migration frameworks that help in reducing the cost, time, manpower and increasing efficiency to conduct cloud migration. This paper concisely addresses cloud migration, stages of conducting cloud migration and introduces various automated cloud migration frameworks along with detailed analysis. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) is the major element of the intelligent transportation system (ITS). The purpose of ITS is to increase road safety and manage the movement of vehicles. ITS is known as one of the main components of smart cities. As a result, there are critical challenges such as routing in these networks. Recently, many scholars have worked on this challenge in VANET. They have used machine learning techniques to learn the routing proceeding in the networks adaptively and independently. In this paper, a Q-learning and fuzzy logic-based hierarchical routing protocol (QFHR) is proposed for VANETs. This hierarchical routing technique consists of three main phases: identifying traffic conditions, routing algorithm at the intersection level, and routing algorithm at the road level. In the first phase, each roadside unit (RSU) stores a traffic table, which includes information about the traffic conditions related to four road sections connected to the corresponding intersection. Then, RSUs use a Q-learning-based routing method to discover the best path between different intersections. Finally, vehicles in each road section use a fuzzy logic-based routing technique to choose the foremost relay node. The simulation of QFHR has been executed on the network simulator version 2 (NS2), and its results have been presented in comparison with IRQ, IV2XQ, QGrid, and GPSR in two scenarios. The first scenario analyzes the result based on the packet sending rate (PSR). In this scenario, QFHR gets better the packet delivery rate by 2.74%, 6.67%, 22.35%, and 29.98% and decreases delay by 16.19%, 22.82%, 34.15%, and 59.51%, and lowers the number of hops by 6.74%, 20.09%, 2.68%, and 12.22% compared to IRQ, IV2XQ, QGrid, and GPSR, respectively. However, it increases the overhead by approximately 9.36% and 11.34% compared to IRQ and IV2XQ, respectively. Moreover, the second scenario evaluates the results with regard to the signal transmission radius (STR). In this scenario, QFHR increases PDR by 3.45%, 8%, 23.29%, and 26.17% and decreases delay by 19.86%, 34.26%, 44.09%, and 68.39% and reduces the number of hops by 14.13%, 32.58%, 7.71%, and 21.39% compared to IRQ, IV2XQ, QGrid, and GPSR, respectively. However, it has higher overhead than IRQ (11.26%) and IV2XQ (25%). [ABSTRACT FROM AUTHOR]

Processing-in-memory (PIM) is a promising architecture to design various types of neural network accelerators as it ensures the efficiency of computation together with Resistive Random Access Memory (ReRAM). ReRAM has now become a promising solution to enhance computing efficiency due to its crossbar structure. In this paper, a ReRAM-based PIM neural network accelerator is addressed, and different kinds of methods and designs of various schemes are discussed. Various models and architectures implemented for a neural network accelerator are determined for research trends. Further, the limitations or challenges of ReRAM in a neural network are also addressed in this review. [ABSTRACT FROM AUTHOR]

Hadoop Distributed File System (HDFS) is based on Google File System (GFS), a big data distributed file management system included in Hadoop. Nowadays, many HDFS and many other similar frameworks have the need to store small files in the system. In this aspect, HDFS affects its performance and Namenode memory management when dealing with a large number of small files. Therefore, researchers have proposed various solutions to address the shortcomings of HDFS for storing small and medium-sized files. This paper presents three HDFS schemes for merging small files and analyzes the importance of correlation and prefetching after merging small files. The efficiency of reading small files can be improved by correlated file prefetching. Finally, the small file storage architecture is obtained to stand superior to the NHAR architecture. [ABSTRACT FROM AUTHOR]

In this study, a new technique has been proposed by numerical simulations by which multiple physical quantities can be simultaneously measured. The sensor is a modification of existing physical sensors such as a thermal motion sensor. Simultaneous measurement of acceleration and rotation is presented herein. Cross-axis sensitivity is employed such that output sensitivities observed at two perpendicular axes, X and Y sensor data, are related to the input physical quantities. The physics involved in measurement is similar to that of a conventional thermal accelerometer, hence the governing equations predicting the sensor response are based on the conservation of mass, momentum, and energy, and are discretized by using a commercially available software FLUENT. A series of computational studies are conducted and using these studies a novel idea is proposed in which the maximum temperature values are obtained at various positions around a heating source and are correlated with the applied acceleration and rotational speed. A parametric study is also presented to find the optimum distance between the heater and sensors. The influence of changing gas medium on the temperature curves has also been examined and it has been concluded that CO2 generates the maximum performance due to its higher density and lower viscosity. [ABSTRACT FROM AUTHOR]

With the swift development of deep learning applications, the convolutional neural network (CNN) has brought a tremendous challenge to traditional processors to fulfil computing requirements. It is urgent to embrace new strategies to improve efficiency and diminish energy consumption. Currently, diverse accelerator strategies for CNN computation based on the field-programmable gate array (FPGA) platform have been gradually explored because they have edges of high parallelism, low power consumption, and better programmability. This paper first illustrates state-of-the-art FPGA-based accelerator design by emphasizing the contributions and limitations of existing research works. Subsequently, we demonstrated significant concepts of parallel computing (PC) in the convolution algorithm and discussed how to accomplish parallelism based on the FPGA hardware structure. Eventually, with the proposed CPU+ FPGA framework, we performed experiments and compared the performance against traditional computation strategies in terms of the operation efficiency and energy consumption ratio. The results revealed that the efficiency of the FPGA platform is much higher than that of the central processing unit and graphics processing unit. [ABSTRACT FROM AUTHOR]

Simulation-based optimization design is becoming increasingly important in engineering. However, carrying out multi-point, multi-variable, and multi-objective optimization work is faced with the "Curse of Dimensionality", which is highly time-consuming and often limited by computational burdens as in aerodynamic optimization problems. In this paper, an active subspace dimensionality reduction method and the adaptive surrogate model were proposed to reduce such computational costs while keeping a high precision. In this method, the active subspace dimensionality reduction technique, three-layer radial basis neural network approach, and polynomial fitting process were presented. For the model evaluation, a NASA standard test function problem and RAE2822 airfoil drag reduction optimization were investigated in the experimental design problem. The efficacy of the method was proved by both the experimental examples in which the adaptive surrogate model in a dominant one-dimensional active subspace is given and the optimization efficiency was improved by two orders. Furthermore, the results show that the constructed surrogate model reduced dimensionality and alleviated the complexity of conventional multivariate surrogate modeling with high precision. [ABSTRACT FROM AUTHOR]

Nowadays the storing of sensitive information in cyberspace shows a significant increase and this led to an escalation of cyberattacks. Hence, the protection against the confidentiality of data or programs has been a matter of special concern to many security experts. One of the problematic cyberattacks is the cache side-channel attacks as caches are playing an important rule to ensure the performance of computer. The leakage of information can occur by observing the distinct access time for a cache miss or hit. This paper conducts some background research about the categorization of different cache side-channel attacks. Besides that, several strategies which are used to defend against cache side-channel attacks such as detection method, prevention method and some proposed secure cache design will be reviewed. Also, the difficulties about the defenses in cache side-channel attacks and some recommended strategies will also be discussed in the following content. [ABSTRACT FROM AUTHOR]

Deep Neural Networks have gained popularity due to their superiority in performance in various applications. Hence, domain-specific architectures have been introduced to speed up the model training process. Since the launch of TPUs by Google, there were few literatures that examines the performance of TPUs compared to existing processing units such as the CPU and GPU in deep learning applications. The recent inclusion of the PyTorch framework to use Google Colaboratory's resources also provided an opportunity for us to build a PyTorch-based Colab notebook that can be used for further study in this area. In this study, we evaluated the performance of the Intel Xeon CPU, Nvidia T4 GPU and TPU v2-8 by using the Convolutional Neural Network (CNN) model on the CIFAR-10 and MNIST datasets. Our study also considers the accuracy and convergence of the model while testing, which were implemented by data augmentation and batch normalization techniques in the VGG16 model trained from scratch. We achieved a model accuracy of 86% and 96% for the CIFAR-10 and MNIST datasets respectively. During the experiments, we observed that the GPU performed 25x faster than the CPU; meanwhile the TPU only achieved a 12x speedup compared to the CPU as there were some issues faced. We proposed some issues and possible solutions in this paper for further study. [ABSTRACT FROM AUTHOR]

Sky computing, the cloud of clouds, has become an emergent trend in cloud computing domain. It allows the creation of large-scale infrastructure by utilizing resources from multiple cloud providers. It enables these massive infrastructures to run parallel computation with high performance. Being a multi-cloud computing model, sky computing addresses several limitations of traditional cloud computing especially the vendor lock-in problem. Sky computing allows the aggregation of cloud resources among multiple cloud providers so that cloud consumers can dynamically provision the required resource on demand. This paper reviews state-of-the-art and explains their significance in the development of a sky computing environment. [ABSTRACT FROM AUTHOR]

Many consider the KDD Cup 99 data sets to be outdated and inadequate. Therefore, the extensive use of these data sets in recent studies to evaluate network intrusion detection systems is a matter of concern. We contribute to the literature by addressing these concerns. [ABSTRACT FROM AUTHOR]

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms. [ABSTRACT FROM AUTHOR]

Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system's performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i) performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii) employs logistic regression and extreme gradient boosting techniques for classification; (iii) introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv) uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people's daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved. [ABSTRACT FROM AUTHOR]

In deploying the Internet of Things (IoT) and Internet of Medical Things (IoMT)-based applications and infrastructures, the researchers faced many sensors and their output's values, which have transferred between service requesters and servers. Some case studies addressed the different methods and technologies, including machine learning algorithms, deep learning accelerators, Processing-In-Memory (PIM), and neuromorphic computing (NC) approaches to support the data processing complexity and communication between IoMT nodes. With inspiring human brain structure, some researchers tackled the challenges of rising IoT- and IoMT-based applications and neural structures' simulation. A defective device has destructive effects on the performance and cost of the applications, and their detection is challenging for a communication infrastructure with many devices. We inspired astrocyte cells to map the flow (AFM) of the Internet of Medical Things onto mesh network processing elements (PEs), and detect the defective devices based on a phagocytosis model. This study focuses on an astrocyte's cholesterol distribution into neurons and presents an algorithm that utilizes its pattern to distribute IoMT's dataflow and detect the defective devices. We researched Alzheimer's symptoms to understand astrocyte and phagocytosis functions against the disease and employ the vaccination COVID-19 dataset to define a set of task graphs. The study improves total runtime and energy by approximately 60.85% and 52.38% after implementing AFM, compared with before astrocyte-flow mapping, which helps IoMT's infrastructure developers to provide healthcare services to the requesters with minimal cost and high accuracy. [ABSTRACT FROM AUTHOR]

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. [ABSTRACT FROM AUTHOR]

Deepfake is one of the applications that is deemed harmful. Deepfakes are a sort of image or video manipulation in which a person's image is changed or swapped with that of another person's face using artificial neural networks. Deepfake manipulations may be done with a variety of techniques and applications. A quintessential countermeasure against deepfake or face manipulation is deepfake detection method. Most of the existing detection methods perform well under symmetric data distributions, but are still not robust to asymmetric datasets variations and novel deepfake/manipulation types. In this paper, for the identification of fake faces in videos, a new multistream deep learning algorithm is developed, where three streams are merged at the feature level using the fusion layer. After the fusion layer, the fully connected, Softmax, and classification layers are used to classify the data. The pre-trained VGG16 model is adopted for transferred CNN1stream. In transfer learning, the weights of the pre-trained CNN model are further used for training the new classification problem. In the second stream (transferred CNN2), the pre-trained VGG19 model is used. Whereas, in the third stream, the pre-trained ResNet18 model is considered. In this paper, a new large-scale dataset (i.e., World Politicians Deepfake Dataset (WPDD)) is introduced to improve deepfake detection systems. The dataset was created by downloading videos of 20 different politicians from YouTube. Over 320,000 frames were retrieved after dividing the downloaded movie into little sections and extracting the frames. Finally, various manipulations were performed to these frames, resulting in seven separate manipulation classes for men and women. In the experiments, three fake face detection scenarios are investigated. First, fake and real face discrimination is studied. Second, seven face manipulations are performed, including age, beard, face swap, glasses, hair color, hairstyle, smiling, and genuine face discrimination. Third, performance of deepfake detection system under novel type of face manipulation is analyzed. The proposed strategy outperforms the prior existing methods. The calculated performance metrics are over 99%. [ABSTRACT FROM AUTHOR]

Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast. [ABSTRACT FROM AUTHOR]

To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. The proposed Internet of Things framework incorporates concepts and mechanisms from several domains of computer science, such as computer vision, artificial intelligence, sensor technology, and communication technology. The information captured by camera, such as road edges, traffic lights, and zebra lines, is highlighted using computer vision. The semantics of highlighted information is recognized by artificial intelligence. Sensors provide information on the direction and distance of obstacles, as well as their speed and moving direction. The communication technology is applied to share the information among the vehicles. Since vehicles have high probability to encounter accidents in congested locations, the proposed system enables vehicles to perform self-positioning with other vehicles in a certain range to reinforce their safety and stability. The empirical evaluation shows the viability and efficacy of the proposed system in such situations. Moreover, the collision time is decreased considerably compared with that when using traditional systems. [ABSTRACT FROM AUTHOR]

The presence of diesel in the water could reduce the growth of plant and thus phytoremediation efficacy. The toxicity of diesel to plant is commonly explained; because of hydrocarbons in diesel accumulate in various parts of plants, where they disrupt the plant cell especially, the epidemis, leaves, stem and roots of the plant. This study investigated the effect of bacterial augmentation in floating treatment wetlands (FTWs) on remediation of diesel oil contaminated water. A helophytic plant, Phragmites australis (P. australis), was vegetated on a floating mat to establish FTWs for the remediation of diesel (1%, w/v) contaminated water. The FTWs was inoculated with three bacterial strains (Acinetobacter sp. BRRH61, Bacillus megaterium RGR14 and Acinetobacter iwoffii AKR1), possessing hydrocarbon degradation and plant growth-enhancing capabilities. It was observed that the FTWs efficiently removed hydrocarbons from water, and bacterial inoculation further enhanced its hydrocarbons degradation efficacy. Diesel contaminated water samples collected after fifteen days of time interval for three months and were analyzed for pollution parameters. The maximum reduction in hydrocarbons (95.8%), chemical oxygen demand (98.6%), biochemical oxygen demand (97.7%), total organic carbon (95.2%), phenol (98.9%) and toxicity was examined when both plant and bacteria were employed in combination. Likewise, an increase in plant growth was seen in the presence of bacteria. The inoculated bacteria showed persistence in the water, root and shoot of P. australis. The study concluded that the augmentation of hydrocarbons degrading bacteria in FTWs is a better option for treatment of diesel polluted water. Image 1 • Plant–hydrocarbons degrading bacteria partnerships is an emerging hydrocarbon remediation approach. • Plant associated microcosms can enhance hydrocarbon degradation. • Phragmites australis stimulates hydrocarbons degrading bacteria to degrade hydrocarbons in water. • Efficient plant-bacteria synergy can reduce phytotoxicity and evapotranspiration of hydrocarbons. [ABSTRACT FROM AUTHOR]