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Primary umbilical endometriosis is a rare benign gynaecological disorder caused by ectopic endometrial tissue in the umbilicus. The disease typically manifests as a painful umbilical nodule associated with cyclic bleeding or discharge during menstruation. Here, a 46-year-old woman with low parity and without previous history of abdominal surgery has been reported who presented with cyclical per-umbilical bleeding and pain in the umbilical region during menstruation without any palpable umbilical nodule. The patient responded well to initial medical management, but symptoms recurred on discontinuation of the medication. Though clinically, there was no palpable nodule, and ultrasonography revealed a hypoechoic lesion in the lower umbilical region suggestive of endometrial deposition. On resuming medical management, her symptoms abated. J Bangladesh Coll Phys Surg 2023; 41: 173-176

This study has a novel approach to capture the attitude of Bottom of the Pyramid (BoP) consumers towards Packaging Influenced Purchase (PIP) during the Covid-19 crisis. Over the years, BoPs consumers have established themselves as an emerging market with ample growth and opportunities. The authors suggested a Multiple-Criteria Decision-Making (MCDM) based framework to assist marketers in targeting both urban and rural BoP consumers regarding PIP. Packaging elements and influence of family, extended family, peers have been included in the framework for gaining in-depth understanding. With a sample size of 100 from West Bengal, this focus group-based study can fulfil the BoP literature’s existing prominent research gap. Results indicate the difference in attitude for urban and rural BoPs towards PIP during this crisis. The fusion of MCDM based approach and relevant machine learning-based technique aims to assist marketers in identifying, formulating, and redefining an action plan.

In this article, an unsupervised domain adaptation strategy has been investigated using a deep Siamese neural network in scene-level land cover classification using remotely sensed images. At the onset, the soft class label and probability scores of each target sample have been obtained using a pretrained model of a deep convolutional neural network. Thereafter, a semiautomatic threshold selection algorithm along with a graph-based approach has been explored to obtain the ``most-confident'' target samples. Furthermore, the deep Siamese network has been incorporated by training the source and ``most-confident'' target samples to generate the classwise cross domain common subspace. To assess the effectiveness of the proposed framework, experiments are carried out using three aerial image datasets. The results are found to be encouraging for the proposed scheme in comparison with the other state-of-art techniques.

Wireless body area networks (WBANs) are becoming a popular and convenient mechanism for IoT-based health monitoring applications. Maintaining the energy efficiency of the nodes in WBANs without degrading network performance is one of the crucial factors for the success of this paradigm. Obtaining routes for data packets should be a dynamic decision depending on network conditions. Consequently, in this paper, a novel cost-based routing protocol ZITA has been proposed that addresses primary issues of WBAN routing, such as timeliness, link quality, temperature control, and energy efficiency while finding the next hop for data packets. Zipf’s law is applied for relay selection to ensure the distribution of forwarding load among the potential relays. ZITA controls the transmission power level adaptively in order to cope with the time-varying channel conditions following multi-hop architecture. The protocol is simulated and the results show that the protocol gives better performance in terms of data received by the sink, heat dissipation of the wearable as well as implantable sensor nodes, and load sharing among relay nodes.

This work proposes a stochastic model of the coordinator units of each wireless body area network (WBAN) in a multi-WBAN scenario. In a Smart Home environment, multiple patients can come in vicinity of each other while each of them are wearing a WBAN configuration for monitoring of body vitals. Thus, while multiple WBAN coexists, the individual WBAN coordinators require adaptive transmission strategies in order to balance between maximizing the likelihood of data transmission and minimizing the chances of packet loss due to inter-BAN interference. Accordingly, the proposed work is divided into two phases. In the offline phase, each WBAN coordinator is modelled stochastically and the problem of their transmission strategy has been modeled as a Markov Decision Process(MDP). The channel conditions, buffer status that influence the transmission decision are taken to be the state parameters in MDP. The formulation is solved offline, prior to deployment of the network to find out the optimal transmission strategies for various input conditions. Such transmission policies for inter-WBAN communication are then deployed in the coordinator nodes in the post deployment phase. The work is simulated using Castalia and the results demonstrate the robustness of the proposed scheme in handling both favorable and unfavorable operating conditions.

This paper proposes effective communication strategies for Wireless Body Area Networks (WBANs) that consist of wearable or implantable sensor nodes placed in, on/around the human body to send body vitals to a sink. The main research challenges for communication strategy formulation include limited energy resources and varying link conditions. Though energy harvested sensor nodes partially address the problem of energy efficiency, finding an optimal balance between the energy constraint of the nodes and communication reliability is still challenging. Since data loss in such networks may prove to be fatal, it is important to investigate the problem prior to deployment and come up with effective communication strategies for initiating post-deployment operations. Hence, in this paper, the nodes are stochastically modeled as a Markov Decision Process. There is a need to adapt to the changing ambient conditions through exploration and exploitation. So, a modified Q-learning technique is proposed for post-deployment decision-making by the WBAN nodes subject to the dynamic ambient conditions. The effectiveness of the proposed strategy is validated through extensive simulation and compared with state-of-the-art works. The performance of the proposed approach is also verified with a real-life dataset. The results demonstrate that around 90% successful data delivery to sink could be made with the proposed scheme in the real-life scenario.

Electoral clientelism or vote buying has been regarded as undermining democratic institutions and weakening the accountability of the state towards its citizens, especially the poor. Social identity as a form of political mobilization may contribute to this, enabling support to be won with clientelist transfers. This paper reports data from a novel laboratory experiment designed to examine whether clientelism can be sustained as a political strategy, and whether identity impacts the nature or efficacy of clientelism. Specifically, we design a voting and leadership game in order to examine whether individuals vote for clientelist allocations by a leader even at the expense of more efficient and egalitarian allocations. We find group identity does not significantly impact the prevalence of clientelist plans. Leaders are more likely, however, to choose allocations that provide fewer benefits (lower rents) to themselves when they are part of the majority in-group than when they are in the minority.

The paper aims at expanding the TPB model to examine the relationship among attitude, subjective norms, perceived behavioural control, motivation, and behavioural intention as well as the extent to which satisfaction mediates the relationship in the link between TPB predictive constructs along with motivation and behavioural intention. A questionnaire survey technique was administered on domestic tourists and subsequently, structural equation modeling using SmartPLS 3.0 was subjected to test the synthesized relationship among variables. The study findings support that attitude, subjective norms, and motivation significantly influence customer satisfaction and behavioural intention whereas perceived behavioural control does not. In addition, the study indicates that customer satisfaction mediates in the link between attitude, subjective norms, and motivation, and behavioural intention. However, despite the availability of international tourists, the study selected only domestic tourists that are liable for future research. Moreover, the findings of the study will enhance the understanding of hospitality researchers and managers what will significantly influence customer satisfaction and behavioural intention.

In this manuscript, a neuro-fuzzy domain adaptation (DA) technique has been proposed for a multi-level incremental transformation of the source-target features to find an intermediate space with lesser cross-domain distribution difference at each level. In the present investigation, the unsupervised layers of a stacked auto-encoder are used for granular transformation of the weighted samples (or group of samples) at every level. Out of the three, the first two layers of the stack involve unsupervised weighted transformation of source-target samples without using any labelled information from the target domain. After that, a fuzzy membership-based transfer learning scheme has been used to capture the target-distinctive information thereby facilitating a selective transformation between matching source-target sample groups in the third level. Finally, more accurate class-label predictions for the unknown target samples are obtained using the labelled source samples in the transformed (source-target) feature space. To validate the effectiveness of the proposed approach, experimentation has been carried out using samples collected from various multi-spectral satellite images captured over various source and target regions of India. The attained results show superior performance in target class prediction for the proposed DA scheme when compared to other state-of-the-art DA techniques for land-cover classification.

Background: Abnormal uterine bleeding is common and important problem in perimenopausal women which has diverse etiology. Among different diagnostic procedures, Dilatation and Curettage (D&C) with histopathology of endometrium has an important role in the detection of underlying cause in perspective of its availability, low cost, and high diagnostic yield. The objective of diagnostic curettage in perimenopausal women with abnormal uterine bleeding is to detect endometrial abnormality. Methods: It was a hospital based cross-sectional study among fifty patients of 45 to 60 years old with history of perimenopausal bleeding, attending the inpatient and outpatient department of Sir Salimullah Medical College and Mitford Hospital, Dhaka. The cases were selected according to inclusion and exclusion criteria. Routine investigations and pelvic ultrasonograpy were done. Endometrial biopsy were taken by Diagnostic D & C. Chi-square test was used as test of statistical significance. Statistical Package of Social Science Software (SPSS, version 16.0) was used for statistical analysis of data. Results: Common histopathological pattern identified were endometrial hyperplasia (32%), and polyp (24%). Proliferative endometrium was found in 16% cases, secretory endometrium in 12%, endometrial carcinoma in 6%, endometritis in 4 % and atrophic endometrium in 6% patients. Conclusion: Endometrial biopsy is an essential step for all cases of perimenopausal and postmenopausal abnormal uterine bleeding to rule out malignancy. Bangladesh J Obstet Gynaecol, 2020; Vol. 35(1): 7-14

Background: microRNAs are small non-coding RNAs which inhibit translational and post-transcriptional processes whereas long non-coding RNAs are found to regulate both transcriptional and post-transcriptional gene expression. Medicago truncatula is a well-known model plant for studying legume biology and is also used as a forage crop. In spite of its importance in nitrogen fixation and soil fertility improvement, little information is available about Medicago non-coding RNAs that play important role in symbiosis. Objective: In this study we have tried to understand the role of Medicago ncRNAs in symbiosis and regulation of transcription factors. Methods: We have identified novel miRNAs by computational methods considering various parameters like length, MFEI, AU content, SSR signatures and tried to establish an interaction model with their targets obtained through psRNATarget server. Results: 149 novel miRNAs are predicted along with their 770 target proteins. We have also shown that 51 of these novel miRNAs are targeting 282 lncRNAs. Conclusion: In this study role of Medicago miRNAs in the regulation of various transcription factors are elucidated. Knowledge gained from this study will have a positive impact on the nitrogen fixing ability of this important model plant, which in turn will improve the soil fertility.

Biogas is one of the promising futuristic renewable energy sources with enormous market potential. However, the presence of CO2 lowers down the calorific value of biogas. Hence, various biogas upgradation technologies are under intense investigation to increase the methane content to the desired level. This study reports on enhancing methane content in biogas through CO2 sequestration into acetic acid via microbial electrosynthesis (MES) process. The previously enriched mixed chemolithoautotrophic microbial culture dominated by Acetobacterium spp. used CO2 present in the biogas as the sole carbon source. After establishing a stable performing biocathode at a fixed cathodic potential of −1 V (vs. Ag/AgCl) through batch mode operation, biogas was fed continuously at different feed rates, viz., 0.5, 0.3, and 0.2 ml/min to the cathode chamber. The highest feed rate of 0.5 ml/min was least effective both for methane content increment (from 61 ± 3% to 86 ± 2%) and acetic acid titer (1.5 ± 0.5 g/L; 0.107 ± 0.02 g/L/d.). In comparison, the lowest flow rate of 0.2 ml/min was the most effective for the intended process (methane upgradation from 62 ± 7% to 93 ± 3% and acetic acid titer 3.4 ± 0.6 g/L produced at 0.24 ± 0.04 g/L/d rate). Both acetic acid bioproduction and biogas upgradation occurred best at an Ecell of 3.3 ± 0.35 V at the low feed rate. A maximum of 84 ± 7%, 57 ± 10% and 29 ± 2% coulombic, carbon and energetic efficiencies, respectively, were achieved in acetic acid. Cyclic voltammograms of biocathodes revealed the decrease in hydrogen evolution potential and increased bioelectrocatalysis, thereby suggesting the contribution of microbes in the process. Acetobacterium, which is known for CO2 fixation, was found to be the dominant microbial genus in biogas fed reactors. The demonstrated approach not only offers the advantage of obtaining two products, one in the bulk phase and the other in the off-gas, it also validates the applicability of the bioelectrochemical biogas upgradation technology.

Polyaniline supported palladium catalyst was applied in the reductive degradation of organic dyes such as Methylene Blue, Rhodamine B, and Methyl Orange in presence of sodium borohydride as an environmental-friendly approach. Role of pH, catalyst amount, and catalyst support were investigated thoroughly to achieve complete and efficient degradation within few minutes under ambient condition. Heterogeneous nature of the catalyst allowed easy recovery by centrifugation and the catalyst was recycled for five cycles with slightly reduced activity. Recovered catalyst was characterized by ICP-AES and TEM and a slight decrease in the activity of the catalyst was attributed to the agglomeration of the palladium nanoparticles.

Background: In developing countries like India acute respiratory infection (ARI) contributes in child mortality upto 75% and out of 10, 7 deaths are due to ARI. The knowledge of the mothers towards the disease is a significant determinant of child’s health.Methods: A descriptive cross-sectional study included 100 mothers of children 0-5 years admitted in pediatric ward and postnatal ward in selected hospital Siliguri during the year 2022 in the month of March. Data was collected using structured interview method.Results: 20% of mothers have good knowledge in prevention and 33% had good knowledge in management of ARI.Conclusions: As the leading cause of death among children, knowledge assessment about ARI among the mothers is very important, which helps for better understanding of the intensity of the problem.

In this article, a domain adaptation (DA) technique using artificial neural networks based classifiers has been proposed using two-level cluster mapping technique by integrating the common data transformation and transfer learning approaches in a single framework. Here, after applying self-organizing feature mapping based clustering technique, a semi-automatic threshold selection mechanism is used to separate out most-confidently paired source–target clusters (i.e. the most similar ones) and alien target clusters (i.e. non-similar ones). Moreover, this strategy makes the proposed technique eligible to apply the transfer learning mechanism. Thereafter, the samples from the most confidently paired target clusters are transformed in terms of the corresponding source clusters using an auto-encoder. Here, the labelled samples are collected from the corresponding source clusters for the paired target clusters; whereas the transfer learning technique is used to select labelled samples from the alien target clusters. To assess the effectiveness of the proposed DA approach, experiments are conducted on the three source–target datasets and the results are compared with other state-of-the-art techniques. Results are also found to be encouraging for the proposed technique.

Wireless Body Area Networks enable a new trend of proactive health care exploiting battery-powered wearable and/or implantable sensor nodes. The transmission of biomedical signals consumes most of the scarce energy resources in a WBAN. State-of-the-art transmission mechanisms either focus on energy efficiency or timely data delivery. However, an optimal balance between the two subject to various ambient conditions is a necessity. Accordingly, in this paper, an optimal data transmission policy is formulated at the node level, considering node lifetime, node heating, and reliable data delivery for various ambient conditions. The proposed model is divided into two phases. In the pre-deployment phase, each node is stochastically modeled following the Markov Decision Process. The formulation involves a reward function that attempts a trade-off between energy consumption and reliable data delivery. Then, a genetic algorithm is applied to obtain a set of optimal transmission decisions for each node based on the reward function for a range of ambient conditions. Finally, in the deployment phase, the results of the previous phase are mapped to an algorithm to be executed at WBAN nodes. The work is experimentally evaluated via simulation and compared with the state-of-the-art approaches. The performance is also observed based on the real-life dataset. Results demonstrate that around 85–90% of data packets could be delivered to the sink with minimal energy so that the nodes hardly get heated.

Plants being sessile are always exposed to various stresses including biotic and abiotic stresses. Some of these stresses are genotoxic to cells causing DNA damage by forming lesions which include altered bases, cross-links, and breaking of DNA strands, which in turn hamper the genomic integrity. In order to survive through all these adverse conditions, plants have evolved different DNA repair mechanisms. As seen from the mammalian system and different human diseases, various microRNAs (miRNAs) can target the 3′-untranslated region of mRNAs that code for the proteins involved in DNA repair pathways. Since miRNAs play an important role in plant cells by regulating various metabolic pathways, it can also be possible that miRNAs play an important role in DNA repair pathways too. However, till date, only a handful of plant miRNAs have been identified to play important role in combating genotoxic stresses in plants. Limitation of information regarding involvement of miRNAs in DNA repair as well as in ROS scavenging prompted us to gather information about plant miRNAs specific for these tasks. This mini-review aims to present pertinent literature dealing with different genotoxic stresses that cause genome instability as well as plant specific responses to survive the damage. This is intertwined with the involvement of miRNAs in genotoxic stress in plants, challenges of applying miRNAs as a tool to combat DNA damage along with ways to overcome these challenges, and finally, the future prospective of these understudied aspects.

The aim of this article is to obtain a simple and efficient estimator of the index parameter of symmetric stable distribution that holds universally, i.e., over the entire range of the parameter. We appeal to directional statistics on the classical result on wrapping of a distribution in obtaining the wrapped stable family of distributions. The performance of the estimator obtained is better than the existing estimators in the literature in terms of both consistency and efficiency. The estimator is applied to model some real life financial datasets. A mixture of normal and Cauchy distributions is compared with the stable family of distributions when the estimate of the parameter &alpha, lies between 1 and 2. A similar approach can be adopted when &alpha, (or its estimate) belongs to (0.5,1). In this case, one may compare with a mixture of Laplace and Cauchy distributions. A new measure of goodness of fit is proposed for the above family of distributions.

Healthcare data protection is very much required in the present time due to the rate of growth in data volume and sensitivity of the data. The data is collected through EHR, diagnostic image like scan or x-ray or IoT wearable devices which has sensors and shared with stakeholders who can analyse the data and use it for betterment of the processes. Benefits of medical data sharing among the entities makes it even more vulnerable. In this paper we have studied and analysed few blockchain based methods of securing healthcare data which is stored in cloud. The methods studied is successful in keeping the data safe from unauthorized access.

Cancer has remained one of the most indomitable conundrums for scientists over centuries due to its multifarious etiology. While improved therapeutic and diagnostic approaches have commendably augmented the rate of survival of cancer patients, a holistic riddance from the ailment is still implausible. Hence, further explorations to scout for novel strategies of cancer therapy and diagnosis are necessary. Theranostics (amalgamation of therapy and diagnostics) has emerged as one of the avant-garde strategies, which provides a two-pronged advantage in cancer management. This integrative approach has found immense relevance in light of nanotechnology. Nanoparticles can be customized (loaded with a mélange of therapeutic drugs and diagnostic probes) to develop theranostic properties, thereby constructing nanotheranostic agents. These nano-composites are lucrative tools for cancer cell obliteration and simultaneous monitoring of the drug action, and can also be tailored for targeted drug delivery. Nanotheranostic agents have emerged as a prudent ploy for synchronized cancer intervention and detection of the 'route and reach' of the drugs. In this review, we discuss the diversified state-of-the-art facets of theranostic nanoparticles, including various nanoparticle-based platforms as well as the plethora of reported therapeutic drugs, aptamers, markers and diagnostic molecules that have found use in the precincts of nanotheranostics.

Non-coding RNAs (ncRNAs) are found to be important regulator of gene expression because of their ability to modulate post-transcriptional processes. microRNAs are small ncRNAs which inhibit translational and post-transcriptional processes whereas long ncRNAs are found to regulate both transcriptional and post-transcriptional gene expression.Medicago truncatulais a well-known model plant for studying legume biology and is also used as a forage crop. In spite of its importance in nitrogen fixation and soil fertility improvement, little information is available about Medicago ncRNAs that play important role in symbiosis. To understand the role of Medicago ncRNAs in symbiosis and regulation of transcription factors, we have identified novel miRNAs and tried to establish an interaction model with their targets. 149 novel miRNAs are predicted along with their 770 target proteins. We have shown that 51 of these novel miRNAs are targeting 282 lncRNAs. We have analyzed the interactions between miRNAs and their target mRNAs as well as their targets on lncRNAs. Role of Medicago miRNAs in the regulation of various transcription factors were also elucidated. Knowledge gained from this study will have a positive impact on the nitrogen fixing ability of this important model plant, which in turn will improve the soil fertility.

The exponential growth in database of bio-molecular sequences have spawned many approaches towards storage, retrieval, classification and analyses requirements. Alignment-free techniques such as graphical representations and numerical characterisation (GRANCH) methods have enabled some detailed analyses of large sequences and found a number of different applications in the eukaryotic and prokaryotic domain. In particular, recalling the history of pandemic influenza in brief, we have followed the progress of viral infections such as bird flu of 1997 onwards and determined that the virus can spread conserved over space and time, that influenza virus can undergo fairly conspicuous recombination-like events in segmented genes, that certain segments of the neuraminidase and hemagglutinin surface proteins remain conserved and can be targeted for peptide vaccines. We recount in some detail a few of the representative GRANCH techniques to provide a glimpse of how these methods are used in formulating quantitative sequence descriptors to analyse DNA, RNA and protein sequences to derive meaningful results. Finally, we survey the surveillance techniques with a special reference to how the GRANCH techniques can be used for the purpose and recount the forecasts made of possible metamorphosis of pandemic bird flu to pandemic human infecting agents.

We have designed seven organic diradicals with polyacene couplers to show the effect of the configuration, aromaticity [estimated with the help of Nucleus Independent Chemical Shift (NICS(0) and NICS(I)), and Harmonic Oscillator Model of Aromaticity (HOMA)] and HOMO–LUMO gap of the couplers on the exchange coupling constant of the diradicals. It has been observed that the linear polyacenes are less aromatic compared to the corresponding angular ones. We have correlated aromaticity indexes NICS and HOMA to explain the change of aromaticity for structures having the same number of carbon and hydrogen atoms and rings. The diradicals with linear couplers manifest stronger exchange coupling constants compared to those with angular couplers. It has been found that the NICS value cannot adequately address the aromaticity of the polyacenes, whereas the HOMA value can reliably account for the observations. Here, we have found the HOMO–LUMO gap is the determining factor for the extent of the magnetic exchange coupling constant in the diradicals. It has been found that not only the energy value of the LUMO, but also its occupation number and spatial position, play an important role in magnetic exchange in diradicals. Thus, the role of the LUMO in magnetic exchange has been firmly established through this work. The magneto-structural correlation has also been studied to establish the mechanism of magnetic interaction.

Group activity recognition in sports is often challenging due to the complex dynamics and interaction among the players. In this paper, we propose a recurrent neural network to classify puck possession events in ice hockey. Our method extracts features from the whole frame and appearances of the players using a pre-trained convolutional neural network. In this way, our model captures the context information, individual attributes and interaction among the players. Our model requires only the player positions on the image and does not need any explicit annotations for the individual actions or player trajectories, greatly simplifying the input of the system. We evaluate our model on a new Ice Hockey Dataset. Experimental results show that our model produces competitive results on this challenging dataset with much simpler inputs compared with the previous work.

In this article, a novel approach using ensemble of semi-supervised classifiers is proposed for change detection in remotely sensed images. Unlike the other traditional methodologies for detection of changes in land-cover, the present work uses a multiple classifier system in semi-supervised (leaning) framework instead of using a single weak classifier. Iterative learning of base classifiers is continued using the selected unlabeled patterns along with a few labeled patterns. Ensemble agreement is utilized for choosing the unlabeled patterns for the next training step. Finally, each of the unlabeled patterns is assigned to a specific class by fusing the outcome of base classifiers using some combination rule. For the present investigation, multilayer perceptron (MLP), elliptical basis function neural network (EBFNN) and fuzzy k-nearest neighbor (k-nn) techniques are used as base classifiers. Experiments are carried out on multi-temporal and multi-spectral images and the results are compared with the change detection techniques using MLP, EBFNN, fuzzy k-nn, unsupervised modified self-organizing feature map and semi-supervised MLP. Results show that the proposed work has an edge over the other state-of-the-art techniques for change detection.

In this paper, a change detection technique using neural networks in active learning framework is proposed under the scarcity of labeled patterns. In the present investigation, two variants of radial basis function neural networks and a multilayer perceptron are used as learners. Instead of training the network (or ensemble of networks) with randomly collected labeled patterns, in the proposed work, the network (or ensemble of networks) is iteratively trained with label patterns, collected using the query functions. Here, two query selection strategies are used: uncertainty sampling and query-by-committee. In this way, the most informative set of labeled patterns can be iteratively generated by querying. To evaluate the effectiveness of the proposed approach, the experiments are conducted on multi-temporal remotely sensed images. The results obtained using the proposed active learning framework are found to be encouraging.

In the present article, semi-supervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In the proposed methodology, training of the MSOFM network is initially performed using only a few labeled patterns. Thereafter, the membership values, in both the classes, for each unlabeled pattern are determined using the concept of fuzzy set theory. The soft class label for each of the unlabeled patterns is then estimated using the membership values of its K nearest neighbors. Here, training of the network using the unlabeled patterns along with a few labeled patterns is carried out iteratively. A heuristic method has been suggested to select some patterns from the unlabeled ones for training. To check the effectiveness of the proposed methodology, experiments are conducted on three multi-temporal and multi-spectral data sets. Performance of the proposed work is compared with that of two unsupervised techniques, a supervised technique and two semi-supervised techniques. Results are also statistically validated using paired t-test. The proposed method produced promising results.

Traffic signs should be accurately identified in order to prevent vital road accidents and secure lives. The objective of this paper is to detect the warning traffic signs and recognize the message it is designed to convey. This system is based on extracting the warning sign from the traffic scene by Windowed Hough Transform. Next Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of triangular object and finally SVM classifier is applied to train the HOG features. To summarize, the system first detects the warning traffic sign in the first place, specifies whether the detected sign is a warning sign, and then determines the meaning of the symbol inside it. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 327 images which contain 292 warning traffic sign and 35 other types of traffic signs. It was found that the accuracy of recognition was approximately 94% which indicates clearly the high robustness targeted by this system.

Chloromethylated polystyrene supported ionic liquid is used as catalyst in the aldol-type coupling of a variety of aldehydes with ethyl diazoacetate in water. Moderate to excellent yields of the corresponding α-diazo-β-hydroxy esters were obtained. The catalyst was separated by filtration and reused in five consecutive cycles without any appreciable loss of activity.

Polyaniline supported CuI catalyst was prepared, characterized and effectively used in the N -arylation of N (H)-heterocycles and benzylamines with aryl halides and arylboronic acids and in aza-Michael reaction of amines with activated alkenes. The catalyst was recovered by simple filtration and reused for several cycles with consistent activity.

Layered double hydroxides supported rhodium(0) catalyst was prepared, characterized and effectively used as a catalyst in the Heck, Suzuki and Stille cross-coupling of haloarenes. The catalyst can easily be removed by simple filtration and reused for several cycles with consistent activity.

Silica tethered copper complexes are effectively used as reusable catalysts in the N-arylation of N(H)-heterocycles and benzyl amines with aryl halides and arylboronic acids. N-Arylated products were isolated in good to excellent yields, demonstrating the versatility of the protocol. Moreover the Cat2 was easily recovered by simple filtration and reused for several cycles with consistent activity.