The COVID-19 pandemic is straining public health systems and the global economy, triggering unprecedented measures by governments around the globe. The adoption of a preventive measure is required to ...control the spread. This research explores the impact of influencing factors like COVID-19 knowledge, behavioral control, moral and subject norms, preventive e-guidelines by the government, and environmental factors on the intention to prevent COVID-19 and risk aversion. A cross-sectional study was performed of 310 respondents about different COVID-19 related influencing factors in Pakistan. The partial least square-structural equation modeling was applied to estimate the path coefficient. Moral and subject norms (0.359) had a comparatively higher path coefficient. Other influencing factors/drivers were preventive e-guideline by the government (0.215) followed by COVID-19 knowledge (0.197), and behavioral control (0.121). The intention to prevent COVID-19 showed a positive and significant impact (0.705) on risk aversion. The indirect analysis also confirmed that the positive influence of moral and subject norms, COVID-19 knowledge, preventive e-guideline by the government, and behavioral control on risk aversion. However, the path coefficient of environmental factors was negative but insignificant, which implies than environmental factors do not influence the intention to prevent COVID-19. It is suggested to provide clear guidelines using print, social, electronic media. It is also suggested to provide e-guidelines in local languages. The COVID-19 knowledge about its transmission, symptoms, and precautions is also useful. It is suggested to include the causes, symptoms, and precaution of viral diseases in the educational syllabus. The government should ensure the availability of preventive medical items like surgical masks and sanitizers to meet the demand of the public.
Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and ...infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model's effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.
Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression (GBR), Artificial ...Neural Networks (ANNs), Extreme Learning Machine (ELM) and Support Vector Machine (SVM). These models encounter high-level of non-linearity between input data and output predictions and limited adoptability in real-world scenarios. Meanwhile, energy forecasting domain demands more robustness, higher prediction accuracy and generalization ability for real-world implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs Convolution Neural Network (CNN) and Gated Recurrent Units (GRU) into a unified framework for accurate energy consumption prediction. The proposed framework has two major phases: (1) data refinement and (2) training, where the data refinement phase applies preprocessing strategies over raw data. In the training phase, CNN features are extracted from input dataset and fed in to GRU, that is selected as optimal and observed to have enhanced sequence learning abilities after extensive experiments. The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features' extraction potentials of CNNs and effectual gated structure of multi-layered GRU. The experimental evaluation over existing energy forecasting datasets reveal the better performance of our method in terms of preciseness and efficiency. The proposed method achieved the smallest error rate on Appliances Energy Prediction (AEP) and Individual Household Electric Power Consumption (IHEPC) datasets, when compared to other baseline models.
Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential ...buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters' data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting.
Bleaching is one of the most highly polluting textile wet processes. High water and chemical consumption, and high volume of the resulting effluent, have huge implications for the environment. In ...this study, an aerosol process has been compared with the conventional process for bleaching of textile socks. In the conventional bleaching process, socks are immersed into a rotating drum filled with water to ensure uniform application of chemicals, while in the aerosol bleaching process, compressed air was mixed with the chemical solution to generate a cloud of bleaching reagents inside the rotating drum of the exhaust processing machine, using the minimum amount of water. The results show comparable whiteness results of the bleached socks, with up to 75% water saved in the aerosol beaching process as compared to the conventional bleaching process. Due to lower water consumption, the overall chemical consumption is also significantly reduced. Citric acid and silicone softener consumption of the process were reduced by 28% and 60%, respectively. The aerosol technology can be retrofitted on existing conventional machines with minimal initial investment and shows immense potential for reduction in water and chemical consumption and harmful impacts on the environment. This study aimed to align developments in the textile and apparel industry with sustainable development goals #6, 12, and 14, which are clean water and sanitation, responsible consumption and production, and life below water, respectively.
Abstract
Selected volatile organic compounds (VOCs), such as benzene (C
6
H
6
), cyclohexane (C
6
H
12
), isoprene (C
5
H
8
), cyclopropanone (C
3
H
4
O), propanol (C
3
H
8
O), and butyraldehyde ...butanal (C
4
H
8
O), in exhaled human breath can act as indicators or biomarkers of lung cancer diseases. Detection of such VOCs with low density would pave the way for an early diagnosis of the disease and thus early treatment and cure. In the present investigation, the density-functional theory (DFT) is applied to study the detection of the mentioned VOCs on Ti
3
C
2
T
X
MXenes, saturated with the functional groups T
x
= O, F, S, and OH. For selectivity, comparative sensing of other interfering air molecules from exhaled breath, such as O
2
, N
2
, CO
2
, and H
2
O is further undertaken. Three functionalization (T
x
= O, F, and S) are found promising for the selective detection of the studied VOCs, in particular Ti
3
C
2
O
2
MXenes has shown distinct sensor response toward the C
5
H
8
, C
6
H
6
, C
6
H
12
, and C
3
H
4
O. The relatively strong physisorption (
$${E}_{ads}\cong -0.45 to-0.65 {\text{eV}}$$
E
ads
≅
-
0.45
t
o
-
0.65
eV
), triggered between VOC and MXene due to an enhancement of van der Waals interaction, is found responsible to affect the near Fermi level states, which in turn controls the conductivity and consequently the sensor response. Meanwhile, such intermediate-strength interactions remain moderate to yield small desorption recovery time (of order
$$\tau \cong \mu {\text{s}}-{\text{ms}})$$
τ
≅
μ
s
-
ms
)
using visible light at room temperature. Thus, Ti
3
C
2
O
2
MXenes are found promising candidate material for reusable biosensor for the early diagnosis of lung cancer diseases through the VOC detection in exhaled breath.
The massive amount of video data produced by surveillance networks in industries instigate various challenges in exploring these videos for many applications, such as video summarization (VS), ...analysis, indexing, and retrieval. The task of multiview video summarization (MVS) is very challenging due to the gigantic size of data, redundancy, overlapping in views, light variations, and interview correlations. To address these challenges, various low-level features and clustering-based soft computing techniques are proposed that cannot fully exploit MVS. In this article, we achieve MVS by integrating deep neural network based soft computing techniques in a two-tier framework. The first online tier performs target-appearance-based shots segmentation and stores them in a lookup table that is transmitted to cloud for further processing. The second tier extracts deep features from each frame of a sequence in the lookup table and pass them to deep bidirectional long short-term memory (DB-LSTM) to acquire probabilities of informativeness and generates a summary. Experimental evaluation on benchmark dataset and industrial surveillance data from YouTube confirms the better performance of our system compared to the state-of-the-art MVS methods.
The high-level variation of different energy generation resources makes the reliable power supply significantly challenging to end-users. These variations occur due to the intermittent nature of ...energy output and time-varying weather conditions. The recent literature focuses on the improvements in power generation and consumption forecasting, which is a demand of the current smart grids’ smooth operations with a balanced amount of energy generation and consumption for the connected customers. Inspired by the applications of load forecasting, therefore, in this work, we develop an efficient and effective hybrid model for power generation and consumption forecasting, thereby contributing to energy harvesting by providing valuable prediction data to the concerned renewable energy analysts. Herein, we integrate a convolutional neural network with an echo state network for robust renewable energy generation and consumption forecasting. The convolutional network is used to extract meaningful patterns from the historical data which is then forwarded to the echo state network for temporal features learning. The output spatiotemporal feature vector is then fed to fully connected layers for final forecasting. The proposed hybrid model is derived after extensive experiments over machine and deep learning models, where the results indicate that the proposed model substantially decreases the forecasting errors using RMSE, MSE, NRMSE, and MAE metrics, when compared to state-of-the-art models and acts as a paradigm towards energy equilibrium between production resources and consumers.
Microbial population of soils irrigated with industrial wastewater may contain certain exopolysaccharides (EPS) and indole-3-acetic acid (IAA) producing bacterial strains having the ability to ...tolerate heavy metals along with plant growth–promoting (PGP) traits. As cadmium is one of the most toxic heavy metals for soils, plants, animals, and human beings, the present study was planned to isolate and characterize EPS- and IAA-producing, Cd-tolerant bacterial strains having tolerance against heavy metals along with plant growth–promoting traits. A total of 30 rhizobacterial strains (FN1–FN30) were isolated from rhizosphere soil collected from fields around industrial areas and roadsides irrigated with industrial wastewater. Out of these, eight isolates with the combined ability of IAA production and EPS production were characterized for PGP traits. On the basis of multifarious PGP traits and the results of root colonization assay, three most efficient EPS- and IAA-producing, Cd-tolerant plant growth–promoting strains, i.e., FN13, FN14, and FN16, were selected for multiple metal (Cd, Pb, Ni, and Cu) tolerance test along with quantification of growth, and IAA and EPS production abilities under Cd stress. Increasing levels of Cd stress negatively affected the tested characteristics of these strains, but FN13 showed more stability in growth, IAA production (18.24 μg mL
−1
), and EPS production (148.99 μg mL
−1
) compared to other strains under Cd stress. The morphological and biochemical analysis confirmed FN13 as Gram-positive, rod-shaped bacteria with smooth colonies of yellow appearance. The strain FN13 has strong root colonization (3.36 × 10
6
CFU g
−1
) ability for mustard seedlings and can solubilize Zn and phosphate along with the production of HCN, ammonia, and siderophores. The 16S rRNA sequencing confirmed it as the
Bacillus safensis
strain FN13. It can be explored as potential phytostabilizing biofertilizer for heavy metal–contaminated soils.
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