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Wireless sensor networks (WSNs) are widely acknowledged for their potential as a robust infrastructure for collecting, processing, and transmitting information. Rezaeipour & Barati proposed a hierarchical key management scheme that manages key creation, distribution, and maintenance to provide services like message confidentiality, integrity, and authenticity to wireless sensor networks. As per the assertions posited by the authors, their scheme purportedly exhibits resilience against a spectrum of potential threats. Nevertheless, upon subjecting their scheme to meticulous scrutiny, our analysis revealed vulnerabilities to manifold adversarial strategies, notably including man-in-the-middle attacks, replay attacks, impersonation attacks, and node capture attacks. This scheme fails to establish a secure session key agreement also. In response to these identified shortcomings, this research devised an "Improved Hierarchical Key Management" (IHKM) Scheme aimed at rectifying the deficiencies of Rezaeipour and Barati's scheme (RB scheme). This work proposed an enhanced key management scheme that utilizes a blend of symmetric and asymmetric key cryptography to enhance the security parameters of Hierarchical WSNs. A comprehensive security analysis of the proposed work shows that the IHKM scheme is secure against impersonation attacks, man-in-the-middle attacks, replay attacks node capture attacks, Denial-of-Service attacks, and base station bypassing attacks while establishing the mutual key agreement between the area managers and sensor nodes. Moreover, a comparative analysis of the IHKM scheme exhibits notable reductions in energy consumption (62.27% and 49.15%) and memory utilization (39.28%, 23.36%) when compared with the RB scheme and HISCOM schemes respectively. Simultaneously, IHKM contributes to the extension of the network's overall lifespan by 48.78% as compared to HISCOM scheme and 32.63% as compared to compared with the RB scheme, thereby enhancing the efficiency and longevity of the WSN infrastructure. [ABSTRACT FROM AUTHOR]

High intensity focused ultrasound (HIFU) is a promising non-invasive technique for treating solid tumors using thermal and histotripsy-based mechanical ablation. However, its clinical significance in different tumor types is not fully understood. To assess its therapeutic efficacy and immunomodulatory properties, we compared HIFU thermal ablation and histotripsy ablation in dogs with spontaneous tumors. We also evaluated the ability of non-ablative HIFU-based mild hyperthermia (40–45 ºC) to improve Doxorubicin delivery and immunomodulation. Our results showed that HIFU thermal ablation induced tumor remission in the majority of treated patients over 60 days, while histotripsy achieved partial response to stable disease persistence. The adverse effects of thermal ablation were minor to moderate, while histotripsy exposures were relatively well-tolerated. Furthermore, we observed a correlation between HIFU-therapeutic response and serum anti-tumor cytokine profiles and the presence of functionally active cytotoxic immune cells in patients. Similarly, Doxorubicin-treated patients showed improved drug delivery, efficacy, and anti-tumor immune responses with HIFU hyperthermia. In conclusion, our study demonstrates that depending on the tumor type and treatment parameters, HIFU treatments can enable tumor growth control, immune activation, and chemotherapy in veterinary patient. These findings have significant clinical implications and highlight the potential of HIFU as a promising cancer treatment approach. [ABSTRACT FROM AUTHOR]

Mineral composition in milk can affect its nutritional as well as physio-chemical properties of milk and is considered an important trait in the evaluation of milk quality. The composition and concentration of milk minerals could be altered with natural source of variation including nutrition and genetics. The effect of diet on milk minerals is well studied. However, genetic effects on the milk minerals have recently gained the attention. This review provides an overview of the genetic variation of milk minerals, and the genomic regions associated with mineral concentration in the milk are also discussed. The difference of milk minerals between breeds and the genetic parameters including heritability estimates and correlation among minerals indicates that milk minerals are under strong genetic control. Recently, the genome-wide association study (GWAS) has explored several regions associated with milk minerals and thus provides a new genetic source for improving the milk quality through genomics-assisted breeding. Hence, a combination of the qualitative and molecular approaches can be exploited to improving the nutritional quality of cattle milk in terms of its mineral composition. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) are one of the best ways to look into the connection between single-nucleotide polymorphisms (SNPs) and the phenotypic performance. This study aimed to identify the genetic variants that significantly affect the important reproduction traits in Vrindavani cattle using genome-wide SNP chip array data. In this study, 96 randomly chosen Vrindavani cows were genotyped using the Illumina Bovine50K BeadChip platform. A linear regression model of the genome-wide association study was fitted in the PLINK program between genome-wide SNP markers and reproduction traits, including age at first calving (AFC), inter-calving period (ICP), dry days (DD), and service period (SP) across the first three lactations. Information on different QTLs and genes, overlapping or adjacent to genomic coordinates of significant SNPs, was also mined from relevant databases in order to identify the biological pathways associated with reproductive traits in bovine. The Bonferroni correction resulted in total 39 SNP markers present on different chromosomes being identified that significantly affected the variation in AFC (6 SNPs), ICP (7 SNPs), DD (9 SNPs), and SP (17 SNPs). Novel potential candidate genes associated with reproductive traits that were identified using the GWAS methodology included UMPS, ITGB5, ADAM2, UPK1B, TEX55, bta-mir-708, TMPO, TDRD5, MAPRE2, PTER, AP3B1, DPP8, PLAT, TXN2, NDUFAF1, TGFA, DTNA, RSU1, KCNQ1, ADAM32, and CHST8. The significant SNPs and genes associated with the reproductive traits and the enriched genes may be exploited as candidate biomarkers in animal improvement programs, especially for improved reproduction performance in bovines. [ABSTRACT FROM AUTHOR]

Milk fat composition is an important trait for the dairy industry as it directly influences the nutritional and technological properties of milk and other dairy products. The synthesis of milk fat is a complex process regulated by a network of genes. Thus, understanding the genetic variation and molecular mechanisms regulating milk fat synthesis will help to improve the nutritional quality of dairy products. In this review, we provide an overview of milk fat synthesis in bovines along with the candidate genes involved in the pathway. We also discuss de novo synthesis of fatty acids (ACSS, ACACA, FASN), uptake of FAs (FATP, FAT, LPL), intracellular activation and channelling of FAs (ACSL, FABP), elongation (EVOLV6), desaturation (SCD, FADS), formation of triglycerides (GPAM, AGPAT, LIPIN, DGAT), and milk lipid secretion (BTN1A1, XDH, PLIN2). The genetic variability of individual fatty acids will help to develop selection strategies for obtaining a healthier milk fat profile in bovines. Thus, this review will offer a potential understanding of the molecular mechanisms that regulate milk fat synthesis in bovines. [ABSTRACT FROM AUTHOR]

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks. Low and discrete power and limited amount of memory and finite number of resources form some major bottlenecks in the workflow. Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility. Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms—grey wolf algorithm and firefly algorithm to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach based on energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio. [ABSTRACT FROM AUTHOR]

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement. [ABSTRACT FROM AUTHOR]

Wireless Sensor networks contain sensor nodes with specialized infrastructure for the purpose of inspection of certain physical conditions at very divergent environments. Usually, physical characteristics of the environment including temperature, humidity, pressure etc. are examined by the sensor nodes. These nodes are generally small sized and light weight. Because of their small size, power supply is very much limited. Careful usage of the limited power supply is a very important factor to be considered during communication of data in the network. With the aim of achieving effective energy usage, several techniques have been applied for the routing of information packets. One such technique is clustering of sensor nodes within the network. It implicates that the nodes organize themselves into groups to form several clusters where each group has a leader of the cluster group called the cluster head. The routing using this clustering technique does not always involve direct transmission. Rather, multi-hop transmission happens usually, with the data firstly being transferred from sensor nodes to their corresponding cluster heads and then later from cluster heads to the sink (Base Station). The cluster head is designated arbitrary or by taking into account various factors which differ according to the different routing protocols. In this paper, a new routing algorithm QBCR (Quadrangle based Clustering Routing Protocol) has been proposed which entails clustering process for routing the data to the base station. This protocol exhibits improvement over conventional routing protocols like LEACH, SEP and DEEC. In this clustering algorithm, division of the zones (clusters) based on the energy levels of the nodes has been carried out in form of quadrangle shaped clusters. The proposed protocol exhibits significant enhancement in the lifetime of the network, stability interval, rate of packet transfer (throughput), and the average energy of the network. The proposed protocol shows an improvement of atleast 50% in all parameters (lifetime of the network, stability interval, rate of packet transfer (throughput) and the average energy of the network) in comparison to the conventional protocols. This proposed protocol is a clear choice over the standard routing protocols wherever it is possible to deploy nodes uniformly in different zones. [ABSTRACT FROM AUTHOR]

As the popularity of wireless sensor networks (WSNs) is rapidly expanding across businesses and industries, we must make everything around us more intelligent and informative. In such networks, sensor nodes (SNs) serve as the WSN's eyes, obtaining data about various environments and conditions, while the base station (or Sink) serves as the WSN's brain, analyzing the acquired data and making decisions. However, on the one hand, the large volume of data collected by the SNs consumes the SNs' limited energy and complicates data analysis at the sink for decision making. In this article, we present a data prediction model based on the stepwise data regression method for handling large amounts of data obtained by cluster-based WSNs. The prediction model is built stepwise data regression method that is implemented at both tiers of each cluster: cluster member nodes and Cluster Heads; and is compatible with both homogeneous and heterogeneous network setups. In intracluster data transmissions, the proposed data prediction model employs a two-buffer stepwise data regression method to synchronize the sensed and predicted data intending to reduce the cumulative errors from continuous predictions. The performance of the proposed work is examined by extensive simulations on real sensor data collected from several applications and is also compared with CPMDC (Diwakaran et al. in J Supercomput 75:3302–3316, 2019) and TDPA (Sinha and Lobiyal in Wirel Pers Commun 84:1325–1343, 2015) models. The proposed model proved to be very energy efficient, with improved data prediction accuracy, increased network lifetime, and more successful data predictions while sustaining an acceptable data accuracy, and improved network lifetime when compared with CPMDC (Diwakaran et al. 2019) and TDPA (Sinha and Lobiyal 2015) models. respectively. [ABSTRACT FROM AUTHOR]

In this paper, a novel computationally intelligent technique for Pattern-Based clustering has been proposed. The proposed technique works in two stages. In the first stage feature selection is done. In the second stage a Significance Weight (SW) based Unsupervised Fuzzy Decision Tree Induction (SW-UFDT) is proposed for clustering. The feature selection is done using the ant colony optimization. The feature selection is done using Ant Colony Optimization is done as it is inherently parallel and provides the optimal features. It helps n the dimensionality reduction of the large dataset for clustering. In the second stage an unsupervised decision tree induction method utilizing significance weight of the attribute has been proposed. Finally, the clusters are formed using the equivalence classes. The performance investigation of the proposed technique is done over 20 different capacities UCI repository datasets in terms of two important cluster quality metrics. The comparative analysis shows that the proposed technique is more efficient than the existing Pattern/Non Pattern-Based clustering algorithms. The difference in performance is also validated using the Friedman and Bergmann–Hommel test. Thus, the proposed technique can be used for clustering of large continuous valued data. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) are the integration of information and communications technologies with applications which are significant in traffic control and management. The increased number of on road vehicles in urban areas urges the need of development of automated methods for traffic management. Vehicle identification, classification and analysis enable the intelligent transportation systems to make decisions. In this paper, an automated method for video analysis for vehicle identification using a modified Region based Convolution Neural Network (RCNN) has been proposed. The traffic videos collected by CCTV cameras installed on the roads are analyzed for vehicle identification in a given frame. The pretrained google net is used to extract features. These features are used by the Region based Convolution Neural Network for vehicle identification. The vehicles are identified using probability score computed using intersection of objects (IoU). The identified vehicles are classified into ten different vehicle classes. The proposed network concatenates features from previous layers to reduce loss and consequently improve the vehicle identification accuracy. The vehicle identification method is further extended for vehicle counting and behavioral analysis. The vehicle counting information can be used for congestion control in smart cities. The behavioral analysis includes computation of speed of vehicles. The speed information is useful for traffic law enforcement in smart cities. The proposed method is applied on MIO-TCD vehicle dataset and EBVT video dataset. The results are calculated using three different metrics namely average accuracy, mean precision and mean recall. Obtained results are also compared with other state of the art methods. The results show significant improvement and thus the method can be effectively used for video analysis. [ABSTRACT FROM AUTHOR]

It is anticipated that during the next 3 decades, approximately two-thirds of the world's population would live in urban regions. This represents a significant movement towards urbanisation on the part of the world's population. Thus, urban planners need effective methods for regulating both the use of land and the management of infrastructure. The accessibility of data that is accurate, up to date, and insightful is an extremely important factor in the formation of effective methods of urban administration and smart cities. Combining technologies for pervasive sensing, intelligent computing, cooperative communication, and mass data management may enhance urban surroundings, quality of life, and smart city systems. To do this, we need to develop novel strategies for integrating computation and intelligence into urban environments. This field of study focuses on the intersection of computing and intelligence in urban settings. [ABSTRACT FROM AUTHOR]

Secure communication is one of the fundamental challenges for wireless sensor networks (WSNs). To transfer data securely over the wireless medium, a secret key is established in an energy-efficient, storage-efficient, and authentic manner. The most recent research in this area has focused on homogenous networks, in which all sensors have the same features and fundamental management techniques. Because of the advantages of clustering, heterogeneous networks have outperformed homogeneous networks in terms of achievement and performance. This prompted us to develop a symmetric-key cryptography-based secure and energy-efficient key-establishment scheme for heterogeneous WSNs. Because symmetric key cryptography consumes little energy, it is an excellent choice for network security. Limitations of symmetric key cryptography can be addressed by employing appropriate methods. Evaluation of the suggested work shows that storage requirements have been minimised and the energy consumption is reduced. This work provides a high level of security against a variety of intruders and potential security risks, and it is also more scalable than current techniques. [ABSTRACT FROM AUTHOR]

The main issue for the security of wireless sensor networks (WSNs) is how to allow sensor nodes (SNs) to establish and share cryptographic keys in an energy-efficient, storage-efficient, and authentic manner for their secure data transmission. Furthermost recent studies carried out in this direction is concerned with homogeneous networks in which all sensor has identical characteristics and fundamental administration mechanisms. However, Cluster-based sensor networks have demonstrated better achievements and performance than homogeneous networks because of the several benefits of clustering. This inspired us to propose a secure key-establishment method for cluster-based sensor networks based on symmetric-key cryptography. Since symmetric key cryptography has small energy consumption, they are a great choice to prefer for securing the networks. Even though symmetric key cryptography has high storage needs, this deficiency can be reduced by using suitable methods—evaluating the proposed work that the storage needs are reduced along with reduced energy consumption. The work offers a favorable level of security against various intruders and possible security threats and is additionally scalable than the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Wireless communication technology is used in various applications and therefore the availability of wireless spectrum is a serious concern. The number of cellular users is increasing rapidly. The 5G network will be able to cater to the requirements of the increasing users. However, the spectrum efficiency needs to be improved. Cooperative spectrum sensing is being widely used by cognitive radios for utilizing the available spectrum in an efficient manner. Evolutionary Algorithm based optimization methods are used in various applications and have proved to be very efficient. These algorithms can also be used for optimizing the cooperative spectrum sensing in cognitive radios. In this paper, two methods are proposed for optimal Cooperative Spectrum Sensing for 5G cognitive networks. The optimization algorithms are designed using whale optimization algorithm (WOA) and Particle Swarm Optimization (PSO). The objective is to increase the probability of detection by optimizing the 'weighting vector'. In the first method, WOA is used for cooperative spectrum sensing optimization in cognitive radios. In the second method, WOA method is improved using the PSO algorithm. A hybridized WOA-PSO algorithm is proposed to further improve the probability of detection. The results obtained are compared with other existing algorithms. The proposed methods perform better than the existing methods. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify candidate genes associated with milk fat per cent and fatty acid (FA) composition in Vrindavani cattle using the Illumina 50 K single-nucleotide polymorphism (SNP) array. After quality control, a total of 41,427 informative and high-quality SNPs were used for a genome-wide association study (GWAS) for milk fat percentage and 16 different types of fatty acids. Lactation stage, parity, test day milk yield, and proportion of exotic inheritance were included as fixed effects in the GWAS model. A total of 67 genome-wide significant (P < 1.20 × 10−06) SNPs and 176 suggestive significant (P < 2.41 × 10−05) SNPs were identified. Out of these, 15 SNPs were associated with more than one trait. The strongest associations were found on BTA14 for milk fat percentage and on BTA2 and BTA16 for polyunsaturated fatty acids. Several significant SNPs were identified close to or within the genes ELOVL6, FABP4, PMP2, PLIN1, MFGE8, GHRL2, and LDLRAD3 which are known to be associated with fat percentage and FA composition in dairy cattle breeds. This study is a step forward to better characterize the molecular mechanisms of phenotypic variation in milk fatty acids in a taurine–indicine composite cattle breed reared in tropical environments. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Big data analytics and knowledge discovery for urban computing and intelligence" published in the journal Complex & Intelligent Systems. The correction addresses an error in the affiliation of the second author, Prof Seungmin Rho. The correct affiliation is the "Department of Industrial Security, Chung-Ang University, Seoul, Korea." The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The article was written by Krishna Kant Singh, Seungmin Rho, Akansha Singh, and Chernyi Sergei. [Extracted from the article]

In the present investigation, differentially expressed genes (DEGs) were studied using RNA sequencing (RNA-seq) technique in porcine peripheral blood mononuclear cells (PBMC) of weaned Ghurrah and crossbred piglets at 3-month age. Transcriptomic analysis was done using three different packages, namely, EBSeq, DESeq2, and edgeR, to identify the DEGs between Ghurrah and crossbred piglets. Total 7717 DEGs were commonly identified by all three packages, out of which 4151 genes found to be up-regulated, and 3566 genes were down-regulated. Functional annotation of these DEGs indicated metabolism as the most commonly enriched category followed by the immune response. Genes related to metabolism and growth were up-regulated in crossbred piglets as compared with Ghurrah piglets, whereas immunity-related genes were up-regulated in Ghurrah piglets elucidating the disease resistance nature of this indigenous breed over crossbred counterparts. Further, eight DEGs, namely, LRP-1, ADCY4, ERRFI1, LDHD, ARG1, OASL, MGARP, and S100A8, were validated by qRT-PCR in a separate set of biological samples and found to be in concordance with RNA-seq results. Finding in the present study provides insight into genes and their molecular mechanisms governing difference in growth performance between Ghurrah and crossbred pigs. [ABSTRACT FROM AUTHOR]

Psoriasis is a common and an inflammatory skin disease. Trace elements play an important role in the skin metabolism such as keratinisation and melanin formation as well as immunological and inflammatory reactions. Henceforth, the present study was aimed to evaluate the essential metals with special reference to Cu and Zn. In this study, 72 psoriatic patients and 50 controls were enrolled. On the basis of PASI score psoriatic group comprising of 48 mild and 24 severe psoriatic patients were grouped. The serum trace elements analysis in the subjects revealed that serum Cu levels and Cu/Zn ratio was significantly higher in the psoriatic patients as compared to controls. Interestingly, severity of the psoriasis was well correlated with the serum Cu levels. Taking together, all these findings suggest that Cu may be a major culprit in the pathogenesis as well as in the severity of the disease. [ABSTRACT FROM AUTHOR]

The present study was conducted to identify the differentially expressed miRNAs (DE miRNAs) in the peripheral blood mononuclear cells of crossbred pigs in response to CSF vaccination on 7 and 21 days of post vaccination as compared to unvaccinated control (0 dpv). Simultaneously, set of miRNA was predicted using mRNA seq data at same time point. The proportion of CD4−CD8+ and CD4+CD8+ increased after vaccination, and the mean percentage inhibition was 86.89% at 21 dpv. It was observed that 22 miRNAs were commonly expressed on both the time points. Out of predicted DE miRNAs, it was found that 40 and 35 DE miRNAs were common, obtained from miRNA seq analysis and predicted using mRNA seq data on 7 dpv versus 0 dpv and 21 dpv versus 0 dpv respectively. Two DE miRNAs, ssc-miR-22-5p and ssc-miR-27b-5p, were selected based on their log2 fold change and functions of their target genes in immune process/pathway of viral infections. The validations of DE miRNAs using qRT-PCR were in concordance with miRNA seq analysis. Two set of target genes, CD40 and SWAP70 (target gene of ssc-miR-22-5p) and TLR4 and Lyn (target gene of ssc-miR-27b-5p), were validated and were in concordance with results of RNA seq analysis at a particular time point (except TLR4). The first report of genome-wide identification of differentially expressed miRNA in response to live attenuated vaccine virus of classical swine fever revealed miR-22-5p and miR-27b-5p were differentially expressed at 7 dpv and 21 dpv. [ABSTRACT FROM AUTHOR]

Aim: India has been predicted to have the fastest increase in deaths attributable to tobacco in the first 2 decades of the twenty-first century. Consequently, it is imperative to examine the extent of nicotine dependency among adults in India. The main objective of the present article is to characterize nicotine dependency related to smoking and smokeless tobacco among adults according to age, education level, duration of use and other socioeconomic characteristics. Subjects and methods: We analyzed Global Adult Tobacco Survey (GATS) 2010 data for 9190 smokers and 13,357 smokeless tobacco users who were 15 years of age or older. Time to first tobacco use of the day was used as a measure of nicotine dependence. We employed a decision tree algorithm from SAS Enterprise Miner to conduct classification analysis to establish the relationship among nicotine dependency for smoking, smokeless tobacco use, and its predictors. Results: More than 65% of smokers and 54.3% of smokeless tobacco users were nicotine dependent. The decision tree results showed that the most important explanatory variable for the prediction of smoking dependency was duration of smoking followed by education, gender and region. For smokeless tobacco use, duration of smokeless tobacco use was also the most important explanatory variable followed by education, occupation and age. Conclusions: Smoking and smokeless tobacco use dependencies are driven by different sets of demographic and socioeconomic predictors. Suitable plans for tobacco cessation need to be implemented based on the type of tobacco and particularly duration of use, which was found to be the most important determinant of quitting. [ABSTRACT FROM AUTHOR]

The wireless sensor networks (WSNs) have imminent constrains that makes security a crucial issue. Weak defense capability makes WSN a soft target against worm attacks. A single compromised node can spread the worm via communication in the entire network. In this paper, we propose a mathematical model that studies the epidemic behavior of such digital worms. Furthermore, we study the effect of these worms with various communication radius and node distributed density. We investigate the proposed model using the stability theory of differential equations. Basic reproduction number is found that helps us to find the threshold values for communication radius and node density distribution. The proposed model is checked and validated through extensive simulation results. Finally, we compare our scheme with the existing schemes. Comparison analysis shows that the proposed model is efficient as it has the low rate of the infectious node for different communication radius. [ABSTRACT FROM AUTHOR]

Turkanovic et al. (Ad Hoc Netw 20:96-112, 2014) proposed a user authentication and key agreement scheme based on the notion of the 'internet of things' for wireless sensor network. Authors claimed that their scheme is safe against various attacks. We found that this scheme fails against session key recovery attack. If an attacker has stolen the smartcard, he can easily obtain the session key generated between user and sensor node. In this paper, we shows that the attacker is able to compute the secret parameter $$ K_{{GW - U_{i}}} $$ , which is the used by a gateway during communication with others. Now the attacker can modify the first message that was send by the user to the sensor node. Finally, he breaks the complete system. We also provide few other insecurities and vulnerability to many attacks like offline password guessing attack, replay attack and impersonate attack etc. To remedy this, an enhanced scheme is also proposed to remove the flaws of the Turkanovic et al. scheme. The result and performance analysis of our proposed scheme shows that the new enhanced scheme provides high security with low computation, communication and storage overhead. [ABSTRACT FROM AUTHOR]

In this paper, a semiautomatic method for landslide detection from satellite images and digital terrain information using generalized improved fuzzy Kohonen clustering network (GIFKCN) classifier is presented. The proposed method classifies the pre- and post-landslide images using the GIFKCN classifier which is trained using spectral indices such as normalized difference vegetation index, normalized difference building index and normalized difference water index. The changes in the vegetation class are identified using the pre- and post-classified images. Generally, landslides result in loss of vegetation; thus, using this property, candidate landslides are identified. Finally, false positives are removed using a rule set created from DEM derivatives slope and aspect. The proposed method is applied on Landsat 5 and Advanced Land Imager EO-1 satellite images to detect earthquake-induced landslides that occurred in Sikkim state of India due to the September 18, 2011, earthquake of magnitude M = 6.9. The terrain information used is ASTER Global Digital Elevation Model of the area. The accuracy assessment of the method is done, and the results show that the landslides are identified and classified efficiently. [ABSTRACT FROM AUTHOR]

The advent of machine learning techniques and image processing techniques has led to new research opportunities in this area. Machine learning has enabled automatic extraction and analysis of information from images. The convergence of machine learning with image processing is useful in a variety of security applications. Image processing plays a significant role in physical as well as digital security. Physical security applications include homeland security, surveillance applications, identity authentication, and so on. Digital security implies protecting digital data. Techniques like digital watermarking, network security, and steganography enable digital security. [ABSTRACT FROM AUTHOR]

Impact of supplemental UV-B (sUV-B) has been investigated on photosynthetic pigments, antioxidative enzymes, metabolites, and protein profiling of radish plants under realistic field conditions. Exposure of sUV-B leads to oxidative damage in plants. However, plants possess a number of UV-protection mechanisms including a stimulation of antioxidant defense system. It caused alteration in reactive oxygen species metabolism primarily by decreasing catalase activity vis-à-vis enhanced activities of other enzymatic (superoxide dismutase, ascorbate peroxidase, and glutathione reductase) and non-enzymatic (ascorbic acid) antioxidants. Qualitative analysis of samples also showed significant reductions in photosynthetic pigments and protein content. After sUV-B exposure, protein profile showed differences mainly at eight points—126.8, 84.8, 71.9, 61.5, 47.8, 40.6, 38.9, and 17.5 kDa, whereas protein(s) of 38.9 kDa showed increment. Results of the present investigation clearly showed the adverse effect of sUV-B on total biomass at final harvest. [ABSTRACT FROM AUTHOR]