•The MnOx particles assembled with nanosheets were uniformly coated on PET fibers.•The growth process of MnOx layer on PET is clearly clarified.•MnOx/PET showed good activity for HCHO decomposition ...at room temperature.•MnOx/PET material is promising for indoor air purification due to its light, flexible and low air-resistant properties.
Removal of low-level formaldehyde (HCHO) is of great interest for indoor air quality improvement. Supported materials especially those with low air pressure drop are of necessity for air purification. Manganese oxides (MnOx) was in situ deposited on the surface of fibers of a non-woven fabric made of polyethylene terephthalate (PET). As-synthesized MnOx/PET were characterized by SEM, XRD, TEM, ATR-FTIR and XPS analysis. The growth of MnOx layer on PET is thought to start with partial hydrolysis of PET, followed by surface oxidation by KMnO4 and then surface-deposition of MnOx particles from the bulk phase. The MnOx particles assembled with nanosheets were uniformly coated on the PET fibers. MnOx/PET showed good activity for HCHO decomposition at room temperature which followed the Mars–van Krevelen mechanism. The removal of HCHO was kept over 94% after 10h continuous reaction under the conditions of inlet HCHO concentration ∼0.6mg/m3, space velocity ∼17,000h−1 and relative humidity∼50%. This research provides a facile method to deposit active MnOx onto polymers with low air resistance, and composite MnOx/PET material is promising for indoor air purification.
Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the ...observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a "brain" for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.
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•UV/chlorine process effectively mitigated membrane fouling caused by natural organic matter.•The membrane fouling was mainly consisted of intermediate blocking and standard blocking ...after UV/chlorine pretreatment.•UV/chlorine integrated with UF membrane significantly improved the removal of atrazine.•The operating cost of UV/chlorine is lower than UV/H2O2 or ozonation process.
This study describes some results concerning the hybrid process of ultraviolet/chlorine (UV/chlorine) as a pre-oxidation strategy prior to ultrafiltration for the treatment of natural organic matter (NOM)-contaminated surface water. Chlorine has been extensively used in water treatment since it can act as disinfectant or coagulant aid. In addition, the combination of UV and chlorine, compared to the other oxidants generally used in water treatment, offers a potentially effective and cheaper alternative for pre-oxidation process. Parallel tests with or without the application of chlorine were conducted to evaluate the effect of chlorine dosage on the increase of trans-membrane pressure under low dosage of UV irradiation. The results showed that UV/chlorine pre-oxidation achieved remarkable reduction of both total membrane fouling (49%) and reversible membrane fouling (59%) at 4 mg/L dose of chlorine as a result of the removal of both high molecular weight (MW) (1–20 kDa) humic substances and lower MW compounds. Hybrid process of UV/chlorine and UF membrane achieved significant removal of DOC (34%) and UV254 (49%) at chlorine dosage of 4 mg/L and a UV dosage of 180 mJ/cm2. Modeling fit results indicated that the NOM-related membrane fouling mitigation was probably attributed to the alleviation of intermediate pore blocking and standard pore blocking with intermediate pore blocking playing a more important role. Additionally, the UV/chlorine unit could have a bright prospect for engineering application as it performed better in cost control as pretreatment prior to UF membrane compared to other pre-oxidation processes.
In recent years, a variety of methods have been developed for indoor localization utilizing fingerprints of received signal strength (RSS) that are location dependent. Nevertheless, the RSS is ...sensitive to environmental variations, in that the resulting fluctuation severely degrades the localization accuracy. Furthermore, the fingerprints survey course is time-consuming and labor-intensive. Therefore, the lightweight fingerprint-based indoor positioning approach is preferred for practical applications. In this paper, a novel multiple-bandwidth generalized regression neural network (GRNN) with the outlier filter indoor positioning approach (GROF) is proposed. The GROF method is based on the GRNN, for which we adopt a new kind of multiple-bandwidth kernel architecture to achieve a more flexible regression performance than that of the traditional GRNN. In addition, an outlier filtering scheme adopting the k-nearest neighbor (KNN) method is embedded into the localization module so as to improve the localization robustness against environmental changes. We discuss the multiple-bandwidth spread value training process and the outlier filtering algorithm, and demonstrate the feasibility and performance of GROF through experiment data, using a Universal Software Radio Peripheral (USRP) platform. The experimental results indicate that the GROF method outperforms the positioning methods, based on the standard GRNN, KNN, or backpropagation neural network (BPNN), both in localization accuracy and robustness, without the extra training sample requirement.
This review presents recent advances of O3 decomposition catalysts and provides fundamental understandings of active sites and corresponding deactivation mechanism, which is helpful for the design of ...new O3 decomposition catalysts.
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Ozone (O3) plays essential roles in stratosphere and helps reduce the amount of harmful ultraviolet arriving the Earth’s surface. However, O3 is also a strong oxidant and causes troubles to human health in troposphere, especially in the confined space, such as indoor environment. Recently, O3 abatement materials have become research hotspots due to the urgent environmental demands. Catalysis is a facile strategy that can eliminate indoor airborne O3 efficiently and economically. Thus, this review summarizes the recent progresses of O3 decomposition catalysts. The catalysts covered here are categorized as follows: zeolite, metal organic frameworks (MOFs), metal oxides, noble metals. Manganese–based catalysts display higher efficiency and are mainly discussed. Generally, the active sites of O3 decomposition catalysts are described as Lewis acid sites (e.g., zeolite), metal sites (e.g., MOFs), oxygen vacancy sites (e.g., MnO2) in the previous work. In this review, we ascribe all the active sites to unsaturated metal sites and their Lewis acidity. Possible evidence from the experimental and theoretical perspectives are proposed. Furthermore, the strategy to circumvent deactivation caused by peroxides (O22–) accumulation and water molecular competition are also elaborated. Finally, perspective is presented on the challenges and opportunities of exploring existing and new O3 decomposition catalysts.
This paper investigates the problem of dynamic spectrum access for canonical wireless networks, in which the channel states are time-varying. In the most existing work, the commonly used optimization ...objective is to maximize the expectation of a certain metric (e.g., throughput or achievable rate). However, it is realized that expectation alone is not enough since some applications are sensitive to fluctuations. Effective capacity is a promising metric for time-varying service process since it characterizes the packet delay violating probability (regarded as an important statistical quality-of-service index), by taking into account not only the expectation but also other high-order statistic. Therefore, we formulate the interactions among the users in the time-varying environment as a non-cooperative game, in which the utility function is defined as the achieved effective capacity. We prove that it is an ordinal potential game which has at least one pure strategy Nash equilibrium. Based on an approximated utility function, we propose a multi-agent learning algorithm which is proved to achieve stable solutions with dynamic and incomplete information constraints. The convergence of the proposed learning algorithm is verified by simulation results. Also, it is shown that the proposed multi-agent learning algorithm achieves satisfactory performance.
Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract ...vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.
Abstract
The existing multi-person collaborative design scheme of Building Information Modeling (BIM) integrated with blockchain faces problems such as poor reliability of BIM drawing, inconsistent ...drawing information, redundant information, and inaccurate protection of copyright interests. This paper proposes a multi-person collaborative design model for BIM drawing that combines blockchain and InterPlanetary File System (IPFS). This model uses blockchain to store drawing design information to protect the copyright interests of designers and combines IPFS to ensure the reliability of drawing. A cycle division mechanism is designed to solve the problem of drawing information synchronization when multiple people collaborate in design. The Semantic Differential Transaction (SDT) method is used to achieve incremental update of drawing and reduce the information redundancy of the blockchain. Finally, a comparative analysis and validation evaluation of the scheme is carried out, and the usability of the scheme is illustrated with an illustrative example. The results show that: (1) proposed scheme is feasible for multi-person collaborative design; (2) proposed scheme can effectively ensure the reliability of drawing and reduce the redundancy of blockchain information, so as to achieve copyright protection for designers.
In this work, UV and UV/chlorine (UV/Cl) were employed to enhance powdered activated carbon (PAC) adsorption pretreatment prior to ultrafiltration process for algae-contaminated surface water ...treatment. Their performance on membrane fouling mitigation and organic pollutant rejection was systematically evaluated. A comparative experiment was conducted under varying pollution degrees of algal extracellular organic matter (EOM) contamination in surface river water. The results indicated that UV/PAC and UV/Cl/PAC pretreatment effectively enhanced the removal of dissolved organic carbon (DOC) and UV-absorbing at 254 nm (UV254). The characteristics of feed water after pretreatments were investigated through apparent molecular-weight (MW) distribution and fluorescence parallel factor analysis (PARAFAC). In regard to membrane fouling mitigation, UV/Cl/PAC noticeably decreased reversible and irreversible fouling resistance simultaneously and UV/PAC preferred reducing reversible membrane fouling. Combined fouling modeling was operated to scrutinize the fouling mitigation mechanisms and standard pore blocking was proved to be dominant during the filtration process. Moreover, the UV/Cl and UV/Cl/PAC pretreatments were proved positive for emerging micropollutants degradation and disinfection by-products formation potential reduction. The results suggested that UV and UV/Cl are likely strategies to enhance the efficiency of PAC adsorption pretreatments prior to ultrafiltration during algae-contaminated water treatment.
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•UV and UV/chlorine were adopted to assist PAC pretreatment to control membrane fouling caused by various algal-laden water.•Protein-like fluorescent fractions were preferentially removed by UV/PAC and UV/Cl/PAC.•UV/Cl/PAC significantly mitigated reversible and irreversible fouling by oxidation and adsorption.•UV and UV/Clorine improved the removal of micropollutants by PAC adsorption.•The formation potential of disinfection by-products was reduced by UV/Cl/PAC.
•In2O3 nanomaterials show much better activity than TiO2.•Photocatalytic half-life of PFOA was shortened to 5.3min.•Oxygen vacancies influence the activity for PFOA decomposition.•In2O3 porous ...microspheres, nanoplates and nanocubes were solvothermally synthesized.
Perfluorooctanoic acid (PFOA), an emerging persistent organic pollutant, recently receives worldwide concerns including methods for its efficient decomposition. Three kinds of nanostructured In2O3 materials including porous microspheres, nanocubes and nanoplates were obtained by dehydration of the corresponding In(OH)3 nanostructures at 500°C for 2h. The In(OH)3 nanostructures with different morphologies were solvothermally synthesized by using different mixed solvents. As-obtained In2O3 nanomaterials showed great photocatalytic activity for PFOA decomposing. The decomposition rates of PFOA by different In2O3 materials, i.e. porous microspheres, nanoplates and nanocubes were 74.7, 41.9 and 17.3 times as fast as that by P25 TiO2, respectively. The In2O3 porous microspheres showed the highest activity, by which the half-life of PFOA was shortened to 5.3min. The roles of surface oxygen vacancies on the adsorption and photocatalytic decomposition of PFOA were discussed, and it was found that In2O3 materials with higher oxygen vacancy defects show better activity.
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