Efficient detection of nitric oxide a biomarker associated with COVID19 via N, P co-doped C60 fullerene: a computational study.

CONTEXT: Coronavirus (COVID-19) is a novel respiratory viral infection, causing a relatively large number of deaths especially in people who underly lung diseases such as chronic obstructive pulmonary and asthma, and humans are still suffering from the limited testing capacity. In this article, a solution is proposed for the detection of COVID-19 viral infections through the analysis of exhaled breath gasses, i.e., nitric oxide, a prominent biomarker released by respiratory epithelial, as a non-invasive and time-saving approach. Here, we designed a novel and low-cost N and P co-doped C60 fullerene-based breathalyzer for the detection of NO gas exhaled from the respiratory epithelial cells. This breathalyzer shows a quick response to the detection of NO gas by directly converting NO to NO2 without passing any energy barrier (0 kcal/mol activation energy). The recovery time of breathalyzer is very short (0.98 × 103 s), whereas it is highly selective for NO sensing in the mixture of CO2 and H2O gasses. The study provides an idea for the synthesis of low-cost (compared to previously reported Au atom decorated nanostructure and metal-based breathalyzer), efficient, and highly selective N and P co-doped C60 fullerene-based breathalyzer for COVID-19 detection. METHODS: The geometries of N and P-doped systems and gas molecules are simulated using spin-polarized density functional theory calculations.

An intelligent diabetes classification and perception framework based on ensemble and deep learning method.

Sugar in the blood can harm individuals and their vital organs, potentially leading to blindness, renal illness, as well as kidney and heart diseases. Globally, diabetic patients face an average annual mortality rate of 38%. This study employs Chi-square, mutual information, and sequential feature selection (SFS) to choose features for training multiple classifiers. These classifiers include an artificial neural network (ANN), a random forest (RF), a gradient boosting (GB) algorithm, Tab-Net, and a support vector machine (SVM). The goal is to predict the onset of diabetes at an earlier age. The classifier, developed based on the selected features, aims to enable early diagnosis of diabetes. The PIMA and early-risk diabetes datasets serve as test subjects for the developed system. The feature selection technique is then applied to focus on the most important and relevant features for model training. The experiment findings conclude that the ANN exhibited a spectacular performance in terms of accuracy on the PIMA dataset, achieving a remarkable accuracy rate of 99.35%. The second experiment, conducted on the early diabetes risk dataset using selected features, revealed that RF achieved an accuracy of 99.36%. Based on our experimental results, it can be concluded that our suggested method significantly outperformed baseline machine learning algorithms already employed for diabetes prediction on both datasets.

Synthesis of hydrazone-based polyhydroquinoline derivatives - antibacterial activities, α-glucosidase inhibitory capability, and DFT study.

In recent years, polyhydroquinolines have gained much attention due to their widespread applications in medicine, agriculture, industry, etc. Here, we synthesized a series of novel hydrazone-based polyhydroquinoline derivatives via multi-step reactions. These molecules were characterized by modern spectroscopic techniques (1H-NMR, 13C NMR, and LC-HRMS) and their antibacterial and in vitro α-glucosidase inhibitory activities were assessed. Compound 8 was found to be the most active inhibitor against Listeria monocytogenes NCTC 5348, Bacillus subtilis IM 622, Brevibacillus brevis, and Bacillus subtilis ATCC 6337 with a zone of inhibition of 15.3 ± 0.01, 13.2 ± 0.2, 13.1 ± 0.1, and 12.6 ± 0.3 mm, respectively. Likewise, compound 8 also exhibited the most potent inhibitory potential for α-glucosidase (IC50 = 5.31 ± 0.25 µM) in vitro, followed by compounds 10 (IC50 = 6.70 ± 0.38 µM), and 12 (IC50 = 6.51 ± 0.37 µM). Furthermore, molecular docking and DFT analysis of these compounds showed good agreement with experimental work and the nonlinear optical properties calculated here indicate that these compounds are good candidates for nonlinear optics.

Genomic Exploration: Unraveling the Intricacies of Indica Rice Oryza sativa L. Germin-Like Protein Gene 12-3 (OsGLP12-3) Promoter via Cloning, Sequencing, and In Silico Analysis.

Germin and Germin-like proteins (GLPs) are a class of plant proteins that are part of the Cupins superfamily, found in several plant organs including roots, seeds, leaves, and nectar glands. They play a crucial role in plant defense against pathogens and environmental stresses. Herein, this study focused on the promoter analysis of OsGLP12-3 in rice cultivar Swat-1 to elucidate its regulation and functions. The region (1863bp) of the OsGLP12-3 promoter from Swat-1 genomic DNA was amplified, purified, quantified, and cloned using Topo cloning technology, followed by sequencing. Further in silico comparative analysis was conducted between the OsGLP12-3 promoters from Nipponbare and Swat-1 using the Plant CARE database, identifying 24 cis-acting regulatory elements with diverse functions. These elements exhibited distinct distribution patterns in the 2 rice varieties. The OsGLP12-3 promoter revealed an abundance of regulatory elements associated with biotic and abiotic stress responses. Computational tools were employed to analyze the regulatory features of this region. In silico expression analysis of OsGLP12-3, considering various developmental stages, stress conditions, hormones, and expression timing, was performed using the TENOR tool. Pairwise alignment indicated 86% sequence similarity between Nipponbare and Swat-1. Phylogenetic analysis was conducted to explore the evolutionary relationship between the OsGLP12-3 and other plant GLPs. Additionally, 2 unique regulatory elements were modeled and docked, GARE and MBS to understand their hydrogen bonding interactions in gene regulation. The study highlights the importance of OsGLP12-3 in plant defense against biotic and abiotic stresses, supported by its expression patterns in response to various stressors and the presence of specific regulatory elements within its promoter region.

Systematic surface bowing in 2D III-nitride monolayers.

In this article we report novel composite materials of bucky ball (C60 fullerene) and III-nitrides (BN, AlN, GaN, InN). The experimental feasibility of the novel composite materials is confirmed through negative binding energies and molecular dynamics simulations performed at 500 K. The structural properties of the novel composites are explored through density functional theory. An unusual phenomenon of surface bowing is observed in the 2D structure of the III-nitride monolayers due to the C60 sticking. This surface bowing systematically increases as one proceeds from BN → AlN → GaN → InN. The electron density difference and Hirshfeld charge density analysis show smaller charge transfer during the complexation, which is probably due to weak van der Waal's forces. The presence of van der Waal's forces is also confirmed by the Atom in Molecule analysis, Reduced Density Gradient Technique and Non-covalent Interaction analysis. This work provides a foundation for further theoretical and experimental studies of the novel phenomenon of systematic bowing in the 2D structure of III-nitride monolayers.

A bromide substituted 2D additive for stable and efficient perovskite photovoltaics.

In this article, a bromide substituted 2D layered perovskite having a repeated vertical orientation and coexisting with the bulk of a 3D perovskite is reported for the first time. This novel structure is obtained through controlled compositional engineering of the perovskite precursor solution. The photovoltaic performance of this novel 2D/3D perovskite was higher than that of 3D MAPbI3 and a maximum photoconversion efficiency (PCE) of 17.4% was achieved. The devices fabricated using this perovskite heterostructure were stable and retained their initial PCE up to 20 days when kept open in a laboratory environment with 40% relative humidity.

Spatial spillover impact of determinants on child mortality in Pakistan: evidence from Spatial Durbin Model.

BACKGROUND: Child mortality is a major challenge to public health in Pakistan and other developing countries. Reduction of the child mortality rate would improve public health and enhance human well-being and prosperity. This study recognizes the spatial clusters of child mortality across districts of Pakistan and identifies the direct and spatial spillover effects of determinants on the Child Mortality Rate (CMR). METHOD: Data of the multiple indicators cluster survey (MICS) conducted by the United Nations International Children's Emergency Fund (UNICEF) was used to study the CMR. We used spatial univariate autocorrelation to test the spatial dependence between contiguous districts concerning CMR. We also applied the Spatial Durbin Model (SDM) to measure the spatial spillover effects of factors on CMR. RESULTS: The study results showed 31% significant spatial association across the districts and identified a cluster of hot spots characterized by the high-high CMR in the districts of Punjab province. The empirical analysis of the SDM confirmed that the direct and spatial spillover effect of the poorest wealth quintile and MPI vulnerability on CMR is positive whereas access to postnatal care to the newly born child and improved drinking water has negatively (directly and indirectly) determined the CMR in Pakistan. CONCLUSION: The instant results concluded that spatial dependence and significant spatial spillover effects concerning CMR exist across districts. Prioritization of the hot spot districts characterized by higher CMR can significantly reduce the CMR with improvement in financial statuses of households from the poorest quintile and MPI vulnerability as well as improvement in accessibility to postnatal care services and safe drinking water.

Synthesis, molecular structure and urease inhibitory activity of novel bis-Schiff bases of benzyl phenyl ketone: A combined theoretical and experimental approach.

Background: Urease belongs to the family of amid hydrolases with two nickel atoms in their core structure. On the basis of literature survey, this research work is mainly focused on the study of bis-Schiff base derivatives of benzyl phenyl ketone nucleus. Objective: Synthesis of benzyl phenyl ketone based bis-Schiff bases in search of potent urease inhibitors. Method: In the current work, bis-Schiff bases were synthesized through two steps reaction by reacting benzyl phenyl ketone with excess of hydrazine hydrate in ethanol solvent in the first step to get the desired hydrazone. In last, different substituted aromatic aldehydes were refluxed in catalytic amount of acetic acid with the desired hydrazone to obtain bis-Schiff base derivatives in tremendous yields. Using various spectroscopic techniques including FTIR, HR-ESI-MS, and 1H NMR spectroscopy were used to clarify the structures of the created bis-Schiff base derivatives. Results: The prepared compounds were finally screened for their in-vitro urease inhibition activity. All the synthesized derivatives (3-9) showed excellent to less inhibitory activity when compared with standard thiourea (IC50 = 21.15 ± 0.32 µM). Compounds 3 (IC50 = 22.21 ± 0.42 µM), 4 (IC50 = 26.11 ± 0.22 µM) and 6 (IC50 = 28.11 ± 0.22 µM) were found the most active urease inhibitors near to standard thiourea among the synthesized series. Similarly, compound 5 having IC50 value of 34.32 ± 0.65 µM showed significant inhibitory activity against urease enzyme. Furthermore, three compounds 7, 8, and 9 exhibited less activity with IC50 values of 45.91 ± 0.14, 47.91 ± 0.14, and 48.33 ± 0.72 µM respectively. DFT used to calculate frontier molecular orbitals including; HOMO and LUMO to indicate the charge transfer from molecule to biological transfer, and MEP map to indicate the chemically reactive zone suitable for drug action. The electron localization function (ELF), non-bonding orbitals, AIM charges are also calculated. The docking study contributed to the analysis of urease protein binding.

A New Method for Determination of Optimal Borehole Drilling Location Considering Drilling Cost Minimization and Sustainable Groundwater Management.

Drilling boreholes for the exploration of groundwater incurs high cost with potential risk of failures. However, borehole drilling should only be done in regions with a high probability of faster and easier access to water-bearing strata, so that groundwater resources can be effectively managed. However, regional strati-graphic uncertainties drive the decision of the optimal drilling location search. Unfortunately, due to the unavailability of a robust solution, most contemporary solutions rely on physical testing methods that are resource intensive. In this regard, a pilot study is conducted to determine the optimal borehole drilling location using a predictive optimization technique that takes strati-graphic uncertainties into account. The study is conducted in a localized region of the Republic of Korea using a real borehole data set. In this study we proposed an enhanced Firefly optimization algorithm based on an inertia weight approach to find an optimal location. The results of the classification and prediction model serve as an input to the optimization model to implement a well-crafted objective function. For predictive modeling a deep learning based chained multioutput prediction model is developed to predict groundwater-level and drilling depth. For classification of soil color and land-layer a weighted voting ensemble classification model based on Support Vector Machines, Gaussian Naïve Bayes, Random Forest, and Gradient Boosted Machine is developed. For weighted voting, an optimal set of weights is determined using a novel hybrid optimization algorithm. Experimental results validate the effectiveness of the proposed strategy. The proposed classification model achieved an accuracy of 93.45% and 95.34% for soil-color and land-layer, respectively. While the mean absolute error achieved by proposed prediction model for groundwater level and drilling depth is 2.89% and 3.11%, respectively. It is found that the proposed predictive optimization framework can adaptively determine the optimal borehole drilling locations for high strati-graphic uncertainty regions. The findings of the proposed study provide an opportunity to the drilling industry and groundwater boards to achieve sustainable resource management and optimal drilling performance.

Anodic SnO2 Nanoporous Structure Decorated with Cu2O Nanoparticles for Sensitive Detection of Creatinine: Experimental and DFT Study.

Advanced anodic SnO2 nanoporous structures decorated with Cu2O nanoparticles (NPs) were employed for creatinine detection. Anodization of electropolished Sn sheets in 0.3 M aqueous oxalic acid electrolyte under continuous stirring produced complete open top, crack-free, and smooth SnO2 nanoporous structures. Structural analyses confirm the high purity of rutile SnO2 with successful functionalization of Cu2O NPs. Morphological studies revealed the formation of self-organized and highly-ordered SnO2 nanopores, homogeneously decorated with Cu2O NPs. The average diameter of nanopores is ∼35 nm, while the average Cu2O particle size is ∼23 nm. Density functional theory results showed that SnO2@Cu2O hybrid nanostructures are energetically favorable for creatinine detection. The hybrid nanostructure electrode exhibited an ultra-high sensitivity of around 24343 µA mM-1 cm-2 with an extremely lower detection limit of ∼0.0023 µM, a fast response time (less than 2 s), and wide linear detection ranges of 2.5-45 µM and 100 µM to 15 mM toward creatinine. This is ascribed to the creation of highly active surface sites as a result of Cu2O NP functionalization, SnO2 band gap diminution, and the formation of heterojunction and Cu(1)/Cu(ll)-creatinine complexes through secondary amines which occur in the creatinine structure. The real-time analysis of creatinine in blood serum by the fabricated electrode evinces the practicability and accuracy of the biosensor with reference to the commercially existing creatinine sensor. The proposed biosensor demonstrated excellent stability, reproducibility, and selectivity, which reflects that the SnO2@Cu2O nanostructure is a promising candidate for the non-enzymatic detection of creatinine.

Instability analysis of spin-electron-acoustic waves and the appearance of separated spin electron cyclotron mode in spin-polarized magnetized quantum plasma.

Linear and nonlinear characteristics of electrostatic waves are studied in a magnetized plasma consisting of spin-up (n_{↑}) and spin-down (n_{↓}) state populations with uniformly distributed static ions in the background. The linear analysis shows the existence of four modes. One of these modes, termed the separated spin electron cyclotron wave, is found to be due to the separated spin populations. The Zakharov-Kuznetsov equation is derived by the reductive perturbation technique. The instability growth rate γ is obtained from the same equation. It is observed that the magnetized spin quantum plasma admits rarefactive soliton with constant amplitude but increasing width with the increasing strength of the applied magnetic field. It has also been observed that the amplitude of soliton decreases and its width increases with the increasing values of polarization ratio κ. The unstable region expands with the increase in polarization ratio and contracts with the increased plasma number density and magnetic-field strength. The (growth rate) γ of instability reduces by increasing the κ and is increasing when the density of the plasma and the strength of the magnetic field increasing. The model developed in this work finds its scope in studying degenerate electron gas and astrophysical systems such as pulsar magnetosphere and neutron stars.

An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI) serves as a non-invasive tool to detect the presence of a tumor. However, Rician noise is inevitably instilled during the image acquisition process, which leads to poor observation and interferes with the treatment. Computer-Aided Diagnosis (CAD) systems can perform early diagnosis of the disease, potentially increasing the chances of survival, and lessening the need for an expert to analyze the MRIs. Convolutional Neural Networks (CNN) have proven to be very effective in tumor detection in brain MRIs. There have been multiple studies dedicated to brain tumor classification; however, these techniques lack the evaluation of the impact of the Rician noise on state-of-the-art deep learning techniques and the consideration of the scaling impact on the performance of the deep learning as the size and location of tumors vary from image to image with irregular shape and boundaries. Moreover, transfer learning-based pre-trained models such as AlexNet and ResNet have been used for brain tumor detection. However, these architectures have many trainable parameters and hence have a high computational cost. This study proposes a two-fold solution: (a) Multi-Scale CNN (MSCNN) architecture to develop a robust classification model for brain tumor diagnosis, and (b) minimizing the impact of Rician noise on the performance of the MSCNN. The proposed model is a multi-class classification solution that classifies MRIs into glioma, meningioma, pituitary, and non-tumor. The core objective is to develop a robust model for enhancing the performance of the existing tumor detection systems in terms of accuracy and efficiency. Furthermore, MRIs are denoised using a Fuzzy Similarity-based Non-Local Means (FSNLM) filter to improve the classification results. Different evaluation metrics are employed, such as accuracy, precision, recall, specificity, and F1-score, to evaluate and compare the performance of the proposed multi-scale CNN and other state-of-the-art techniques, such as AlexNet and ResNet. In addition, trainable and non-trainable parameters of the proposed model and the existing techniques are also compared to evaluate the computational efficiency. The experimental results show that the proposed multi-scale CNN model outperforms AlexNet and ResNet in terms of accuracy and efficiency at a lower computational cost. Based on experimental results, it is found that our proposed MCNN2 achieved accuracy and F1-score of 91.2% and 91%, respectively, which is significantly higher than the existing AlexNet and ResNet techniques. Moreover, our findings suggest that the proposed model is more effective and efficient in facilitating clinical research and practice for MRI classification.

Silicon-doped boron nitride graphyne-like sheet for catalytic N2O reduction: A DFT study.

In this work, spin-polarized density functional theory calculations are conducted to evaluate the possible applicability of a single Si atom doped boron nitride graphyne-like nansoheet (Si@BN-yne) for reduction of nitrous oxide (N2O). The calculations show that Si-doping in BN graphene is energetically favorable, and the resulting Si@BN-yne is both dynamically and thermodynamically stable. According to our findings, N2O spontaneously dissociates when it interacts with the Si@BN-yne from its O site without the need for an energy barrier, releasing 2.89 eV of energy. The adsorption energy of CO molecule on the Si@BN-yne is less negative than that of N2O, implying that N2O will predominately occupy the catalyst surface. The CO + Oad reaction is used to remove the remaining oxygen atom (Oad) from the Si@BN-yne surface. The calculations show that the reaction proceeds through a low energy barrier of 0.05 eV, which is much lower than the previously reported catalysts. This demonstrates the high catalytic activity of Si@BN-yne nanosheet. Furthermore, the adsorption of H2O and O2 species on the Si@BN-yne nanosheet is investigated. The results show that the presence of these species has no effect on the catalytic activity of the Si@BN-yne for N2O reduction. These results show that the proposed novel Si@BN-yne catalyst can be regarded as an efficient material in the development of promising active catalysts for N2O elimination from the environment.

Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights.

Autonomous flights are the major industry contributors towards next-generation developments in pervasive and ubiquitous computing. Modern aerial vehicles are designed to receive actuator commands from the primary autopilot software as input to regulate their servos for adjusting control surfaces. Due to real-time interaction with the actual physical environment, there exists a high risk of control surface failures for engine, rudder, elevators, and ailerons etc. If not anticipated and then timely controlled, failures occurring during the flight can have severe and cataclysmic consequences, which may result in mid-air collision or ultimate crash. Humongous amount of sensory data being generated throughout mission-critical flights, makes it an ideal candidate for applying advanced data-driven machine learning techniques to identify intelligent insights related to failures for instant recovery from emergencies. In this paper, we present a novel framework based on machine learning techniques for failure prediction, detection, and classification for autonomous aerial vehicles. The proposed framework utilizes long short-term memory recurrent neural network architecture to analyze time series data and has been applied at the AirLab Failure and Anomaly flight dataset, which is a comprehensive publicly available dataset of various fault types in fixed-wing autonomous aerial vehicles' control surfaces. The proposed framework is able to predict failure with an average accuracy of 93% and the average time-to-predict a failure is 19 s before the actual occurrence of the failure, which is 10 s better than current state-of-the-art. Failure detection accuracy is 100% and average detection time is 0.74 s after happening of failure, which is 1.28 s better than current state-of-the-art. Failure classification accuracy of proposed framework is 100%. The performance analysis shows the strength of the proposed methodology to be used as a real-time failure prediction and a pseudo-real-time failure detection along with a failure classification framework for eventual deployment with actual mission-critical autonomous flights.

Modification of sludge-based biochar using air roasting-oxidation and its performance in adsorption of uranium(VI) from aqueous solutions.

Modification methods for sludge-based biochar are often complex and generally ineffective. In this study, sludge-based biochars were prepared at low cost using a simple air roasting-oxidation modification method and the adsorption performance on U(VI) was investigated. Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) results together indicated that more carbon-oxygen functional groups were formed on the surface of oxidized biochar (OBC) compared to unoxidized biochar (BC). The adsorption performance of 550-OBC (biochar oxidized at 550 °C) on U(VI) was explored in batch experiments. The maximum adsorption capacity was up to 490.2 mg/g at 25 °C and pH 6, exceeding most of the reported biochars. 550-OBC also showed good adsorption performance at low U(VI) concentration, with 96% removal at pH 6 and an initial U(VI) concentration of 1 mg/L. Density functional theory (DFT) calculations indicated that the H-bond length between the solvated U(VI) and functional groups on the OBC was about 1.7 Å, which forms stronger H-bonds between them compared to that between U(VI) and BC (4.21 Å), and the adsorption energy value for this complex was highly negative -31.82 kcal/mol. In addition, 550-OBC exhibited high selectivity for U(VI) adsorption and excellent regeneration performance, making it a cost-effective and high-performance adsorbent.

Impact of credit, liquidity, and systematic risk on financial structure: comparative investigation from sustainable production.

The role of risk assessment and capital structure is vital for the sustainable growth of firms and increasing the shareholders' wealth. This research explores the correlation between firm risk and capital structure using datasets from the sugar and cement sectors of Pakistan as a developing economy. This study is unique as it involved two firms of different nature (sugar firms operate seasonally while cement firms operate yearly) to view the real picture on the impact of risk and structure assessment on firms' credibility and shareholders' wealth. For this purpose, 15-year data (2000-2014) containing the financial statements of the target sectors were collected and the ANOVA analysis was applied with credit risk, liquidity risk, systematic risk, and firm size were used as the regressor variables, firm growth and dividend payout ratio as the control variables, and leverage as the regression variable. The findings showed that credit risk and liquidity risk are significantly correlated with leverage. This suggests that decision-makers pertaining to firms' risk and efficiency must focus more on risk to pursue a stronger and sustainable increase in shareholder wealth.

Nano-porous C4N as a toxic pesticide's scavenger: A quantum chemical approach.

The sensing affinity of C4N is the most fascinating topic of research due to its excellent chemical and electronic properties. Moreover, owing to the highly active porous cavity, C4N can easily accommodate foreign molecules. Herein, we studied the adsorption properties of carbamate insecticides (CMs) namely, Dimetalin (DMT), Carbanolate (CBT), Isolan (ISO) and Propoxur (PRO) using density functional theory calculations. All the results are calculated at widely accepted ωB97XD functional along with 6-31G(d, p) basis set. The calculated counterpoise corrected interaction energy of the reported complexes ranges between -20.05 and -27.04 kcal/mol, however, the interaction distances are found to be higher than 2.00 Å. The values of interacting parameters depict that the carbamate molecules are physisorbed via noncovalent interactions that can easily be reversible. Moreover, the binding of selected insecticides notably changes the electronic structure of C4N. The electronic changes are characterized by the energies of HOMO & LUMO, their energy gaps and CHELPG charge transfer. The charge density difference between C4N surface and carbamate pesticides are characterized by EDD and CDA analysis. Moreover, the ab initio molecular dynamic study reveals that the complexes are stable even at 500 K. The photochemical sensing properties of C4N are estimated by time dependent UV-Vis calculations. The high sensitivity of C4N towards considered analytes enable it to act as a promising sensor for toxic pesticides.

Physiological, Biochemical and Defense System Responses of Roadside Vegetation to Auto-Exhaust Pollution.

This study determined the effects of traffic pollutants on plants (Nerium oleander and Ricinus communis) growing along Faisalabad to Okara (R-1) and Okara to Lahore (R-2) roads in Pakistan. The photosynthetic pigments, photosynthetic rate, transpiration rate and total soluble proteins of roadside vegetation were significantly lower than control plants (50 m away from road). The average decrease in photosynthetic rate of Nerium oleander and Ricinus communis was 33.90% and 27.94% along R-1 and 41.85% and 32.409% along R-2 road, respectively. The decreased photosynthesis in roadside flora resulted in higher water use efficiency and substomatal CO2 concentration. However, higher antioxidant activity and free amino acid contents were noted in roadside plants that might be due to their defensive response to traffic pollutants. N. oleander was more affected by traffic pollutants and R. communis showed more resistance. Thus, N. oleander could be used for biomonitoring and R. communis for phytoremediation of vehicular pollution.

Establishing growth charts for proposed body shape and size index of the Pakistani population, using quantile regression approach.

BACKGROUND: Obesity leads to other fatal diseases like diabetes, cardiovascular diseases, depression, and some forms of cancer. Still, the well-known tool to measure obesity is the body mass index. But it usually failed in the measurement of adipose tissues. So, we present a novel anthropometric measure, called body shape and size index which is developed by the combination of major anthropometric determinants: body surface area, body mass index, weight, and height. METHODS: This study is based on cross-sectional data consisting of 7224 individuals that were taken from the city Multan, Punjab, Pakistan. All the individuals, both males, and females, of age 2 years and above were included in the study except the pregnant women. The variables included in this study are gender, area (urban and rural), age (years), weight (kg), and height (meters). Growth charts of quantile regression are used for the inferential analysis of data. Comparison of proposed body shape and size index at different obesity levels has also been made to access the relationship of proposed body shape and size index with obesity. RESULTS: The results show that the proposed body shape and size index has a great association with body surface area, body mass index, weight, height, and age. The proposed body shape and size index has a high negative association with body surface area, moderate negative association with body mass index and weight, and low negative association with height and age. According to growth charts of body shape and size index, after the age of 25 years, body shape and size index curves go upward while it smoothly goes downward at the age of 50 years but decreases in earlier ages. Body shape and size index showed a significant association with body shape and body size (body development) at the same time. CONCLUSION: Body shape and size index is found, generally linear with age, and increased with decreasing body mass index and body surface area. The proposed index has an indirect relationship with obesity. Body shape and size index with low values indicates a high risk of obesity. While, however, body shape and size index with high values indicates a low risk of obesity. Applications of the proposed body shape and size index are also presented in statistical modeling.

A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring.

Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient's vital signs data in the IoT-based e-Health domain.