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In this investigation, the practicability of utilizing 3-aminopropyl triethoxysilane (3-APTES) crosslinked chitosan (Ch)/graphene oxide (GO) membranes were explored for adsorptive removal of anionic dyes from aqueous medium. Membranes were successfully fabricated through solution casting technique. Strong interactions amongst matrix (chitosan), 3-APTES, polyvinylpyrrolidone (PVP) and GO were confirmed by Infrared spectroscopy. Thermal stability of the chitosan was improved by adding graphene oxide and results were verified via thermogravimetric (TGA) analysis. Swelling and hydrolytic results confirmed that 2 %-Ch/PVP was a stable membrane while increasing the amount of 3-APTES in the chitosan nanocomposites membrane decreased its stability in aqueous medium. The adsorption characteristics of the membranes were evaluated by the adsorption of Congo red (CR) dye from aqueous medium. The adsorbent can remove 80% of CR from aqueous medium and follows second order kinetics. This study outlines the possibility of exploring green membranes which can be easily fit in various flow systems.

Nanocrystalline copper ferrite shows distinct photocatalytic properties, but it suffers from a high recombination rate of photogenerated electrons (e−) and holes (h+) due to its narrow bandgap. Herein, Al3+ doping is shown to reduce the (e−/h+) recombination rate and improve the charge carriers’ availability in doped CuFe2−xAlxO4 (0 ≤ x ≤ 1) nanoparticles produced by a solid-state, mechanochemical process. CuFe2−xAlxO4 (0 ≤ x ≤ 1) nanoparticles exhibit the growth of a nanocrystalline cubic spinel lattice when annealed at 1000 °C. The lattice parameter is reduced by Al3+ doping due to the smaller ionic radius of Al3+ ions substituting bigger Fe3+ ions. However, a higher degree of sintering and greater crystallite size are observed for Al3+ doped samples. The surface morphology and topography also reveal an increase in the particle size, but significantly narrow size distribution and greater homogeneity. The effect of Al3+ doping on the optical properties of CuFe2−xAlxO4 (0 ≤ x ≤ 1) nanoparticles is demonstrated by a decrease in the photoluminescence signal that is attributed to the lower rate of (e−/h+) recombination. Thus, Al3+ doping increases transition time and improves the availability of charge carriers for potential photocatalytic applications.

Antioxidant proteins have been discovered closely associated with disease control due to its capability to eradicate excess free radicals. The accurate identification of antioxidant proteins is on the upsurge owing to their therapeutic significance. However, observing the rapid increases of this toxic disease in the human body, several machine learning algorithms have been applied and performed inadequately to identify antioxidant proteins. Therefore, measuring the effectiveness of antioxidant proteins on the human body, a reliable intelligent model is indispensable for the researchers. In this study, primary protein sequences are formulated using evolutionary and sequence-based numerical descriptors. Whereas, evolutionary features are collected using a bigram Position-specific scoring matrix, besides, K-space amino acid pair (KSAAP) and dipeptide composition are utilized to extract sequential information. Furthermore, in order to reduce the computational time and to eradicate irreverent and noisy features, the Sequential forward selection and Support vector machine (SFS-SVM) based ensemble approach is applied to select optimal features. At last, several distinct nature classification learning methods are applied to choose a suitable operational engine for our model. After evaluating the empirical results, SVM using optimal features achieved an accuracy of 97.54%, 93.71% using the training and independent dataset, respectively. It was found that our proposed model outperformed and reported the highest performance than the existing computational models. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. The source code and all datasets are publicly available at https://github.com/salman-khan-mrd/Antioxident_proteins .

Alzheimer’s disease (AD) is a severe neurodegenerative disease. The identification of patients at high risk of conversion from mild cognitive impairment to AD via earlier close monitoring, targeted investigations, and appropriate management is crucial. Recently, several machine learning (ML) algorithms have been used for AD progression detection. Most of these studies only utilized neuroimaging data from baseline visits. However, AD is a complex chronic disease, and usually, a medical expert will analyze the patient’s whole history when making a progression diagnosis. Furthermore, neuroimaging data are always either limited or not available, especially in developing countries, due to their cost. In this paper, we compare the performance of five widely used ML algorithms, namely, the support vector machine, random forest, k-nearest neighbor, logistic regression, and decision tree to predict AD progression with a prediction horizon of 2.5 years. We use 1029 subjects from the Alzheimer’s disease neuroimaging initiative (ADNI) database. In contrast to previous literature, our models are optimized using a collection of cost-effective time-series features including patient’s comorbidities, cognitive scores, medication history, and demographics. Medication and comorbidity text data are semantically prepared. Drug terms are collected and cleaned before encoding using the therapeutic chemical classification (ATC) ontology, and then semantically aggregated to the appropriate level of granularity using ATC to ensure a less sparse dataset. Our experiments assert that the early fusion of comorbidity and medication features with other features reveals significant predictive power with all models. The random forest model achieves the most accurate performance compared to other models. This study is the first of its kind to investigate the role of such multimodal time-series data on AD prediction.

Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protege Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions.

The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods.

Three different types (blank, annealed, and functionalized) of copper ferrite nanoparticles (CuFe

Inorganic nanoparticles (NPs) have a tunable shape, size, surface morphology, and unique physical properties like catalytic, magnetic, electronic, and optical capabilities. Unlike inorganic nanomaterials, organic polymers exhibit excellent stability, biocompatibility, and processability with a tailored response to external stimuli, including pH, heat, light, and degradation properties. Nano-sized assemblies derived from inorganic and polymeric NPs are combined in a functionalized composite form to import high strength and synergistically promising features not reflected in their part as a single constituent. These new properties of polymer/inorganic functionalized materials have led to emerging applications in a variety of fields, such as environmental remediation, drug delivery, and imaging. This review spotlights recent advances in the design and construction of polymer/inorganic functionalized materials with improved attributes compared to single inorganic and polymeric materials for environmental sustainability. Following an introduction, a comprehensive review of the design and potential applications of polymer/inorganic materials for removing organic pollutants and heavy metals from wastewater is presented. We have offered valuable suggestions for piloting, and scaling-up polymer functionalized nanomaterials using simple concepts. This review is wrapped up with a discussion of perspectives on future research in the field.

Robust and rabid mortality prediction is crucial in intensive care units because it is considered one of the critical steps for treating patients with serious conditions. Combining mortality prediction with the length of stay (LoS) prediction adds another level of importance to these models. No studies in the literature predict such tasks for neonates, especially using time-series data and dynamic ensemble techniques. Dynamic ensembles are novel techniques that dynamically select the base classifiers for each new case. Medically, implementing an accurate machine learning model is insufficient to gain the trust of physicians. The model must be able to justify its decisions. While explainable AI (XAI) techniques can be used to handle this challenge, no studies have been done in this regard for neonate monitoring in the neonatal intensive care unit (NICU). This study utilizes advanced machine learning approaches to predict mortality and LoS through data-driven learning. We propose a multilayer dynamic ensemble-based model to predict mortality as a classification task and LoS as a regression task for neonates admitted to the NICU. The model has been built based on the patient's time-series data of the first 24 h in the NICU. We utilized a cohort of 3,133 infants from the MIMIC-III real dataset to build and optimize the selected algorithms. It has shown that the dynamic ensemble models achieved better results than other classifiers, and static ensemble regressors achieved better results than classical machine learning regressors. The proposed optimized model is supported by three well-known explainability techniques of SHAP, decision tree visualization, and rule-based system. To provide online assistance to physicians in monitoring and managing neonates in the NICU, we implemented a web-based clinical decision support system based on the most accurate models and selected XAI techniques. The code of the proposed models is publicly available at https://github.com/InfoLab-SKKU/neonateMortalityPrediction.

Due to the environmental and production problems of emulsion, it is important to efficiently separate oil-water emulsion to meet the refinery requirement and clean up oil spills. Synthesis of a universal demulsifier is not an easy task because the physical properties of crude oil vary, which makes its characterization and demulsification procedure difficult. To overcome this problem, hydrophilic and magnetically recoverable poly (methyl methacrylate-acrylic acid)/iron oxide magnetic composite nanoparticles ((P(MMA-AA)/Fe

Social networks play a key role in providing a new approach to collecting information regarding mobility and transportation services. To study this information, sentiment analysis can make decent observations to support intelligent transportation systems (ITSs) in examining traffic control and management systems. However, sentiment analysis faces technical challenges: extracting meaningful information from social network platforms, and the transformation of extracted data into valuable information. In addition, accurate topic modeling and document representation are other challenging tasks in sentiment analysis. We propose an ontology and latent Dirichlet allocation (OLDA)-based topic modeling and word embedding approach for sentiment classification. The proposed system retrieves transportation content from social networks, removes irrelevant content to extract meaningful information, and generates topics and features from extracted data using OLDA. It also represents documents using word embedding techniques, and then employs lexicon-based approaches to enhance the accuracy of the word embedding model. The proposed ontology and the intelligent model are developed using Web Ontology Language and Java, respectively. Machine learning classifiers are used to evaluate the proposed word embedding system. The method achieves accuracy of 93%, which shows that the proposed approach is effective for sentiment classification.

Meiotic recombination plays an important role in the process of genetic diversity generation. “Hotspots” are regions that show a higher rate of meiotic recombination, while the regions with a lower rate are called “cold spots”. It has a great effect on the genome evolution via gene conversion or mutagenesis. According to recent research, recombination is present in uneven distribution across the genome. Many computational methods have been developed using secondary sequence information or physiochemical properties of nucleotide descriptor for the prediction of hotspots and cold spots, which are computationally cheap and fast in performance rather than web-lab experiments, but the correlations between nucleotides pairs at different positions along DNA sequence is often ignored, which conceal a very important predictive information. In this study, we have proposed a deep neural network to predict recombination spots by fusing both the secondary sequence information and physio-chemical derived features. Our deep learning algorithm leverage's deep dense architecture by showing its effectiveness over the state-of-the-art methods with a classification accuracy of 90.04%, sensitivity of 92.21%, specificity of 92.11% and area under the curve of 0.9801. Moreover, it is anticipated, that our model will provide novel insight into basic research, drug designing, academic research and recombination spots studies particularly. All the methodology and python-based source code is publicly available for the users at https://github.com/zaheerkhancs/irSpot_SPI along with publicly accessible web server using the proposed predictor.

This present study aimed to determine the efficacy of supplementing layer diets with vitamin A (0, 8,000 and 16,000 IU/kg diet) and vitamin E (0, 250 and 500 mg/kg diet) either individually or in combination on egg production and quality, and blood hematology and chemistry of birds reared under summer conditions. A total of 135 Bovans Brown laying hens were distributed to 9 treatment groups with 5 replicates of 3 hens/pen in a 3 × 3 factorial design. A significant improvement in feed conversion ratio (FCR) was observed as supplementary vitamin A or E increased (P ≤ 0.01). Hens fed diets supplemented with 16,000 IU vitamin A plus 500 mg vitamin E/kg diet had the best FCR among all groups. Egg quality traits were not significantly affected by the interaction of vitamin A and vitamin E levels. There was a significant increase in monocytes (P ≤ 0.01) and a decrease in basophils counts (P ≤ 0.05) in response to vitamin E. Significant decreases were observed in packed cell volume (PCV), thyroxine (T4), alanine transferase (ALT), albumin, total cholesterol and total lipids ( (P ≤ 0.05 or P ≤ 0.01) P ≤ 0.01) , and increases were observed in serum concentrations of globulin (P ≤ 0.05) and calcium (P ≤ 0.01) due to vitamin A. The combination of 0 IU vitamin A and 500 mg vitamin E/kg diet had the highest values of PCV (40.09%) and hemoglobin (Hb) (10.33 mg/100 mL) among all groups. Vitamin E raised serum values of total protein, total cholesterol and total lipids (P ≤ 0.05 or P ≤ 0.01). Feed intake, FCR, PCV, Hb, lymphocytes, monocytes, eosinophils, T4, ALT and total protein were significantly affected by the interaction of vitamins A and E (P ≤ 0.05 or P ≤ 0.01). The interaction of vitamins A and E was only significant with respect to serum total protein (P ≤ 0.05). It can be concluded that layer diets supplemented with vitamins A and E had good results in alleviating the harmful impacts of high ambient temperature. The combination of 16,000 IU vitamin A and 500 mg vitamin E per kilogram diet is preferable for obtaining better production of laying hens reared under hot summer conditions. Keywords: Vitamin A, Vitamin E, Layer, Egg quality, Blood hematology, Serum metabolites

Summary The c-Src oncogene is anchored to the cytoplasmic membrane through its N-terminal myristoylated SH4 domain. This domain is part of an intramolecular fuzzy complex with the SH3 and Unique domains. Here we show that the N-terminal myristoyl group binds to the SH3 domain in the proximity of the RT loop, when Src is not anchored to a lipid membrane. Residues in the so-called Unique Lipid Binding Region modulate this interaction. In the presence of lipids, the myristoyl group is released from the SH3 domain and inserts into the lipid membrane. The fuzzy complex with the SH4 and Unique domains is retained in the membrane-bound form, placing the SH3 domain close to the membrane surface and restricting its orientation. The apparent affinity of myristoylated proteins containing the SH4, Unique, and SH3 domains is modulated by these intramolecular interactions, suggesting a mechanism linking c-Src activation and membrane anchoring., Graphical Abstract, Highlights • The myristoyl group of c-Src interacts with the SH3 domain • The Unique Lipid Binding Region modulates myristoyl-related interactions • Mutations in the Unique and SH3 domains affect membrane anchoring • A fuzzy intramolecular complex is retained in the lipid-anchored form of USH3 Src, Structural Biology; Protein Structure Aspects; Biophysics

DNA-protein interaction is a critical biological process that performs influential activities, including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely associated with different kinds of human diseases (asthma, cancer, and AIDS), while some of the DBPs are used in the production of antibiotics, steroids, and anti-inflammatories. Several methods have been reported for the prediction of DBPs. However, a more intelligent method is still highly desirable for the accurate prediction of DBPs. This study presents an intelligent computational method, Target-DBPPred, to improve DBPs prediction. Important features from primary protein sequences are investigated via a novel feature descriptor, called EDF-PSSM-DWT (Evolutionary difference formula position-specific scoring matrix-discrete wavelet transform) and several other multi-evolutionary methods, including F-PSSM (Filtered position-specific scoring matrix), EDF-PSSM (Evolutionary difference formula position-specific scoring matrix), PSSM-DPC (Position-specific scoring matrix-dipeptide composition), and Lead-BiPSSM (Lead-bigram-position specific scoring matrix) to encapsulate diverse multivariate features. The best feature set from the features of each descriptor is selected using sequential forward selection (SFS). Further, four models are trained using Adaboost, XGB (eXtreme gradient boosting), ERT (extremely randomized trees), and LiXGB (Light eXtreme gradient boosting) classifiers. LiXGB, with the best feature set of EDF-PSSM-DWT, has attained 6.69% and 15.07% higher performance in terms of accuracies using training and testing datasets, respectively. The obtained results verify the improved performance of our proposed predictor over the existing predictors.

Stimuli-responsive proteins have gained a wide spread interest as functional biomaterials in drug delivery area. However, particular attention has been diverted to peptide-based materials due to their adaptability and reliable practical utility in biomedical applications under a range of physiological situations. Peptides exhibit a high degree of chemical and biophysical versatility, biocompatibility, and biodegradability compared to other responsive systems. Several peptide motifs have been identified to undergo significant changes in conformation as a result of their response to both internal and external stimuli including light, pH, temperature, enzymes, ionic species and redox reactions. Understanding the structure and functions of peptides both at micro and macro levels is necessary before they can be used as nano devices, as biosensors, in regenerative tissue engineering and in drug delivery. The present review provides an overview of the peptide-based stimuli responsive materials, their design and discussion of their potential setbacks and opportunities for further exploration in drug delivery applications. The design of peptide-based materials of desired architectures, their properties and potential applications will be discussed covering the literature of the past decade, with a focus on their responsiveness to external and internal stimuli.

Current study is based on the sequential conversion of indolyl butanoic acid (1) into ethyl indolyl butanoate (2), indolyl butanohydrazide (3), and 1,3,4-oxadiazole-2-thiol analogs (4) by adopting chemical transformations. In a parallel series of reactions, 2-bromo-N-phenyl/arylacetamides (7a-l) were synthesized by reacting different amines derivatives (5a-l) with 2-bromoacetyl bromide (6) to serve as electrophile. Then, the synthesized electrophiles (7a-l) were treated with nucleophilic 1,3,4-oxadiazole-2-thiol analog (4) to afford a range of N-substituted derivatives (8a-l). The structural confirmation of all the synthetic compounds was carried out by IR, 1H-, 13C NMR, EI-MS, and CHN analysis data. All synthesized molecules (8a-l) were tested for their antidiabetic potential via inhibition of the α-glucosidase enzyme followed by their in silico study. Their cytotoxicity profile was also ascertained via hemolytic activity and all of them possessed very low cytotoxicity. Compounds 8h and 8l were found most active having IC50 values 9.46 ± 0.03 µM and 9.37 ± 0.03 µM, respectively. However, all other molecules also exhibited good to moderate inhibition potential with IC50 values between 12.68 ± 0.04–37.82 ± 0.07, compared to standard acarbose (IC50 = 37.38 ± 0.12 µM), hence can be used as lead molecules for further research in order to get better antidiabetic agents.

Novel derivatives of flurbiprofen 1–18 including flurbiprofen hydrazide 1, substituted aroyl hydrazides 2–9, 2-mercapto oxadiazole derivative 10, phenacyl substituted 2-mercapto oxadiazole derivatives 11–15, and benzyl substituted 2-mercapto oxadiazole derivatives 16–18 were synthesized and characterized by EI-MS, 1H and 13C NMR spectroscopic techniques. All derivatives 1–18 were screened for α-amylase inhibitory activity and demonstrated a varying degree of potential ranging from IC50 = 1.04 ± 0.3 to 2.41 ± 0.09 µM as compared to the standard acarbose (IC50 = 0.9 ± 0.04 µM). Out of eighteen compounds, derivatives 2 (IC50 = 1.69 ± 0.1 µM), 3 (IC50 = 1.04 ± 0.3 µM), 9 (IC50 = 1.25 ± 1.05 µM), and 13 (IC50 = 1.6 ± 0.18 µM) found to be excellent inhibitors while rest of the compounds demonstrated comparable inhibition potential. A limited structure-activity relationship (SAR) was established by looking at the varying structural features of the library. In addition to that, in silico study was conducted to understand the binding interactions of the compounds (ligands) with the active site of α-amylase enzyme.

AhyI is homologous to the protein LuxI and is conserved throughout bacterial species including Aeromonas hydrophila. A. hydrophila causes opportunistic infections in fish and other aquatic organisms. Furthermore, this pathogennot only poses a great risk for the aquaculture industry, but also for human public health. AhyI (expressing acylhomoserine lactone) is responsible for the biosynthesis of autoinducer-1 (AI-1), commonly referred to as a quorum sensing (QS) signaling molecule, which plays an essential role in bacterial communication. Studying protein structure is essential for understanding molecular mechanisms of pathogenicity in microbes. Here, we have deduced a predicted structure of AhyI protein and characterized its function using in silico methods to aid the development of new treatments for controlling A.hydrophila infections. In addition to modeling AhyI, an appropriate inhibitor molecule was identified via high throughput virtual screening (HTVS) using mcule drug-like databases.The AhyI-inhibitor N-cis-octadec-9Z-enoyl-l-Homoserine lactone was selected withthe best drug score. In order to understand the pocket sites (ligand binding sites) and their interaction with the selected inhibitor, docking (predicted protein binding complex) servers were used and the selected ligand was docked with the predicted AhyI protein model. Remarkably, N-cis-octadec-9Z-enoyl-l-Homoserine lactone established interfaces with the protein via16 residues (V24, R27, F28, R31, W34, V36, D45, M77, F82, T101, R102, L103, 104, V143, S145, and V168), which are involved with regulating mechanisms of inhibition. These proposed predictions suggest that this inhibitor molecule may be used as a novel drug candidate for the inhibition of auto-inducer-1 (AI-1) activity.The N-cis-octadec-9Z-enoyl-l-Homoserine lactone inhibitor molecule was studied on cultured bacteria to validate its potency against AI-1 production. At a concentration of 40 μM, optimal inhibition efficiency of AI-1 was observedin bacterial culture media.These results suggest that the inhibitor molecule N-cis-octadec-9Z-enoyl-l-Homoserine lactone is a competitive inhibitor of AI-1 biosynthesis.

DNA-binding proteins play a crucial role in various biological processes such as regulation of DNA modification, repair, replication, and transcription. These proteins widely participate in the production of drugs, antibiotics, and steroids. Many computational approaches have been developed to identify DNA-binding proteins, but some methods are time-consuming and expensive while some are laborious. Still, it is a challenging task for the researchers to develop highly promising computational methods to identify DNA-binding proteins with high precision. In our work, we developed a new computational approach named as DBPPred-PDSD which has more promising prediction power for DNA-binding proteins. We employed two datasets, extracted features via Split Amino Acid Composition (SAAC) and Position Specific Scoring Matrix (PSSM). Further, we applied the Discrete Wavelet Transform (DWT) on PSSM to extract dominant features. From these features space, optimal features are generated by Maximum Relevance and Minimum Redundancy (mRMR) and fused. To obtain highly informative features, we used Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and provided to well-known classifiers namely Support Vector Machine (SVM) and Random Forest (RF). Our model with the SVM classifier on three tests i.e. Jackknife cross-validation, 10-fold cross-validation and Independent tests achieved the highest success rate than other existing methods in the literature.

Diamond nanoparticles (DNPs) have been strongly integrated into diverse technological applications due to its nano-size, good chemical stability, super hardness, thermal conductivity and biocompatibility. Agglomeration and non-uniform distribution of DNPs in different matrices are the two main concern problems which limits its wide spread applications. Herein, our present research work demonstrated a comparative study of ball milling and acid treatments of functionalization DNPs through surface modification with carboxylic acid and amid functional groups. The dispersion measurement showed that ball mill treated DNPs is uniform and homogeneously dispersed in dimethyl-sulfoxide (DMSO) rather than as-received and acid treated DNPs. Furthermore, the use of various weight percentages of acid and ball mill functionalized DNPs in epoxy resin showed that 0.2 wt% is the optimum amount which revealed the highest ultimate tensile strength, flexural strength, Young's modulus and energy to break values of the nanocomposites. The comparative analysis showed that ball mill treatment of functionalization significantly enhanced the mechanical performances of epoxy resin more effectively than acid treatment thus demonstrated the importance of ball milling technique.

In this work, we propose a simple and cost effective colorimetric method for the detection of Pb 2+ using silver nanoparticles (AgNPs) prepared from the leaf extract of Aconitum violaceum . Due to the presence of significant amount of polyphenols in the extract, the AgNPs were successfully synthesized and were then characterized by UV–vis spectroscopy, Fourier transform infrared spectroscopy (FT-IR), X-Ray diffraction (XRD) and transmission electron microscopy (TEM). The as prepared AgNPs were employed as colorimetric probes for the detection of Pb 2+. In the presence of Pb2+, the biosynthesized AgNPs showed a color transformation from yellow to red, which was readily determined by UV–vis spectroscopy. The novel sensor exhibited good linearity for the detection of Pb2+ in the concentration range of 0.5 ˗ 25 μM (R2 = 0.996) with a detection limit (LOD) of 0.1 μM. Also, this sensing system presented a highly selective response for Pb2+ determination.

Today, telemedicine has a great reputation because of its capacity to provide quality healthcare services to remote locations. To achieve its purposes, telemedicine utilizes a number of wireless technologies as well as the Internet of Things (IoT). The IoT is redefining the capacity of telemedicine in terms of improved and seamless healthcare services. In this regard, this paper contributes to the set of features of telemedicine by proposing a model for an IoT-based health prescription assistant (HPA), which helps each patient to follow the doctors recommendations properly. This paper also designs a security system that ensures user authentication and protected access to resources and services. The security system authenticates a user based on the OpenID standard. An access control mechanism is implemented to prevent unauthorized access to medical devices. Once the authentication is successful, the user is issued an authorization ticket, which this paper calls a security access token (SAT). The SAT contains a set of privileges that grants the user access to medical IoT devices and their services and/or resources. The SAT is cryptographically protected to guard against forgery. A medical IoT device verifies the SAT prior to serving a request, and thus, ensures protected access. A prototype of the proposed system has been implemented to experimentally analyze and compare the resource efficiency of different SAT verification approaches in terms of a number of performance metrics, including computation and communication overhead.