PAR-Net: An Enhanced Dual-Stream CNN-ESN Architecture for Human Physical Activity Recognition.

Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.

Human Activity Recognition via Hybrid Deep Learning Based Model.

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.

Pilot Decontamination Using Asynchronous Fractional Pilot Scheduling in Massive MIMO Systems.

Due to large spectral efficiency and low power consumption, the Massive Multiple-Input-Multiple-Output (MIMO) became a promising technology for the 5G system. However, pilot contamination (PC) limits the performance of massive MIMO systems. Therefore, two pilot scheduling schemes (i.e., Fractional Pilot Reuse (FPR) and asynchronous fractional pilot scheduling scheme (AFPS)) are proposed, which significantly mitigated the PC in the uplink time division duplex (TDD) massive MIMO system. In the FPR scheme, all the users are distributed into the central cell and edge cell users depending upon their signal to interference plus noise ratio (SINR). Further, the capacity of central and edge users is derived in terms of sum-rate, and the ideal number of the pilot is calculated which significantly maximized the sum rate. In the proposed AFPS scheme, the users are grouped into central users and edge users depending upon the interference they receive. The central users are assigned the same set of pilots because these users are less affected by interference, while the edge users are assigned the orthogonal pilots because these users are severely affected by interference. Consequently, the pilot overhead is reduced and inter-cell interference (ICI) is minimized. Further, results verify that the proposed schemes outperform the previous proposed traditional schemes, in terms of improved sum rates.

Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid.

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer's knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.

A review of airport dual energy X-ray baggage inspection techniques: Image enhancement and noise reduction.

In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.

Microbial spoilage, instability risk of antacid suspension in the presence of commonly used preservative system.

Manifestation of microbial spoilage of any product by bacteria and to assess the effectiveness of the anti-microbial preservatives (parabens) used for the prevention and stability purpose. The aim of the present work is to study the effectiveness of preservatives used in the antacid suspensions and to analyze the effect of microbial growth on the quality of respective antacid suspensions. Samples of various antacid suspensions were randomly collected from local market and Government hospital pharmacies. Three different antacid formulations were prepared in the laboratory. All the formulations were preliminarily evaluated on the basis of organoleptic characteristics, pH, viscosity and assay. Efficacy of the preservative system in suspension formulation was determined by inoculating the samples in its final container, with specific strains of bacteria i.e. Escherichia coli ATCC 8739, Pseudomonas aeruginosa ATCC 9027 and Staphylococcus aureus ATCC 6538, taking samples from the inoculated preparation at specified intervals of time i.e. 0 time, 07 days, 14 days and 28 days, growing it on nutrient agar medium and colony forming units (CFUs) were scored by plate count. At the same time the samples were also subjected to qualitative and quantitative testing. The decrease in CFU and alteration in assay, pH and viscosity was observed in all the formulations except formulation M2 and F3 that showed stability throughout the study period.

Green leadership in manufacturing industry: Unveiling the green Revolution's impact on organizational performance.

This study aims to analyze the role of green transformational leadership and human resource management in organizational performance with the mediating effect of green innovation. The study also proposes the moderating role of commitment to management change. The study used a quantitative research method with a random sampling technique and surveyed 235 managers from the Cement industry across Pakistan. The statistical techniques used to assess the study's hypotheses were partial least squares structural equation modeling (PLS-SEM) and SPSS. The findings show that green transformational leadership and HRM positively impact organizational performance, with green innovation partially mediating effect. Furthermore, affective commitment to management change strongly moderates the relationship between green innovation and organizational performance. In addition, the proposed relationship is based on the resource-based view (RBV) theory. Therefore, Managers and executives should adopt green principles and practices and build an environmentally friendly culture that encourages employees to engage in green behaviors and activities to improve organizational performance. To successfully implement green practices in a developing country like Pakistan, the Cement sector must understand how the cement industry improves its performance and whether employees demonstrate green innovation.

Enhanced cellulose nanofiber mechanical stability through ionic crosslinking and interpretation of adsorption data using machine learning.

Herein we report the fabrication of cationic functionalized cellulose nanofibers (c-CNF) having 0.13 mmol.g-1 ammonium content and its ionic crosslinking via the pad-batch process. The overall chemical modifications were justified through infrared spectroscopy. It is revealed that the tensile strength of ionic crosslinked c-CNF (zc-CNF) improved from 3.8 MPa to 5.4 MPa over c-CNF. The adsorption capacity of zc--CNF was found to be 158 mg.g-1 followed by the Thomas model. Further, the experimental data were used to train and test a series of machine learning (ML) models. A total of 23 various classical ML models (as a benchmark) were compared simultaneously using Pycaret which helped reduce the programming complexity. However, shallow, and deep neural networks are used that outperformed the classic machine learning models. The best classical-tuned ML model using Random Forests regression had an accuracy of 92.6 %. The deep neural network made effective by early stopping and dropout regularization techniques, with 20 × 6 (Neurons x Layers) configuration, showed an appreciable prediction accuracy of 96 %.

A critical review in recent progress of hollow fiber membrane contactors for efficient CO2 separations.

Among wide range of membrane-based operations, membrane contactors, as they reify comparatively modern membrane-based mechanism are gaining quite an attention in both pilot and industrial scales. In recent literature, carbon capture is one of the most researched applications of membrane contactors. Membrane contactors have the potential to minimize the energy consumption and capital cost of traditional CO2 absorptions columns. In a membrane contactor, CO2 regeneration can take place below the solvent boiling point, resulting into lower consumption of energy. Various polymeric as well as ceramic membrane materials have been employed in gas liquid membrane contactors along with several solvents including amino acids, ammonia, amines etc. This review article provides detailed introduction of membrane contactors in terms of CO2 removal. It also discusses that the main challenge that is faced by membrane contactors is membrane pore wetting caused by solvent that in turn can reduce the mass transfer coefficient. Other potential challenges such as selection of suitable solvent and membrane pair as well as fouling are also discussed in this review and are followed by potential ways to reduce them. Furthermore, both membrane gas separation and membrane contactor technologies are analysed and compared in this study on the basis of their characteristics, CO2 separation performances and techno economical transvaluation. Consequently, this review provides an opportunity to thoroughly understand the working principle of membrane contactors along its comparison with membrane-based gas separation technology. It also provides a clear understanding of latest innovations in membrane contactor module designs as well as challenges encountered by membrane contactors along with possible solutions to overcome these challenges. Finally, semi commercial and commercial implementation of membrane contactors has been highlighted.

Challenges, Opportunities and Future Directions of Membrane Technology for Natural Gas Purification: A Critical Review.

Natural gas is an important and fast-growing energy resource in the world and its purification is important in order to reduce environmental hazards and to meet the required quality standards set down by notable pipeline transmission, as well as distribution companies. Therefore, membrane technology has received great attention as it is considered an attractive option for the purification of natural gas in order to remove impurities such as carbon dioxide (CO2) and hydrogen sulphide (H2S) to meet the usage and transportation requirements. It is also recognized as an appealing alternative to other natural gas purification technologies such as adsorption and cryogenic processes due to its low cost, low energy requirement, easy membrane fabrication process and less requirement for supervision. During the past few decades, membrane-based gas separation technology employing hollow fibers (HF) has emerged as a leading technology and underwent rapid growth. Moreover, hollow fiber (HF) membranes have many advantages including high specific surface area, fewer requirements for maintenance and pre-treatment. However, applications of hollow fiber membranes are sometimes restricted by problems related to their low tensile strength as they are likely to get damaged in high-pressure applications. In this context, braid reinforced hollow fiber membranes offer a solution to this problem and can enhance the mechanical strength and lifespan of hollow fiber membranes. The present review includes a discussion about different materials used to fabricate gas separation membranes such as inorganic, organic and mixed matrix membranes (MMM). This review also includes a discussion about braid reinforced hollow fiber (BRHF) membranes and their ability to be used in natural gas purification as they can tackle high feed pressure and aggressive feeds without getting damaged or broken. A BRHF membrane possesses high tensile strength as compared to a self-supported membrane and if there is good interfacial bonding between the braid and the separation layer, high tensile strength, i.e., upto 170Mpa can be achieved, and due to these factors, it is expected that BRHF membranes could give promising results when used for the purification of natural gas.

Graphene-based nanomaterials as antimicrobial surface coatings: A parallel approach to restrain the expansion of COVID-19.

The recently emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a significant and topmost global health challenge of today. SARS-CoV-2 can propagate through several direct or indirect means resulting in its exponential spread in short times. Consequently, finding new research based real-world and feasible solutions to interrupt the spread of pathogenic microorganisms is indispensable. It has been established that this virus can survive on a variety of available surfaces ranging from a few hours to a few days, which has increased the risk of COVID-19 spread to large populations. Currently, available surface disinfectant chemicals provide only a temporary solution and are not recommended to be used in the long run due to their toxicity and irritation. Apart from the urgent development of vaccine and antiviral drugs, there is also a need to design and develop surface disinfectant antiviral coatings for long-term applications even for new variants. The unique physicochemical properties of graphene-based nanomaterials (GBNs) have been widely investigated for antimicrobial applications. However, the research work for their use in antimicrobial surface coatings is minimal. This perspective enlightens the scope of using GBNs as antimicrobial/antiviral surface coatings to reduce the spread of transmittable microorganisms, precisely, SARS-CoV-2. This study attempts to demonstrate the synergistic effect of GBNs and metallic nanoparticles (MNPs), for their potential antiviral applications in the development of surface disinfectant coatings. Some proposed mechanisms for the antiviral activity of the graphene family against SARS-CoV-2 has also been explained. It is anticipated that this study will potentially lead to new insights and future trends to develop a framework for further investigation on this research area of pivotal importance to minimize the transmission of current and any future viral outbreaks.

Research and Development Journey and Future Trends of Hollow Fiber Membranes for Purification Applications (1970-2020): A Bibliometric Analysis.

Hollow fiber membrane (HFM) technology has received significant attention due to its broad range separation and purification applications in the industry. In the current study, we applied bibliometric analysis to evaluate the global research trends on key applications of HFMs by evaluating the global publication outputs. Results obtained from 5626 published articles (1970-2020) from the Scopus database were further manipulated using VOSviewer software through cartography analysis. The study emphasizes the performance of most influential annual publications covering mainstream journals, leading countries, institutions, leading authors and author's keywords, as well as future research trends. The study found that 62% of the global HFM publications were contributed by China, USA, Singapore, Japan and Malaysia, followed by 77 other countries. This study will stimulate the researchers by showing the future-minded research directions when they select new research areas, particularly in those related to water treatment, biomedical and gas separation applications of HFM.