Microwave assisted economic multi walled carbon nanotubes (MWCNTs) were synthesized with 10–40 nm diameter and 9.1 m2/g surface area. CNTs were used to remove arsenite and arsenate in water with ...60 min contact time, 40.0 μg/L concentration, 6.0 pH, 2.0 g/L dose and 25 °C. The percentage removal of arsenate and arsenite were 92.0 and 91.0. The sorption data obeyed Temkin, Dubinin-Radushkevich, Freundlich, and Langmuir models. The free energy values for arsenate and arsenite were −15.68 and −15.75 and −13.80 and −13.85 and −13.66 and −13.73 kJ/mol at 20, 25 and 30 °C. The values of enthalpy and entropy for arsenate and arsenite were −16.11 and −16.15 and −36.33 × 10−3 and −36.53 × 10−3 kJ/mol. The negative value of ΔG° showed spontaneous adsorption on CNTs. The sorption was exothermic and the kinetics was controlled by pseudo-first order. Arsenic species removal was occurred via liquid film diffusion mechanism. The column data followed Bohart and Adams and Thomas models. The removal amounts of arsenite and arsenate species in column operations were 13.5 and 14.0 μg/g. The reported method was low priced, quick and reproducible. The reported method is appropriate for the uptake of arsenic in ordinary water sources owing to its capability to work in normal water situations.
•Economic synthesis of MWCNTs through CVD method•MWCNTs have 40–45% larger yield and 2 times larger surface area.•Economic arsenate and arsenite removal in water by batch and column operation•Method is capable to work at natural water conditions, economic, efficient and fast.•As(III) and As(V) may be removed economically from any water resource.
Water is the most important and essential component of earth's ecosystem, playing a vital role in the proper functioning of flora and fauna. But water resources are contaminating continuously. The ...whole world may be in great water scarcity in the coming few decades. Among many water treatment methods, adsorption is considered to be one of the best. Many articles describe adsorption applications for water treatment in batch mode. Only few works report laboratory, pilot and commercial applications of adsorption technology. This review is a critical evaluation of the contribution of adsorption technology at laboratory, pilot and process scales. Water treatment, through column operations, has been divided into two parts: removal of (i) inorganic and (ii) organic pollutants. Attempts have been made to evaluate the application of adsorption columns at the ground level. Besides, efforts have also been made to emphasize the importance of adsorption columns at commercial levels to tackle water treatment problems in the future.
There is plenty of research on personality traits that explains its impact on human behaviors in different situations. However, there is sparse research available in the literature that explains how ...does personality traits affect innovativeness among individuals and satisfaction with life perceptions (subjective wellbeing). The current study proposes and empirically examines a conceptual model that addresses this important gap in the body of knowledge. Famous Big-Five personality traits theory is used to explain this phenomenon in this research. Data is collected from 613 students enrolled in different executive, master and PhD level programs in different universities of Pakistan. The study found positive influence of extraversion, agreeableness, conscientiousness, and openness to experience on individual innovativeness and satisfaction with life perceptions. Neuroticism is found to be negatively related to individual innovativeness and satisfaction with life perceptions. Finally, the study noted a positive association between individual innovativeness and perception with life. The applications and implications of this research are discussed in details.
Due to overgrowth in population, industrialization and civilization, demands for water are increasing geometrically. Therefore, alternative sources of water are required and wastewater treatment and ...recycling may serve this purpose. Among various water treatment technologies, adsorption onto activated carbon is in the front line due to its universal nature. Activated carbon is the best adsorbent able to capture inorganic, as well as organic, pollutants that contaminate water resources. Inorganic pollutants, especially metal ions, are more dangerous due to their toxic and possibly carcinogenic natures. Also they are most often persistent and difficult to biodegrade. The present article describes the quest to find an economically viable substitute to active carbon adsorbent to remove toxic metal ions. A brief discussion of design of batch and column adsorption experiments, development of inexpensive adsorbents, and experimental conditions of metal ions removal by batch and column procedures is included. Efforts have also been made to differentiate adsorption versus speciation of metal ions.
Conventional cellular systems are designed to ensure ubiquitous coverage with an always present wireless channel irrespective of the spatial and temporal demand of service. This approach raises ...several problems due to the tight coupling between network and data access points, as well as the paradigm shift towards data-oriented services, heterogeneous deployments and network densification. A logical separation between control and data planes is seen as a promising solution that could overcome these issues, by providing data services under the umbrella of a coverage layer. This article presents a holistic survey of existing literature on the control-data separation architecture (CDSA) for cellular radio access networks. As a starting point, we discuss the fundamentals, concepts, and general structure of the CDSA. Then, we point out limitations of the conventional architecture in futuristic deployment scenarios. In addition, we present and critically discuss the work that has been done to investigate potential benefits of the CDSA, as well as its technical challenges and enabling technologies. Finally, an overview of standardisation proposals related to this research vision is provided.
Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. State-of-the-art literature ...applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, in this article we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that preprocesses raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of Things environment.
In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, ...quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner.
Soaring capacity and coverage demands dictate that future cellular networks need to migrate soon toward ultra-dense networks. However, network densification comes with a host of challenges that ...include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads, and higher backhaul costs. Interestingly, most of the problems that beleaguer network densification stem from legacy networks' one common feature, i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of the aforementioned challenges. In this survey, we review various proposals that have been presented in the literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification, namely: energy efficiency, system level capacity maximization, interference management, and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP) and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up-to-date survey on SARC, CoMP, and D2D. Most importantly, this survey provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.
Mobile data traffic grew by 74% in 2015 and it is expected to grow eight-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. ...Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating inter-cell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity, and additional backhaul limitation. Hence, small CoMP clusters will need to be formed in the network. This paper surveys the state-of-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organization as an important concept for effective CoMP clustering to maximize CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organization and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilization of big data in cellular context to support proactive CoMP clustering based on prediction modeling. Finally, we conclude this paper with a summary of lessons learned in this field. This paper aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.
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