Carbonaceous materials are abundantly used for electrochemical applications and especially for energy and environmental purposes. In this review, the carbon felt (CF) based-electrodes are discussed ...in a holistic manner. First of all, the study centers on the issues relevant to pristine CF materials such as manufacturing method and specific properties. The various methods and equations used to identify physical values of CF material are supplied. As main part of the review, the different modification methods for CF electrodes are described. The novel properties of fabricated materials are characterized by physical as well as electrochemical techniques. The strengths of each method are presented in the comparison with raw CF electrodes. The energy applications of CF based-electrodes are figured out in various fields such as vanadium redox flow batteries (VRFB), microbial fuel cells (MFCs), biofuel cells (BFCs), capacitors, solar cells and lithium ion batteries. For environmental applications, we focus our study on the wastewater treatment containing biorefractory pollutants by electro-Fenton (EF) process. The degradation result by EF technology using CF materials is impressive when most of toxic contaminants are mineralized to non-toxic compounds at the end of the electrolysis. To decrease the cost treatment and upgrade the treatment efficiency, the EF system has been improved by using modified electrodes or new catalyst sources. The CF materials are also investigated to apply in bio-fuel cell-Fenton in which electrons were produced from fuel cell (FC) towards zero-energy depollution. Finally, the sketches about EF pilot open new gates for application of CF materials in industrial areas.
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Load identification is a core concept in non-intrusive load monitoring (NILM). Through NILM systems, users can check their home appliance usage habits and then adjust their behavior to save ...electricity. In this way, a NILM system offers an effective method to detect the event status of household appliances as well as individual loads' energy consumption. However, prior NILM methods have encountered a challenge in improving recognition accuracy for both linear load and non-linear load types. These methods used a representative feature, namely transient load signals. However, the transient signals on these loads differ in terms of transient time and transient shape, which is the main cause of reduced accuracy performance in load identification. To this end, this paper presents a novel method, HT-LSTM (Hilbert Transform Long Short-Term Memory), which enhances recognition of the various load types that contain the difference in the transient time and the transient shape of any load signal. The proposed method consists of two main parts: (i) generating a novel transient feature based on a Hilbert transform (HT), called APF (Amplitude-Phase-Frequency). APF features are sequential data, which is used for the classification model; and (ii) applying Sequence-to-Sequence Long Short-Term Memory (Seq2Seq LSTM) to identify appliances by using APF features as the input data. In this work, we evaluate the HT-LSTM method using two high-frequency public datasets, Building-Level fUlly-labeled dataset for Electricity Disaggregation (BLUED) and Plug Load Appliance Identification Dataset (PLAID). Also, we evaluate our method using a private dataset collected in the lab. Based on the experimental results obtained and comparison classification performance pointed, the proposed method outperforms previous methods of F-score measurement on both public datasets in load identification as well as the private dataset.
The chemical composition and larvicidal activity of essential oils from the leaves and rhizomes of Zingiber collinsii Mood & Theilade (Zingiberaceae) were reported. The main compounds in the leaf oil ...were α-pinene (25.6%), β-caryophyllene (16.8%), β-pinene (16.1%) and bicyclogermacrene (6.9%) while the rhizome oil consist mainly of camphene (22.5%), β-pinene (16.3%), α-pinene (9.0%) and humulene oxide II (9.0%). The rhizome oil demonstrated larvicidal effects towards fourth instant larvae of mosquito vectors. The highest mortality (100%) was observed at 24 h exposure against Aedes albopictus (concentration 100 μg/mL) and 48 h (concentration of 50 and 100 μg/mL), while the highest mortality (100%) was observed for Culex quinquefasciatus at 24 h and 48 h at concentration of 100 μg/mL. The 24 h mosquito larvicidal activity of the rhizome oil against Ae. albopictus were LC50 = 25.51 μg/mL; LC90 = 40.22 μg/mL and towards Cx. quinquefasciatus with LC50 = 50.11 μg/mL and LC90 = 71.53 μg/mL). However, the 48 h larvicidal activity were LC50 = 20.03 μg/mL and LC90 = 24.51 μg/mL (Ae. albopictus), as well as LC50 = 36.18 μg/mL and LC90 = 55.11 μg/mL (Cx. quinquefasciatus). On the other hand, no appreciable mortality and larvicidal activity was observed for the leaf oil. The larvicidal activity of the essential oils of Z. collinsii was being reported for the first time.
In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep ...neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.
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Porous carbon cathode (PCF) was fabricated by thermal treatment at high temperature.An increase of 700 times of the surface area and 10 times of the electroactive surface area is ...observed.The PCF was applied for the removal of paracetamol (PCM) in acidic aqueous medium using an electro-Fenton process.Mineralization of PCM on PCF shows an improvement of 31% compared to the non-modified cathode after 2h of electrolysis.The porous carbon cathode was stable after 10 cycles.
Porous carbon cathode (PCF) was fabricated by thermal treatment at high temperature under a nitrogen gas flow mixed with 1% of oxygen. Scanning electron microscopy results revealed a homogenous porosity covered the carbon fibres. This property improved significantly the hydrophilicity that supported the oxygen reduction reaction (ORR) in electro-Fenton process as confirmed by contact angle measurements. In addition, an increase of 700 times of the surface area is observed after the thermal treatment. The crystalline average size of the new material was also ameliorated during thermal treatment as observed by X-ray diffraction pattern (XRD) due to the selective etching of amorphous carbon. Porous cathode exhibited also better electrochemical performances than raw carbon felt cathode as proved by cyclic voltammograms (CVs) because of the higher electroactive surface area. According to the Randles-Sevcik formula, the electroactive surface area of PCF was 10 times higher than raw CF. The concentration of H2O2 on PCF and raw CF was 24.6, 7.9mgL⿿1 respectively after 80min. The porous cathode was applied for removal of paracetamol (PCM) in acidic aqueous medium using an electro-Fenton process. Mineralization of PCM was followed by total organic carbon (TOC) measurements and an improvement of 31% was observed compared to the non-modified cathode after 2h of electrolysis. The porous carbon cathode kept its stability after 10 cycles.
A new cathode for electro-Fenton process was set up by electrochemical deposition of reduced graphene oxide (rGO) on the surface of carbon felt (CF). The structure and properties of modified ...electrode was investigated. Among the different reduction methods used, the constant potential technique demonstrated significant advantages. The parameters affecting the conversion of GO to rGO, such as pH, applied potential and duration of the reduction process, were investigated. The rGO modified cathode presents enhanced electrochemical properties like an increase of the redox current and a decrease of the charge transfer resistance in presence of the redox probe Fe(CN)63−/Fe(CN)64− showing better kinetic properties compared to raw CF. This improvement enhanced significantly the production of hydrogen peroxide, a key parameter in electro-Fenton (EF) process, which was confirmed by linear scanning voltammetry (LSV) analysis. Therefore, the use of graphene modified cathode could decolorize efficiently Acid Orange 7, a model azo dye molecule, within only 5min and almost completely mineralized (94.3%) it in 8h treatment under optimal current density applied. The new cathode exhibited good stability as the mineralization ratio in 2h that was still above 64% after 10 cycles’ degradation, showing that this rGO-CF is a powerful and promising electrode for improving the removal efficiency of dye pollutants using EF technology.
Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that ...all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collects local models and constructs the global model. Then, we formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers. In the proposed game, each mobile user submits its bids according to the minimal energy cost that the mobile users experiences in participating in FL. To decide winners in the auction and maximize social welfare, we propose the primal-dual greedy auction mechanism. The proposed mechanism can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency. Finally, numerical results are shown to demonstrate the performance effectiveness of our proposed mechanism.
This paper contributes to the literature on the impact of financial liberalization by including the earnings of self-employed while investigating the core determinants and mechanisms driving the ...income share going to labor during financial integration. The question of the precise impact of liberalization on the share of the self-employed has received less attention in the literature. The author also uses both measures of capital account openness: de jure and de facto indicators. The empirical work is applied for a panel dataset of 30 countries during the period of 1970–2013. Despite using different measurement methods of financial openness, the results from all specifications support the hypothesis that financial integration leads to a decline in the labor share of income for the all countries sample.
A thermally stable perovskite solar cell (PSC) based on a new molecular hole transporter (MHT) of 1,3‐bis(5‐(4‐(bis(4‐methoxyphenyl) ...amino)phenyl)thieno3,2‐bthiophen‐2‐yl)‐5‐octyl‐4H‐thieno3,4‐cpyrrole‐4,6(5H)‐dione (coded HL38) is reported. Hole mobility of 1.36 × 10−3 cm2 V−1 s−1 and glass transition temperature of 92.2 °C are determined for the HL38 doped with lithium bis(trifluoromethanesulfonyl)imide and 4‐tert‐butylpyridine as additives. Interface engineering with 2‐(2‐aminoethyl)thiophene hydroiodide (2‐TEAI) between the perovskite and the HL38 improves the power conversion efficiency (PCE) from 19.60% (untreated) to 21.98%, and this champion PCE is even higher than that of the additive‐containing 2,2′,7,7′‐tetrakis(N,N‐di‐p‐methoxyphenylamine)‐9,9′‐spirobifluorene (spiro‐MeOTAD)‐based device (21.15%). Thermal stability testing at 85 °C for over 1000 h shows that the HL38‐based PSC retains 85.9% of the initial PCE, while the spiro‐MeOTAD‐based PSC degrades unrecoverably from 21.1% to 5.8%. Time‐of‐flight secondary‐ion mass spectrometry studies combined with Fourier transform infrared spectroscopy reveal that HL38 shows lower lithium ion diffusivity than spiro‐MeOTAD due to a strong complexation of the Li+ with HL38, which is responsible for the higher degree of thermal stability. This work delivers an important message that capturing mobile Li+ in a hole‐transporting layer is critical in designing novel MHTs for improving the thermal stability of PSCs. In addition, it also highlights the impact of interface design on non‐conventional MHTs.
A thermally stable perovskite solar cell is developed by capturing mobile lithium ions using a new molecular hole transporter, 1,3‐bis(5‐(4‐(bis(4‐methoxyphenyl)amino)phenyl)thieno3,2‐bthiophen‐2‐yl)‐5‐octyl‐4H‐thieno3,4‐cpyrrole‐4,6(5H)‐dione (coded HL38), where a strong interaction of the lithium ions in lithium bis(trifluoromethanesulfonyl)imide with the 5‐octylthieno3,4‐cpyrrole‐4,6‐dione (octyl‐TPD) moiety in HL38 is responsible for maintaining ≈86% of the initial power conversion efficiency for over 1000 h at 85 °C.
The Industrial Internet of Things (IIoT) has advanced digital technology and the fastest interconnection, which creates opportunities to substantially grow industrial businesses today. Although IIoT ...provides promising opportunities for growth, the massive sensor IoT data collected are easily attacked by cyber criminals. Hence, IIoT requires different high security levels to protect the network. An Intrusion Detection System (IDS) is one of the crucial security solutions, which aims to detect the network’s abnormal behavior and monitor safe network traffic to avoid attacks. In particular, the effectiveness of the Machine Learning (ML)-based IDS approach to building a secure IDS application is attracting the security research community in both the general cyber network and the specific IIoT network. However, most available IIoT datasets contain multiclass output data with imbalanced distributions. This is the main reason for the reduction in the detection accuracy of attacks of the ML-based IDS model. This research proposes an IDS for IIoT imbalanced datasets by applying the eXtremely Gradient Boosting (XGBoost) model to overcome this issue. Two modern IIoT imbalanced datasets were used to assess our proposed method’s effectiveness and robustness, X-IIoTDS and TON_IoT. The XGBoost model achieved excellent attack detection with F1 scores of 99.9% and 99.87% on the two datasets. This result demonstrated that the proposed approach improved the detection attack performance in imbalanced multiclass IIoT datasets and was superior to existing IDS frameworks.
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