Multi-Layered Filtration Framework for Efficient Detection of Network Attacks Using Machine Learning.

The advancements and reliance on digital data necessitates dependence on information technology. The growing amount of digital data and their availability over the Internet have given rise to the problem of information security. With the increase in connectivity among devices and networks, maintaining the information security of an asset has now become essential for an organization. Intrusion detection systems (IDS) are widely used in networks for protection against different network attacks. Several machine-learning-based techniques have been used among researchers for the implementation of anomaly-based IDS (AIDS). In the past, the focus primarily remained on the improvement of the accuracy of the system. Efficiency with respect to time is an important aspect of an IDS, which most of the research has thus far somewhat overlooked. For this purpose, we propose a multi-layered filtration framework (MLFF) for feature reduction using a statistical approach. The proposed framework helps reduce the detection time without affecting the accuracy. We use the CIC-IDS2017 dataset for experiments. The proposed framework contains three filters and is connected in sequential order. The accuracy, precision, recall and F1 score are calculated against the selected machine learning models. In addition, the training time and the detection time are also calculated because these parameters are considered important in measuring the performance of a detection system. Generally, decision tree models, random forest methods, and artificial neural networks show better results in the detection of network attacks with minimum detection time.

NOMA-Based VLC Systems: A Comprehensive Review.

The enhanced proliferation of connected entities needs a deployment of innovative technologies for the next generation wireless networks. One of the critical concerns, however, is the spectrum scarcity, due to the unprecedented broadcast penetration rate nowadays. Based on this, visible light communication (VLC) has recently emerged as a viable solution to secure high-speed communications. VLC, a high data rate communication technology, has proven its stature as a promising complementary to its radio frequency (RF) counterpart. VLC is a cost-effective, energy-efficient, and secure technology that exploits the current infrastructure, specifically within indoor and underwater environments. Yet, despite their appealing capabilities, VLC systems face several limitations which constraint their potentials such as LED's limited bandwidth, dimming, flickering, line-of-sight (LOS) requirement, impact of harsh weather conditions, noise, interference, shadowing, transceiver alignment, signal decoding complexity, and mobility issue. Consequently, non-orthogonal multiple access (NOMA) has been considered an effective technique to circumvent these shortcomings. The NOMA scheme has emerged as a revolutionary paradigm to address the shortcomings of VLC systems. The potentials of NOMA are to increase the number of users, system's capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. Motivated by this, the presented study offers an overview of NOMA-based VLC systems. This article provides a broad scope of existing research activities of NOMA-based VLC systems. This article aims to provide firsthand knowledge of the prominence of NOMA and VLC and surveys several NOMA-enabled VLC systems. We briefly highlight the potential and capabilities of NOMA-based VLC systems. In addition, we outline the integration of such systems with several emerging technologies such as intelligent reflecting surfaces (IRS), orthogonal frequency division multiplexing (OFDM), multiple-input and multiple-output (MIMO) and unmanned aerial vehicles (UAVs). Furthermore, we focus on NOMA-based hybrid RF/VLC networks and discuss the role of machine learning (ML) tools and physical layer security (PLS) in this domain. In addition, this study also highlights diverse and significant technical hindrances prevailing in NOMA-based VLC systems. We highlight future research directions, along with provided insights that are envisioned to be helpful towards the effective practical deployment of such systems. In a nutshell, this review highlights the existing and ongoing research activities for NOMA-based VLC systems, which will provide sufficient guidelines for research communities working in this domain and it will pave the way for successful deployment of these systems.

Thermo-Chemical Modification of Cellulose for the Adsorptive Removal of Titan Yellow from Wastewater.

This research work focuses on the isolation and thermo-chemical modification of cellulose and its application as an adsorbent for the removal of organic pollutants. The used cellulose was collected from a locally available plant (Olive Europa) commonly called Zaitoon. The stem branches of Zaitoon were collected and then kept in water for 40-45 days at room temperature to extract the cellulose fibers. These cellulose fibers were then kept in the Soxhlet apparatus for washing in n-hexane for 72 h. The purified cellulose was divided into three parts: one part was subjected to thermal activation (TAC), the second was modified chemically (CMC) with Benzyl Chloride, while the last one remained un-functionalized (UFC). All the three forms of cellulose were characterized via FTIR and SEM, then utilized for the removal of Titan Yellow (TY) dye from aqueous media via adsorption process by varying the contact time, temperature, concentration of dye and type, and dose of adsorbent. The adsorption efficiencies of all adsorbents were compared under different experimental variables. Thermally activated cellulose showed the best results for the removal of TY compared with other materials. The calculated removal percentage of TY was found to be 97.69, 94.83, 94.83, and 98% under equilibrium conditions of contact time, temperature, adsorbent dose, and TY concentration. Similarly, the uptake capacities of TAC under optimal experimental conditions were found to be 19.56, 18.96, 18.52, and 18.75 mg/g. Thermodynamic studies of TAC, CMC, and UFC showed that the values of ΔG are negative, while those of ΔH and ΔS are positive in all cases and at all temperatures. This indicates that the TY elimination process is endothermic and spontaneous with an entropy-driven nature. The obtained results indicate that the as-fabricated low-cost biomaterials can effectively remove dyes from wastewater through physicochemical interactions. The removal process was influenced by the nature of the adsorbent and the operating variables.

Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain.

Electroencephalography (EEG) signals are considered one of the oldest techniques for detecting disorders in medical signal processing. However, brain complexity and the non-stationary nature of EEG signals represent a challenge when applying this technique. The current paper proposes new geometrical features for classification of seizure (S) and seizure-free (SF) EEG signals with respect to the Poincaré pattern of discrete wavelet transform (DWT) coefficients. DWT decomposes EEG signal to four levels, and thus Poincaré plot is shown for coefficients. Due to patterns of the Poincaré plot, novel geometrical features are computed from EEG signals. The computed features are involved in standard descriptors of 2­D projection (STD), summation of triangle area using consecutive points (STA), as well as summation of shortest distance from each point relative to the 45-degree line (SSHD), and summation of distance from each point relative to the coordinate center (SDTC). The proposed procedure leads to discriminate features between S and SF EEG signals. Thereafter, a binary particle swarm optimization (BPSO) is developed as an appropriate technique for feature selection. Finally, k-nearest neighbor (KNN) and support vector machine (SVM) classifiers are used for classifying features in S and SF groups. By developing the proposed method, we have archived classification accuracy of 99.3 % with respect to the proposed geometrical features. Accordingly, S and SF EEG signals have been classified. Also, Poincaré plot of SF EEG signals has more regular geometrical shapes as compared to S group. As a final remark, we notice that the Poincaré plot of coefficients in S EEG signals has occupied more space as compared to SF EEG signals (Tab. 3, Fig. 11, Ref. 57). Text in PDF www.elis.sk Keywords: EEG signal, DWT, Poincaré plot, geometrical feature, BPSO, SVM, KNN.

Pharmacologically active herbal remedies against atherosclerosis, characterization and DoE based marker quantification by densitometry, and cell based assays on THP-1 cell lines.

Atherosclerosis is a complex condition that develops at varying rates in multiple configurations and blood vessels. The primary cause of morbidity and mortality worldwide, particularly in the industrialized nations, continues to be atherosclerosis. Ayurveda, Siddha, and Unani systems of medicine, among other traditional medical systems, utilize polyherbal compositions. The treatment of atherosclerosis has been improved with a novel multibotanical combination. In this study, we sought to formulate, characterize, and standardize a polyherbal formulation based on design of experiments (DoE), densitometric studies and to predict for antioxidant activity using molecular docking analysis based on LC- MS identified phytomarkers. In addition we have assessed its cell viability by MTT assay along with Ao/EtBr staining technique and intracellular ROS assay using THP-1 cell lines. Reported findings showed that the HPTLC based quantified components of selected multiherbals has the ability to treat for atherosclerosis. This document could be used to quickly authenticate the formulation as the method optimized was based on CCD design which shows desirability of 0.962 and 0.839. Cell based assays scientifically proves that the formulation was not toxic based on MTT assay along with AO/EtBr staining technique and has excellent antioxidant activities based on intracellular ROS assay using THP-1 cell lines. The observed findings would be crucial for future clinical aspects since the bioactive molecules contained in the extracts may have anticipated effects with other compounds and show a superior therapeutic potential. As a result, this study offers standardized and potentially therapeutic information about effective polyherbal formulation for atherosclerosis.

Characterisation of pharmacokinetics, safety and tolerability in a first-in-human study for AZD8154, a novel inhaled selective PI3Kγδ dual inhibitor targeting airway inflammatory disease.

AIMS: This 3-part, randomised, phase 1 first-in-human study (NCT03436316) investigated the safety, tolerability and pharmacokinetics (PK) of AZD8154, a dual phosphoinositide 3-kinase (PI3K) γδ inhibitor developed as a novel inhaled anti-inflammatory treatment for respiratory disease. METHODS: Healthy men, and women of nonchildbearing potential, were enrolled to receive single and multiple ascending inhaled doses of AZD8154 in parts 1 and 3 of the study, respectively, while part 2 characterised the systemic PK after a single intravenous (IV) dose. In part 1, participants received 0.1-7.7 mg AZD8154 in 6 cohorts. In part 2, participants were given 0.15 mg AZD8154 as an IV infusion. In part 3, AZD8154 was given in 3 cohorts of 0.6, 1.8 and 3.1 mg, with a single dose on Day 1 followed by repeated once-daily doses on Days 4-12. RESULTS: In total, 78 volunteers were randomised. All single inhaled, single IV and multiple inhaled doses were shown to be well tolerated without any safety concerns. A population PK model, using nonlinear mixed-effect modelling, was developed to describe the PK of AZD8154. The terminal mean half-life of AZD8154 was 18.0-32.0 hours. The geometric mean of the absolute pulmonary bioavailability of AZD8154 via the inhaled route was 94.1%. CONCLUSION: AZD8154 demonstrated an acceptable safety profile, with no reports of serious adverse events and no clinically significant drug-associated safety concerns reported in healthy volunteers. AZD8154 demonstrated prolonged lung retention and a half-life supporting once-daily dosing.

Role of tourism development in environmental degradation: A step towards emission reduction.

Globally, the interaction and vulnerability of tourism and climate change have recently been in focus. This study examines how carbon dioxide emissions respond to changes in the tourism development. Panel data from 2000 to 2017 for 70 countries are analyzed using spatial econometric method to investigate the spatial spillover effect of tourism development on environmental pollution. The direct, indirect, and overall impact of tourism on environmental pollution are estimated after the selection of the most appropriate GNS method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1 % level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and carbon emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness and economic growth significantly influence on environmental pollution through spatial spill over. In addition, education expenditure and infrastructure play a significant moderating role in the relationship among tourism development and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and environmental pollution and indicate that while a country's emissions initially rise with the tourism industry's growth, they begin declining after a limit.

A basis for prenatal diagnosis of Haemophilia-A in Pakistani patients.

Objective: To screen Haemophilia-A patients for five known recurrent F8 gene variants and to analyze CA repeats in intron 13 of F8 gene in the mother and the affected child pairs. Methods: In this descriptive cross sectional study, 80 unrelated pairs of "mother and son" affected by Haemophilia-A were recruited as subjects. After collection of samples DNA was extracted using the Chelex (Bio-Rad, USA) method. Five known F8 gene variants were screened in the mothers and the affected sons by the allele specific PCR. The amplified products were separated on 2% agarose gels.CA repeats in intron 13 of the F8 gene in the mother and the affected child pairs were analyzed by Sanger sequencing method. The CA repeat alleles were used to look for the feasibility of linkage based diagnosis of Haemophilia-A in the affected families. The data were analyzed using Statistical Package for Social Sciences (SPSS) version 22.0. Results: In the 80 subject "mother and son" pairs a recurrent F8 gene variant was found in 32 pairs (40%). The most recurrent variant c.6869G>A was seen in 12 (15%). Linkage based analysis of the CA repeats in intron 13 was found to be informative in 29 (36.2%) mother-son pairs. Conclusion: The five known Haemophilia-A disease causing variants were found in 40% of the Pakistani Haemophilia-A patients. The five recurrent F8 gene variants and the CA repeats in intron 13 of F8 gene can provide a comprehensive basis for carrying out prenatal diagnosis and carrier screening in majority of the Pakistani Haemophilia-A families.

Formulation of nutritional and ready to mix gluten-free porridge from proso millet (Panicum miliaceum) little millet (Panicum sumatrense) and legume faba bean (Vicia faba).

The underutilized millets and legumes (proso millet, little millet, soaked and germinated faba beans) were mixed in different combinations to develop ready to eat porridge. Physiochemical and functional properties such as color, water absorption index (WAI), water solubility index (WSI), pasting properties, microstructure and in vitro starch digestibility of formulated porridge mix were evaluated. The porridge mix formulations exhibited a considerable amount of proteins (17.97-22.17 g/100 g) and carbohydrates (70.24-72.25 g/100 g). WAI and WSI of the different porridge formulations (P1-P6) ranged from 3.07 to 3.34 g/g and 10.37 to 25.81 g/100 g/100 g, respectively. The in vitro starch digestibility of formulation P5 (little millet, proso millet and soaked faba bean) was significantly higher (67.70%) compared to other formulations. This study concluded that nutritional, functional values as well as the digestibility of porridge mixes were improved after incorporation of soaked faba beans. The ready to eat porridge mix based on underutilized legumes and millets can serve as source of macro and micronutrients for all age groups.

Comparative study of physicochemical and functional properties of soaked, germinated and pressure cooked Faba bean.

Faba bean flours (germinated, soaked, cooked and raw) were evaluated for physiochemical and functional properties. The flour samples exhibited considerable amounts of carbohydrates (58.79-66.19 g/100 g) and proteins (21.9-29.1 g/100 g). Soaked faba bean (SFB) (29.1 g/100 g) and raw faba bean (RFB) (25.70 g/100 g) flour contained significantly higher amount of protein than germinated faba bean (GFB) and pressure cooked faba bean (PCFB). The physicochemical and functional composition of GFB and PCFB were improved compare to raw flour. Physical and functional properties such as water absorption index (2.97 g/g) and foaming stability (140.13 mL/100 mL) were increased by germination. The functional properties of pressure-cooked faba bean such as water solubility index (2.12 g/100 g) and water absorption capacity (2.02 g/g) were higher than other flour samples. The microstructure of legume flour samples explained that the starch granules of raw flours were smooth, oval and granular structure whereas soaked, germinated and cooked flours showed damaged starch granules. The effect of soaking, germination and pressure-cooking demonstrated significant variations in functionals characteristics of faba bean flour. Therefore, various processing conditions can be combined to obtain the desired characteristics in faba bean-based food products.

Inhibition of miR-188-5p alleviates hepatic fibrosis by significantly reducing the activation and proliferation of HSCs through PTEN/PI3K/AKT pathway.

Persistent hepatic damage and chronic inflammation in liver activate the quiescent hepatic stellate cells (HSCs) and cause hepatic fibrosis (HF). Several microRNAs regulate the activation and proliferation of HSCs, thereby playing a critical role in HF progression. Previous studies have reported that miR-188-5p is dysregulated during the process of HF. However, the role of miR-188-5p in HF remains unclear. This study investigated the potential role of miR-188-5p in HSCs and HF. Firstly, we validated the miR-188-5p expression in primary cells isolated from liver of carbon tetrachloride (CCl4 )-induced mice, TGF-ß1-induced LX-2 cells, livers from 6-month high-fat diet (HFD)-induced rat and 4-month HFD-induced mice NASH models, and human non-alcoholic fatty liver disease (NAFLD) patients. Furthermore, we used miR-188-5p inhibitors to investigate the therapeutic effects of miR-188-5p inhibition in the HFD + CCl4 induced in vivo model and the potential role of miR-188-5p in the activation and proliferation of HSCs. This present study reported that miR-188-5p expression is significantly increased in the human NAFLD, HSCs isolated from liver of CCl4 induced mice, and in vitro and in vivo models of HF. Mimicking the miR-188-5p resulted in the up-regulation of HSC activation and proliferation by directly targeting the phosphatase and tensin homolog (PTEN). Moreover, inhibition of miR-188-5p reduced the activation and proliferation markers of HSCs through PTEN/AKT pathway. Additionally, in vivo inhibition of miR-188-5p suppressed the HF parameters, pro-fibrotic and pro-inflammatory genes, and fibrosis. Collectively, our results uncover the pro-fibrotic role of miR-188-5p. Furthermore, we demonstrated that miR-188-5p inhibition decreases the severity of HF by reducing the activation and proliferation of HSCs through PTEN/AKT pathway.

Accrual of functional redundancy along the lifespan and its effects on cognition.

Despite the necessity to understand how the brain endures the initial stages of age-associated cognitive decline, no brain mechanism has been quantitatively specified to date. The brain may withstand the effects of cognitive aging through redundancy, a design feature in engineered and biological systems, which entails the presence of substitute elements to protect it against failure. Here, we investigated the relationship between functional network redundancy and age over the human lifespan and their interaction with cognition, analyzing resting-state functional MRI images and cognitive measures from 579 subjects. Network-wide redundancy was significantly associated with age, showing a stronger link with age than other major topological measures, presenting a pattern of accumulation followed by old-age decline. Critically, redundancy significantly mediated the association between age and executive function, with lower anti-correlation between age and cognition in subjects with high redundancy. The results suggest that functional redundancy accrues throughout the lifespan, mitigating the effects of age on cognition.

Induction of PDCD4 by albumin in proximal tubule epithelial cells potentiates proteinuria-induced dysfunctional autophagy by negatively targeting Atg5.

Autophagy dysfunction is a hallmark of type 1 diabetes. However, the precise molecular mechanism of proteinuria-induced dysfunctional autophagy remains unclear. Herein, we investigated the role of programmed cell death 4 (PDCD4) in the regulation of autophagy in the pathogenesis of diabetic kidney disease (DKD) in vivo and in vitro. RT-qPCR, immunohistochemistry (IHC), and western blotting demonstrated an upregulation of Pdcd4 mRNA and protein in streptozotocin (STZ)-induced DKD rats, as compared to the control. In addition, IHC and western blotting of a unilateral ureteral obstruction mouse model showed an upregulation of PDCD4 in the disease group, as compared to their respective controls. IHC analysis of kidney biopsy samples of human DKD patients showed an upregulation of PDCD4 compared to the control. Western blotting of the STZ-induced DKD rat tissues displayed a low microtubule-associated protein 1A/1B-light chain 3 (LC3)-II, as compared to the control. It was found that albumin overload in cultured PTECs upregulated the expression of PDCD4 and p62 and decreased the expression of LC3-II and autophagy-related 5 (Atg5) proteins. The knockout of Pdcd4 in cultured PTECs could reduce albumin-induced dysfunctional autophagy, as evidenced by the recovery of Atg5 and LC3-II protein. The forced expression of PDCD4 could further suppress the expression of the crucial autophagy-related gene Atg5. Evidence suggests that endogenous PDCD4 promotes proteinuria-induced dysfunctional autophagy by negatively regulating Atg5. Therefore, PDCD4 may be a potential therapeutic target in DKD.

Biocompatibility performance evaluation of high flux hydrophilic CO3Ap/HAP/PSF composite membranes for hemodialysis application.

Carbonate apatite/hydroxyapatite (CO3Ap/HAP) additive was obtained by calcination of wasted chicken bones at 900°C. Intermolecular attraction exists between CO3Ap/HAP additive and blended polysulfone (PSF) polymer. Electron dispersive X-ray (EDX) and FTIR analysis were carried out to check the elemental composition and bonding chemistry of prepared additive. The instantaneous demixing process generated consistent finger-like networks in CO3Ap/HAP/PSF-based composite membranes while sponge-like structure was shown by PSF as revealed by SEM images. The increase in weight % of additive loading is also confirmed by EDX analysis. Furthermore, the interaction mechanism of CO3Ap/HAP additive with polysulfone medium was analyzed by FTIR exploration. The water absorption experiment defined a 93% expansion in hydrophilic performance. Change in porosity occurs with additive loading and pure water permeation flux improved up to 11 times. Approximately, antifouling results revealed that 87% of water flux was recovered after treating with a protein solution, whereas a 30% improvement in antifouling capability in case of bovine serum albumin solution occurred. In vitro cytotoxicity, and clotting times study was carried out to evaluate virulent behavior and anticoagulation activity of formulated membranes.

Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station.

A new slot-based antenna design capable of producing horizontal polarization for unmanned aerial vehicle (UAV) ground control station (GCS) applications is outlined in this paper. The proposed antenna consists of oversize coaxial cylinders, slots, and slot-feed assembly. Each of the four vertical slots, arranged periodically around the antenna's outer cylinder, emits a horizontally polarized broad beam of radiation, in phase, to produce an omnidirectional pattern. The antenna possesses a low-ripple ±0.5 dB in azimuth gain (yaw) due to its symmetric axis shape and an enclosed feed within itself, which does not radiate and interfere with the main azimuth pattern. This is crucial for a UAV GCS to symmetrically extend its coverage range in all directions against yaw planes. Simulation and measurement results reveal that the antenna maintains stable gain in the omnidirectional pattern (+0.5 dB) over the entire operational frequency band (2.55 GHz to 2.80 GHz), where S11 is lower than -10 dB. A further advantage of this configuration is its enhanced polarization purity of -40 dB over the full frequency band. The direct-current (DC) grounding approach used in this antenna is beneficial due to its electrostatic discharge (ESD) and lightning protection. Furthermore, its aerodynamic, self-supporting, and surface-mount structural shape makes this antenna a good and worthy choice for a UAV GCS.

Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron.

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

A step toward reducing air pollution in top Asian economies: The role of green energy, eco-innovation, and environmental taxes.

Environmental degradation is significantly studied both in the past and the current literature; however, steps towards reducing the environmental pollution in carbon emission and haze pollution like PM2.5 are not under rational attention. This study tries to cover this gap while considering the carbon emission and PM2.5 through observing the role of renewable energy, non-renewable energy, environmental taxes, and ecological innovation for the top Asian economies from 1990 to 2017. For analysis purposes, this research considers cross-sectional dependence analysis, unit root test with and without structural break (Pesaran, 2007), slope heterogeneity analysis, Westerlund and Edgerton (2008) panel cointegration analysis, Banerjee and Carrion-i-Silvestre (2017) cointegration analysis, long-short run CS-ARDL results, as well as AMG and CCEMG for robustness check. The empirical evidence in both the short- and long-run has confirmed the negative and significant effect of renewable energy sources, ecological innovation, and environmental taxes on carbon emissions and PM2.5. Whereas, non-renewable energy sources are causing environmental degradation in the targeted economies. Finally, various policy implications related to carbon emission and haze pollution like PM2.5 are also provided to control their harmful effect on the natural environment.

Prevalence of fungi in fresh tomatoes and their control by chitosan and sweet orange (Citrus sinensis) peel essential oil coating.

BACKGROUND: Fungal contamination is a major cause of food spoilage. There is an urgent need to find and characterize natural preservatives. This study evaluates the prevalence of fungi in tomatoes and their control by using essential oil (EO) from sweet orange peel. Essential oils were extracted from dried and fresh sweet orange peels by using n-hexane and ethanol as extraction solvents. Fourier transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GC-MS) analyses were performed to identify the chemical composition of the EO. A combination of chitosan (CS) and EO was used to control the fungal decay of tomatoes inoculated with Aspergillus niger and Penicillium citrinum. RESULTS: Tomatoes obtained from local markets and supermarkets showed a high prevalence of Aspergillus and Penicillium spp. Essential oils extracted by ethanol from dried peels showed complete inhibition of A. niger and P. citrinum and hyphal degradation at a minimum inhibitory concentration (MIC) of 100 µL mL-1 . The combination of EO with chitosan (2%) as a coating, effectively controlled the fungal decay of tomatoes until the eighth day of storage at 25 °C. CONCLUSION: Due to their edible nature, and their antifungal and preservative potential, EO- and CS-based coatings can be used to extend the shelf life of tomatoes and other agriculture commodities. Essential oil- and CS-based coating can be used as alternative to synthetic preservatives, which are associated with various health hazards. © 2021 Society of Chemical Industry.

Entropy Generation Analysis of Hybrid Nanomaterial through Porous Space with Variable Characteristics.

Salient features of hybrid nanofluid (MoS2-SiO2/water) for Darcy-Forchheimer-Brinkman porous space with variable characteristics is examined. Heat transfer analysis subject to viscous dissipation, nonlinear thermal radiation, and heat generation/absorption is carried out. Disturbance inflow is created by an exponentially stretching curved sheet. Relevant equations are simplified by employing boundary layer theory. Adequate transformations lead to a set of dimensionless equations. Velocity, temperature, and entropy generation rate are analyzed graphically. Comparative results are obtained for hybrid (MoS2-SiO2/water) and nanofluid (MoS2-water and SiO2-water). Physical quantities are analyzed through numerical data.

Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index.

The development of fast and robust brain-computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.