Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

Isolation and Bioassay of a New Terminalone A from Terminalia arjuna.

Terminalia arjuna possesses significant cardioprotective, antidiabetic and antioxidant properties as these properties are described in Ayurveda. In the present study, three flavonoids were isolated through the separation and chromatographic purification of the whole plant material of T. arjuna. Spectroscopic characterization identified one of them as a new flavonoid "Terminalone A (1)" and two known flavonoids i.e., 6-hydroxy-2-(4-hydroxyphenyl)-7-methoxy-4H-chromen-4-one (2) and 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one (3). The bioactivity studies showed considerable antibacterial and antioxidant (DPPH radical scavenging) potential for all the three compounds 1-3 where the compound 1 showed strong antibacterial and antioxidant activity.

An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are low-cost, special-purpose networks introduced to resolve various daily life domestic, industrial, and strategic problems. These networks are deployed in such places where the repairments, in most cases, become difficult. The nodes in WSNs, due to their vulnerable nature, are always prone to various potential threats. The deployed environment of WSNs is noncentral, unattended, and administrativeless; therefore, malicious attacks such as distributed denial of service (DDoS) attacks can easily be commenced by the attackers. Most of the DDoS detection systems rely on the analysis of the flow of traffic, ultimately with a conclusion that high traffic may be due to the DDoS attack. On the other hand, legitimate users may produce a larger amount of traffic known, as the flash crowd (FC). Both DDOS and FC are considered abnormal traffic in communication networks. The detection of such abnormal traffic and then separation of DDoS attacks from FC is also a focused challenge. This paper introduces a novel mechanism based on a Bayesian model to detect abnormal data traffic and discriminate DDoS attacks from FC in it. The simulation results prove the effectiveness of the proposed mechanism, compared with the existing systems.

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts.

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks.

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

UHPLC-MS phytochemical profiling, biological propensities and in-silico studies of Alhagi maurorum roots: a medicinal herb with multifunctional properties.

The biological, chemical, and in silico properties of methanol and dichloromethane (DCM) extracts of Alhagi maurorum roots with respect to the antioxidant, enzyme inhibition, and phytochemical composition were evaluated. Total bioactive contents were determined spectrophotometrically, and the individual secondary metabolites composition was assessed via ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS) analysis. Antioxidant capacities were evaluated using a panoply of assays (2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radical scavenging, ferric reducing antioxidant power (FRAP), cupric reducing antioxidant power (CUPRAC), phosphomolybdenum total antioxidant capacity (TAC), and metal chelating activity (MCA)). The enzyme inhibition potential was studied against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), α-amylase, α-glucosidase, tyrosinase, urease and lipoxygenase (LOX) enzymes. The methanol extract was found to contain higher total phenolic (105.91 mg GAE/g extract) and flavonoid (2.27 mg RE/g extract) contents which can be correlated to its more substantial antioxidant potential as well as AChE, BChE, tyrosinase and α-glucosidase inhibition. However, the DCM extract was the most effective against α-amylase (1.86 mmol ACAE/g extract) enzyme inhibition. The UHPLC-MS analysis of methanol extract identified the tentative presence of a total of 18 secondary metabolites, including flavonoids, saponins, phenolic and terpenoid derivatives. Three compounds named emmotin A, luteolin 5,3'-dimethyl ether, and preferrugone were further investigated for their in silico molecular docking studies against the tested enzymes. The selected compounds were found to have higher binding interaction with AChE followed by BChE, α-glucosidase, α-amylase, and tyrosinase. The results of the present study have demonstrated A. mauroram to be considered as a lead source of natural antioxidant and enzyme inhibitor compounds.

Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks.

Internet of things (IoT) is a very important research area, having many applications such as smart cities, intelligent transportation system, tracing, and smart homes. The underlying technology for IoT are wireless sensor networks (WSN). The selection of cluster head (CH) is significant as a part of the WSN's optimization in the context of energy consumption. In WSNs, the nodes operate on a very limited energy source, therefore, the routing protocols designed must meet the optimal utilization of energy consumption in such networks. Evolutionary games can be designed to meet this aspect by providing an adequately efficient CH selection mechanism. In such types of mechanisms, the network nodes are considered intelligent and independent to select their own strategies. However, the existing mechanisms do not consider a combination of many possible parameters associated with the smart nodes in WSNs, such as remaining energy, selfishness, hop-level, density, and degree of connectivity. In our work, we designed an evolutionary game-based approach for CH selection, combined with some vital parameters associated with sensor nodes and the entire networks. The nodes are assumed to be smart, therefore, the aspect of being selfish is also addressed in this work. The simulation results indicate that our work performs much better than typical evolutionary game-based approaches.

Review: Bioactive constituents form Buddleja species.

Present review discuss the reported work on structures, origins and the potent biologically active natural products isolated from Genus Buddleja, which is known for having many important pharmacologically active substances. The Genus Buddleja have more than 100 species, many of them are distributed in Mediterranean and Asian regions. A very small number of common species of the Genus in majority of fruiting plants have been investigated for their biological potential. So for, isolation of about 153 or more new/novel chemical substances have been reported. Purposes of the review is to discuss the structurally established and pharmacologically significant natural substances from wide variety of different species of this genus. Traditionally, species of the genus are reported to be used for healing, treatment of liver diseases, bronchial complaints, preventing several other diseases by exhibiting diuretic properties, sedative functions, analgesic potential, antirheumatic actions, antimicrobial activities, anti hyperglycemic functions and antioxidant properties. In this review we will describe recently established medicinal chemistry aspects and complete list of phytoconstituents as well as their sources and reference.

In silico and BSA binding study of some new biological analogs of 1,2,4-triazolependant with azinane through microwave and conventional synthesis.

Microwave and conventional techniques were employed to synthesize a novel array of compounds 7a-g with 1,2,4-triazole and piperidine rings having great biological importance. The microwave assisted method has a better operational scope with respect to time and yield comparative to the conventional method. 1H-NMR, 13C-NMR and IR techniques were employed to justify the structure of synthesized compounds. The antioxidant, butyrylcholinesterase inhibition and urease inhibition potential of every synthesized compound was evaluated. Every member of the synthesized series was found potent against mentioned activities. Compound 7g was the most active anti-urease agent having IC50 (µM) value 16.5±0.09 even better than the thiourea with an IC50(µM) value of 24.3±0.24. The better urease inhibition potential of 7g was also elaborated and explained by docking and bovine serum albumin (BSA) binding studies.

Three new α-glucosidase inhibitor benzo-isochromenes from Datura stramonium.

Three new α-glucosidase inhibitory benzo-isochromenes were isolated from the chloroform fraction of Datura stramonium. Their structures were established with the help of modern spectroscopic techniques and were assigned the names as 1,6,8-triimethoxy-2-methyl-3,4-dihydrobenzoisochromene-5,10-diol, 3,6-dimethoxy-5-hydroxy-4-methylbenzoisochromene-9,11-dione and demethylflavasperon for compounds 1-3 respectively. The α-glucosidase inhibiting activity of compound 2 showed strong inhibition with an IC50 value of 27.5 µM, while compound 1 exhibited moderate activity with IC50 value of 60.2 µM compared to positive control (acarbose).

Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy.

The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmologists examine fundus images (FI) to detect and classify stages of diabetic retinopathy (DR). DR is a chronic disease that appears in patients with long-term diabetes. Unattained patients can lead to severe conditions of DR, such as retinal eye detachments. Therefore, early detection and classification of DR are crucial to ward off advanced stages of DR and preserve the vision. Data diversity in an ensemble model refers to the use of multiple models trained on different subsets of data to improve the ensemble's overall performance. In the context of an ensemble model based on a convolutional neural network (CNN) for diabetic retinopathy, this could involve training multiple CNNs on various subsets of retinal images, including images from different patients or those captured using distinct imaging techniques. By combining the predictions of these multiple models, the ensemble model can potentially make more accurate predictions than a single prediction. In this paper, an ensemble model (EM) of three CNN models is proposed for limited and imbalanced DR data using data diversity. Detecting the Class 1 stage of DR is important to control this fatal disease in time. CNN-based EM is incorporated to classify the five classes of DR while giving attention to the early stage, i.e., Class 1. Furthermore, data diversity is created by applying various augmentation and generation techniques with affine transformation. Compared to the single model and other existing work, the proposed EM has achieved better multi-class classification accuracy, precision, sensitivity, and specificity of 91.06%, 91.00%, 95.01%, and 98.38%, respectively.

Bioassay's Directed Isolation-Structure Elucidation and Molecular Docking of Triterpenes from Persea duthiei against Biologically Important Microbial Proteins.

The research work presented in this study is mainly concerned with the bioactivity-directed phytochemical and biological evaluation of Persea duthiei. Persea duthiei is a typical medicinal plant used to treat a variety of ailments such as asthma, edema, and bronchitis. Ethyl acetate, n-hexane, n-butanol, and compounds that are soluble in water were used to examine the antibacterial as well as antifungal capacities of the plant. The antibacterial activity of the soluble parts of ethyl acetate and n-hexane against Escherichia coli, Staphylococcus aureus, Salmonella typhi, and Bacillus subtilis was high, even though there was no activity against Pseudomonas aeruginosa. Likewise, the n-hexane and ethyl acetate fractions were found to have substantial efficacy against several fungal strains such as Aspergillus flavus, Aspergillus fumigates, Fusarium solani, and Aspergillus niger, but not against Candida glabrata. Among the studied fractions, the ethyl acetate soluble fraction had potent antibacterial activity against all of the tested species. This fraction was submitted to phytochemical analysis utilizing various chromatographic methods for the extraction of various pure components. As a consequence, four compounds were isolated, and their structures were elucidated using various spectroscopic methods such as IR, EIMS, HR-EIMS, 1H-NMR, 13C-NMR, NOESY, COSY, HMBC, and HMQC. Urs-12-en-3ß-ol (α-amyrine) (1), Urs-12-ene-2α-3ß-diol (chamaedrydiol) (2), 3ß-hydroxyurs-12-en-28-aldehyde (ursolic aldehyde) (3), and 12-oleanex-3ß-ol (ß-amyrine) (4) were extracted. Compounds 1, 2, 3, and 4 were examined for antibacterial and antifungal activity and found to have zones of inhibition ranging from 0 to 11 mm against tested bacteria strains and percent inhibition ranging from 0 to 25 percent against fungus strains. Compounds 1 and 4 showed strong efficacy against the investigated fungal species, with a 25% inhibition rate. In the case of antibacterial activity, compounds 4 and 1 showed potent activity with zones of inhibition of 11 mm and 10 mm, respectively. Compounds 2 and 3 were observed to have nonsignificant antimicrobial activity. However, docking studies reflected the complex formation of compound 1 with beta-hydroxyacyl-ACP dehydratase HadAB and S. aureus tyrosyl-tRNA synthetase and compound 2 with topoisomerase II DNA gyrase complex, and they were reported to have antibacterial properties. Similarly, compound 4 was discovered to be well compatible with the lanosterol 14-demethylase (fungal enzyme) and is thus regarded as having antifungal capabilities. Chimera software was used to identify the binding pockets of these complexes. These results indicated that Persea duthiei is a valuable source of medicinal compounds for medication development.

Macrocyclic ß-sheet peptides that inhibit the aggregation of a tau-protein-derived hexapeptide.

This paper describes studies of a series of macrocyclic ß-sheet peptides 1 that inhibit the aggregation of a tau-protein-derived peptide. The macrocyclic ß-sheet peptides comprise a pentapeptide "upper" strand, two δ-linked ornithine turn units, and a "lower" strand comprising two additional residues and the ß-sheet peptidomimetic template "Hao". The tau-derived peptide Ac-VQIVYK-NH(2) (AcPHF6) aggregates in solution through ß-sheet interactions to form straight and twisted filaments similar to those formed by tau protein in Alzheimer's neurofibrillary tangles. Macrocycles 1 containing the pentapeptide VQIVY in the "upper" strand delay and suppress the onset of aggregation of the AcPHF6 peptide. Inhibition is particularly pronounced in macrocycles 1a, 1d, and 1f, in which the two residues in the "lower" strand provide a pattern of hydrophobicity and hydrophilicity that matches that of the pentapeptide "upper" strand. Inhibition varies strongly with the concentration of these macrocycles, suggesting that it is cooperative. Macrocycle 1b containing the pentapeptide QIVYK shows little inhibition, suggesting the possibility of a preferred direction of growth of AcPHF6 ß-sheets. On the basis of these studies, a model is proposed in which the AcPHF6 amyloid grows as a layered pair of ß-sheets and in which growth is blocked by a pair of macrocycles that cap the growing paired hydrogen-bonding edges. This model provides a provocative and appealing target for future inhibitor design.

Multi-channel multi-radio using 802.11 based media access for sink nodes in wireless sensor networks.

The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.

Multi-channel distributed coordinated function over single radio in wireless sensor networks.

Multi-channel assignments are becoming the solution of choice to improve performance in single radio for wireless networks. Multi-channel allows wireless networks to assign different channels to different nodes in real-time transmission. In this paper, we propose a new approach, Multi-channel Distributed Coordinated Function (MC-DCF) which takes advantage of multi-channel assignment. The backoff algorithm of the IEEE 802.11 distributed coordination function (DCF) was modified to invoke channel switching, based on threshold criteria in order to improve the overall throughput for wireless sensor networks (WSNs) over 802.11 networks. We presented simulation experiments in order to investigate the characteristics of multi-channel communication in wireless sensor networks using an NS2 platform. Nodes only use a single radio and perform channel switching only after specified threshold is reached. Single radio can only work on one channel at any given time. All nodes initiate constant bit rate streams towards the receiving nodes. In this work, we studied the impact of non-overlapping channels in the 2.4 frequency band on: constant bit rate (CBR) streams, node density, source nodes sending data directly to sink and signal strength by varying distances between the sensor nodes and operating frequencies of the radios with different data rates. We showed that multi-channel enhancement using our proposed algorithm provides significant improvement in terms of throughput, packet delivery ratio and delay. This technique can be considered for WSNs future use in 802.11 networks especially when the IEEE 802.11n becomes popular thereby may prevent the 802.15.4 network from operating effectively in the 2.4 GHz frequency band.

Photo-assisted inactivation of highly drug resistant bacteria and DPPH scavenging activities of zinc oxide graphted Pd-MCM-41 synthesized by new hydrothermal method.

A major current biomedical challenge is to find materials that are specific, have high efficiency and with long lasting stability to serve as antimicrobial agents. In this contribution we examined new bifunctional nanostructural materials (ZnO/Pd-MCM-41) which were synthesized by a new hydrothermal procedure. To deposit active cites i.e. ZnO, a new protocol was followed in which catechol was used as a precipitating agent. Results indicated that nanostructures comprising palladium nanocrystals of a small size dispersed consistently within the hexagonal pores of the MCM-41 and also ZnO was successfully coated on mesoporous Pd-MCM-41 and that the mesoporous Pd-MCM-41 structure has been well-maintained upon modification of ZnO. The ZnO/Pd-MCM-41 is promising antibacterial agent and have efficient light inhibition activity towards Escherichia coli (E. coli), Psedomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus). The inhibition zone of irradiated ZnO/Pd-MCM-41 nanostructure against E. coli, P. aeruginosa and S. aureus were (17 ± 0.4) mm, 18 (±0.4) mm and 22 (±0.2) mm respectively while that in dark were (9 ± 0.5) mm, 11 (±0.3) mm and 13 (±0.4) mm respectively. The production of reactive oxygen species and hemolytic assay were also analyzed. Different parameters affecting the photo-inhibition efficiency of ZnO/Pd-MCM-41 were also studied. Likewise, the antioxidant activity of these nanostructures was studied against DPPH stabilization. Results indicated that the synthesized nanostructures are highly active and stabilized 99 % DPPH at very low concentration i.e. 1.4 mg/mL.

A facile fabrication of silver/copper oxide nanocomposite: An innovative entry in photocatalytic and biomedical materials.

In this work, principles and techniques of green chemistry were implemented which exploit environmentally and economically friendly methods using an accessible and non-toxic medium, that is water and ascorbic acid (Vit.C), which leads to the synthesis of silver/ copper oxide (Ag/CuO) nanocomposite. Vit.C was used to furnish the synthesis of excellent and controlled crystalline silver nanoparticles (AgNPs), copper oxide (CuO) and Ag/CuO nanocomposite. Moreover, the structures of all the synthesized nanomaterials were confirmed by wide range of characterization techniques which include UV-vis spectroscopy, FTIR, X-ray diffraction and Electron Diffraction Spectroscopy. However external and internal morphology of newly synthesized nanomaterials were examined by using Scanning Electron Microscopy and High resolution Transmission Electron Microscopy, respectively. The prepared nanomaterials were evaluated for catalytic decomposition of methyl orange (MO) in dark, visible light and UV light. The results showed that 20 %, 50 % and 90 % degradation of MO in 40 min was observed in dark, visible light and UV light, respectively. To further explore the biological potential of synthesized Ag/CuO nanocomposite, we also evaluated it for an inactivation of bacteria where Escherichia coli has 17(±0.5 mm) and Staphylococcus aureus has 20 (±0.6 mm) zone of inhibition in light. The results showed that reactive oxygen species (ROS) were produced in the presence of light and Ag/CuO. These ROS are the main source of inactivation of bacteria. The prepared nanomaterial has also good efficiency against DPPH stabilization. Further study is required to investigate the hidden applications of the as synthesized nanomaterials.

Antioxidant constituents from Cotoneaster racemiflora.

A new lignan rhamnoside, racemiside (1), has been isolated from the ethyl acetate-soluble fraction of Cotoneaster racemiflora, along with scopoletin (2), 7,8-dimethoxy-6-hydroxycoumarin (3), 3,3',4'-tri-O-methylellagic acid (4), and cereotagloperoxide (5), reported for the first time from this species. All of them showed profound antioxidative activities in the DPPH assay.

Severe recurrent oral ulceration secondary to erosive lichen planus.

A case of recurrent progressively severe ulceration secondary to erosive lichen planus is reported. The patient developed marked malnutrition as a result of extensive involvement of the oral cavity. In addition to the oral ulcerations, she also had violaceous spots present over her forearm. Treatment administered in view of histopathological report and clinical presentation, resulted in marked improvement in symptoms and weight gain.

Two new protease-inhibiting glycosphingolipids from Buddleja crispa.

Crispins A (1) and B (2), two new glycosphingolipids, were isolated from the whole plant Buddleja crispa, along with three known compounds: alpha-amyrin, linoleic acid, and stigmasterol. Their structures were elucidated by chemical and spectroscopic techniques. Both 1 and 2 showed significant inhibitory activity against alpha-chymotrypsin in a concentration-dependent manner.