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Health Monitoring is the process of identifying, locating and detecting the amount of damage that are occurring in the structure in early stage to prevent the unexpected failures. Structural health monitoring (SHM) using self-sensing material is emerging and it is preferred research area in structural engineering field. This self-sensing property for material is achieved by adding piezoresistive materials into them. These piezo resistivity makes the materials self-sensing by indicating a detectable change in their electrical resistivity with applied stress or strain and make them useful for health monitoring of structures. Many studies shows that nano materials possess greater conductivity properties which can be used as smart materials. A recent development shows cement matrix can be made self-sensing be by addition carbon fibre and nano materials such as graphene, multiwall carbon nanotubes (MWNCT), nano fibres. In the proposed work optimization of nano materials dosage to achieve conductivity property for cement matrix is carried out. The experimental study is carried out by preparing the cement nano composite sensor by adding different proposition of multiwall carbon nanotubes (MWCNT) and carbon fibre, which act as nano filler in the cement based nano composite sensor. Copper wire was used as electrode material in the sensor. The size of specimen sensor was fixed to 80 mm ×80 mm ×50 mm with three 10 mm diameter tube was the filler is filled with copper electrode. In the last step, electrotechnical test for nano sensor with the optimized dosage of carbon fibre and multiwall carbon nanotubes (MWNCT) were embedded in the beam and column of size 150 mm ×150 mm ×700 mm to evaluate the conductivity properties of the materials. The obtained results for optimum dosage of nano materials is then verified with full factorial method and two factorial method of design of experiment (DoE) which are the statistical methods of optimization.

Multi detector computed tomography (MDCT) scan of paranasal sinuses has become mandatory for all patients undergoing functional endoscopic sinus surgery. It depicts the anatomical variations in much simpler way and acts as a roadmap for endoscopic sinus surgery. In this study the maximum of participants were in the age group of 41- 50 years (32 %). The mean age of the study participants was observed to be 43.8 ± 15.6 years. HRCT PNS results shows nasal septum was found to be the most common anatomical variant (DNS- 94%). Concha Bullosa in right side were seen in 37.5% and left side were 21.9% (total unilateral cases- 59.4%) and bilateral Concha Bullosa were seen in 40.6% of the cases. Agger nasi were found to be present in 58% of the CRS cases among which right AN was 41%, left side was 30.8%, and bilateral AN were seen in 28.2% of the cases and Paradoxical Middle Turbinate were seen in 42% of cases in our study. Medialised uncinate was seen in 22% of the cases.

Recent advances in material science and self-sensing technology have enabled the development of cement-based nanocomposite sensors that detect the damage on their own by exhibiting piezoelectric properties corresponding to the response of the structures. The present study involves the development and implementation of these sensors in the structural components and monitors the response by correlating the piezoelectric properties of the sensors with the stress-strain response to identify the potential damage. For this purpose, the carbon fiber (CF) and multiwalled carbon nanotubes (MWCNT) are used as nanofiller in the cementitious matrix to develop the self-sensing sensors. These sensors possess high strength, large elastic modulus, and piezo resistivity properties, which make them promising smart sensor materials for structural health monitoring applications. Two example applications involving the beam and column as the structural components are used for the experimentation. After embedding the sensors into the structural components, the response is evaluated in the form of resistance versus load. The self-sensing sensor is capable of detecting the nanostructural cracks during the loading of the system. Based on the severity of loading, the resistivity will indicate the damage state of the structural component which helps in deciding the suitable retrofitting strategies for the maintenance of the structural component to elongate the service life of the structures. The developed sensors also possess good mechanical and electrical properties and hence they have promising characteristics for real-time health monitoring applications.

Smart Hybrid Nano composite sensors are developed to diagnosing their own state of strain in which Nanomaterials used as conducting filler. Nanomaterial like carbon fibre (CF), multiwall carbon nanotube (MWNCT), and graphene have a unique property that they can change applied strain into electric resistance. Hence, they can be used as conducting filler in the cement matrix. The present study mainly concerns with developing smart hybrid Nano composite cement based sensors and implementation of these sensors for structural health monitoring by embedding sensors into structural components. Two different combinations of sensors are developed by inserting Nano materials (0.25 and 0.5% weight of cement of MWCNT, CF, and Graphene) into cement mortar matrix. The insertion of carbon Nano filler into base material makes them into sensitivity to mechanical modification. From the electromechanical test it indicates that both combination sensors show good strain sensing with respect to applied load and also it is observed that is increased in flexural strength about 30.08% for 0.5% combination. A flexural and compression test was conducted by embedding Nano composite cement based sensors into structural components. The test results shows the variation in resistance for both compression and flexural loading which indicates into self-sensing property of structural element by embedding sensors into them, hence it will be helpful in monitoring the structure.

Background: Goat farming is highly viable and Virudhunagar district of Tamil Nadu has 10 blocks out of which three blocks are situated in the foot hills of Western Ghat. There are no data available regarding Socio economic status of Goat farmers residing in foothill region of western ghat. Hence, this study was aimed to study the Socio economic status of Goat farmers in fringe villages of Western Ghat in Virudhunagar district, Tamil Nadu.Methods: Three blocks are situated in the foot hills of Western Ghat. In each block, three fringe villages near to the Western Ghat were selected randomly. In each village, 30 goat farmers were selected randomly, a total of 270 farmers were selected randomly and data were collected by personal interview through pre tested questionnaire. Result: Most of the goat farmers were between 35 to 50 years of age, owned house, lived as nucleus family, 86% of them were landless. Most of them were illiterate and not exposed to training centre. Hence, it is suggested that government or any other agency take necessary steps to expose them to scientific farming which will improve their socio economic status.

It is important to maintain secrecy of one’s valuable data when it is loaded in the Internet. Unintended parties can view the data, resulting in breach of confidentiality. Another serious concern is the possibility of misuse of data to the advantage of an unintended party. To overcome these problems, this paper presents an algorithm developed and implemented to make the data that is uploaded to the servers, pseudo anonymous (pseudonymous). Typically, user-sensitive information exists as either texts or images. Hence, such data is used as input to the developed algorithm. From the textual data, the algorithm can detect keywords called tags, such as names, places, financials, and replace the original tag with newly created false tags. Detection of the tags is done by the existing natural language processing (NLP) algorithms. For image input, the algorithm detects face and replaces it with a falsified one. This is done by using generative adversarial networks (GANs). Further, the original or true image is hidden in the falsified image using SteganoGAN. This image will be sent over the network. At the receiver, both anonymized text and image data are de-anonymized as per the developed algorithm.

The energy consumption pattern and greenhouse gas (GHG) emission of any rice production system is important to know the sustainability of varied cultivation and establishment technique. This study was conducted to determine the energy use pattern, GHG emission and efficiency of rice farms in puddled transplanted (PTR, rainfed) and direct-seeded rice (DSR, irrigated) production systems in Karnataka, India. The energy indices and GHG emission of different input and output in a rice production system were assessed by using energy and carbon equivalence. The efficiency of PTR and DSR farms were identified using data envelopment analysis (DEA) and energy optimization was ascertained. The key finding was excessive use of non-renewable energy inputs was observed for the PTR (92.4%) compare to DSR (60.3%) methods. The higher energy use efficiency (7.3), energy productivity (0.3 kg MJ&minus, 1) and energy profitability (6.3) were mainly attributed to the large decrease in energy inputs under DSR. The DEA showed efficiency for 26 PTR farms in comparison for 87 DSR farms. The mean technical efficiency value highlighted the scope for saving energy by 6% and 2% in PTR and DSR, respectively and showed an economic reduction of $405.5/ ha with PTR versus $163.3/ha with the DSR method if these inefficient farms perform efficiently. The GHG emissions revealed that the total emissions for PTR versus DSR production caused by on-farm emissions were 86% and 65%, respectively. The DSR method also had a higher carbon efficiency ratio and carbon sustainability index (10.1 and 9.1, respectively). Thus, adoption of DSR method is imperative for reduction of energy consumption and GHG emissions to achieve the carbon sustainability.

In this paper, we study the conformal vector elds on a class of Finsler metrics. In particular Finsler space with special (α, β)- metric `F =\alpha +\frac{\beta^2}{\alpha} ` is dened in Riemannian metric α and 1-form β and its norm. Then we characterize the PDE's of conformal vector elds on Finsler space with special (α, β)- metric.

Fighter aircraft recognition is important in military applications to make strategic decisions. The complexity lies in correctly identifying the unknown aircraft irrespective of its orientations. The work reported here is a research initiative in this regard. The database used here was obtained by using rapid prototyped physical models of four classes of fighter aircraft: P51 Mustang, G1-Fokker, MiG25-F, and Mirage 2000. The image database was divided into the training set and test set. Two feature sets, Feature Set1 (FS1) and FS2, were extracted for the images. FS1 consisted of 15 general features and FS2 consisted of 14 invariant moment features. Four multilayered feedforward backpropagation neural networks were designed and trained optimally with the normalized feature sets. The neural networks were configured to classify the test aircraft image. An overall accuracy of recognition of 91% and a response time of 3 s were achieved for the developed automatic fighter aircraft model image recognition system.

This paper presents an robust and efficient unsupervised method for ECG arrhythmia classification. The proposed method consists of two steps: feature selection and clustering. In proposed method, initially the input ECG data is fed into feature selection method to reduce the dimensionality. In this paper, three well known feature selection methods i.e., Principal Component Analysis, Linear Discriminant Analysis (LDA) and Regularized Locality Preserving Indexing (RLPI) are employed. Further, the reduced data is clustered using a Robust Spatial Kernel FCM (RSKFCM) method. RSKFCM is variant of FCM method which uses Gaussian kernel as distance metric and incorporates neighborhood information. To evaluate the proposed method the experimentation’s are conducted on UCI arrhythmia dataset and compared the results with traditional FCM and Kernel Fuzzy C-Means (KFCM). Extensive experimental results show that, the proposed RSKFCM method with RLPI feature selection method outperforms other methods in comparison.

Context: Although spinal anesthesia (also known as subarachnoid block [SAB]) is used widely for inguinal hernia repair, the paravertebral block (PVB) that produces unilateral, segmental analgesia is used with a high success rate in inguinal hernia repair. Aims: The aim of the study was to compare SAB and PVB in inguinal hernia repair, in terms of the duration of postoperative analgesia and adverse events. Settings and Design: This was a prospective, randomized, controlled double-blind study. Methods: This study was done on 60 male patients of American Society of Anesthesiology (ASA) I and II. Patients were categorized into 30 in each group, either to receive PVB block at two levels T10 and L1 using 15 mL and 5 mL of 0.5% bupivacaine and 1 μg.kg−1 of buprenorphine or SAB with 12.5 mg of 0.5% hyperbaric bupivacaine injected intrathecally. Statistical Analysis Used: SPSS 18.0 and R version 3.2.2 were used for analyzing the data. Categorical measurements were presented in number (%) and analyzed using Chi-square/Fischer's exact test. Continuous measurements were analyzed using Student's t-test. Results: Age, weight, height, and ASA status were comparable in both the groups. In the PVB group, eight patients had failure of block. Hemodynamic responses, time to first analgesia and ambulation, time required to perform the block, Bromage score, satisfaction score, failure rate, and intra- and postoperative drugs used showed a statistically significant difference between the groups (P < 0.001). Conclusion: PVB is not a sole anesthetic technique due to a higher failure rate and increased intraoperative fentanyl requirement but has advantages such as prolonged analgesia, stable hemodynamics, and early ambulation.

This paper presents a hybrid model consisting of hardware, software subsystems and publicly available trained feature sets. The developed hybrid model is useful in automated contactless collection and analysis of employees’ and visitors’ data in an organization especially during pandemic situations to ensure biosecurity. Such data include temperature and face mask status. If the set norms are not satisfied, the entry into the premises will be restricted or denied. The status is also updated in the corresponding record in the organization database. The hardware subsystem includes an arduino nano, sensors and audio visual alarms. The software subsystem was developed using OpenCV in Python and VSCode editor. Both offline and real time implementations were carried out. The model was validated using real time images and online data sources. The system was tested and found to work satisfactorily under practical input conditions.

Bemisia tabaci (Gennadius) is a species complex, affecting wide varieties of crops globally, mainly a dreaded vector of Gemini viruses. It also causes extensive damage as a pest through direct feeding on the phloem sap and impacting crop yield by excreting copious amounts of honeydew. Sugars of honeydew serve as an excellent source for predators and parasitoids in pursuing their prey. In addition, the volatiles emitted by microbes in the honeydew are reported to attract the natural enemies of sucking pests. In the present study, an attempt was made to estimate sugars and identify volatiles in the honeydew of B. tabaci genetic groups viz; MEAM-I and Asia-I. HPLC analysis revealed that ribose sugar was highest in both phloem sap and honeydews followed by glucose, sucrose, and fructose. GC-MS result revealed volatiles, n-nonane, n-decane, 2-methyldecane, o-cymene benzene, n-undecane, propyl disulfide, n-dodecane, disulfide, bis (1-methylpropyl) are common in B. tabaci genetic groups.

This paper presents automatic ECG arrhythmia classification method using symbolic dynamics through hybrid classifier. The proposed method consists of four steps: pre-processing, data extraction, symbolic time series construction and classification. In the proposed method, initially ECG signals are pre-processed to remove noise. Further, QRS complex is extracted followed by R peak detection. From R peak value, symbolic time series representation is formed. Finally, the symbolic time series is classified using Fuzzy clustering Neural Network (FCNN). To evaluate the proposed method we conducted the experiments on MIT-BIH dataset and compared the results with Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) classifiers. The experimental results reveal that the FCNN classifier outperforms other two classifiers.