Analysis and capacity based earthquake resistant design of multi bay multi storeyed RC frame.

Earthquakes in different parts of the world demonstrated the disastrous consequences and vulnerability of inadequate structures. Many reinforced concrete (RC) framed structures located in zones of high seismicity in India are constructed without considering the seismic codal provisions.The vulnerability of inadequately designed structures represents seismic risk to occupants.The main cause of failure of multi-storey multi-bay reinforced concrete frames during seismic motion is the soft storey sway mechanism or column sway mechanism. If the frame is designed on the basis of strong column-weak beam concept the possibilities of collapse due to sway mechanisms can be completely eliminated. In multi storey frame this can be achieved by allowing the plastic hinges to form, in a predetermined sequences only at the ends of all the beams while the columns remain essentially in elastic stage and by avoiding shear mode of failures in columns and beams. This procedure for design is known as Capacity based design which would be the future design philosophy for earthquake resistant design of multi storey multi bay reinforced concrete frames. The aim of this project work is to present a detailed worked out example on seismic analysis and capacity based design of four-storey reinforced concrete frame building

Optical-based ATR algorithms for applications in swarmed UAVs

Swarmed Unmanned Aerial Vehicles (UAVs) with Automatic Target Recognition (ATR) technology are becoming an important element of electronic warfare. The advantages of UAVs over piloted vehicles have increased the need to develop robust and reliable ATR algorithms. Various issues like changing weather, camouflage, low contrast and resolution, clutter, inadequate databases place a limit on the performance capabilities of a typical ATR algorithm. In an effort to deal with these issues, a correlation-based algorithm is proposed in this thesis. This algorithm calculates the correlation between the input image and a target template which is created by projecting a 3-D model from the perspective of the UAV. The locations of correlation peaks are then declared to be the locations of the targets. We apply this algorithm to images with one object of a known class and move on to the more general case of images with an unknown number of targets from one or more classes. We provide an analysis of the performance of this correlation-based algorithm.;We compare the performance of the proposed correlation-based approach with that of a training-based approach. To provide a concrete example of an off-the-shelf training-based ATR algorithm, the open source IntelCV library was used. In the training-based method, a sample set is created and trained (Haar-like features are used for training) to produce results for comparison purpose. We further develop and analyze a method of correlating across multiple frames that have been preprocessed using the correlation-based approach. This method is shown to be useful in detecting true targets and suppressing false alarms in cases where a single image is not sufficient for classification

Comparison of 19mm Superpave and Marshall Base II mixes in West Virginia

The Superior Performing Asphalt Pavements (Superpave(TM)) asphalt concrete mix design method was developed through Strategic Highway Program (SHRP) in 1993. With the introduction of Superpave mix design, the Marshall method of mix design is becoming obsolete for highway pavements. Superpave implementation varies by state. The WVDOH has implemented Superpave on all National Highway System projects since 1997. The decision regarding implementation of Superpave for low volume roads in WV is still under review.;The primary objective of this research work was to compare the 19mm Superpave and Base II Marshall design mixes in WV to supplement information required for WVDOH to make a suitable decision regarding the implementation of Superpave for low volume roads.;The Marshall and Superpave methods were compared by preparing similar mix design with each method. The mix designs from each method were cross-compared with the conclusion that mixes developed under one method meet the criteria of the other method. In addition, the Asphalt Pavement Analyzer (APA) was used to evaluate rutting performance of gyratory compacted samples in the laboratory. The statistical analysis of rut depth results indicated there is not enough evidence to conclude there is a significant difference between the Marshall and Superpave mix design methods. It can be concluded that for the materials evaluated in this research, the Marshall and Superpave methods produce interchangeable results

Analysis of ADC2 and SAMDC1 expression and the effects of transgenic manipulation of polyamine biosynthesis on flower development in Arabidopsis

Polyamines are low molecular weight, aliphatic cationic molecules playing a vital role in all cellular and physiological processes such as cell growth, differentiation, abiotic stress, and somatic embryogenesis. The present study is aimed at unraveling their role during floral development in Arabidopsis thaliana by studying the temporal and spatial expression of ADC2 and SAMDC1 genes by promoter-reporter fusion analysis, and by the transgenic expression of mouse ODC and carrot SAMDC under tissue specific promoters AP1 and AP3, respectively. The expression of SAMDC1 was found to be prominent during different stages of development and in different whorls of flowers whereas ADC2 expression was almost undetectable in all stages and in various whorls. The heterologous expression of mouse ODC and carrot SAMDC under the control of tissue specific promoters AP1 and AP3 resulted in altered polyamine levels leading to abnormalities like delayed flowering, bolting, abnormal sepal formation and a reduction in stamen number

Fuerza de unión por micro-expulsión del sellador de conductos radiculares a base de minerales en conductos con diferentes ahusamientos.

Objective: The aim of this study was to assess the micro-push-out bond strength of a mineral-based root canal sealer, BioRoot RCS in canals prepared by K3XF rotary systems of two different tapers. Material and Methods: Eighty caries free maxillary central incisors were used in this study. The samples were allocated into 4 groups (n=20) according to the root canal sealer and taper of the rotary instruments. The samples were obturated using single cone obturation technique. From each root 1mm thick slices at coronal, middle and apical thirds were collected using hard tissue microtome under continuous water coolant. Push-out tests were done for these sections using a Universal testing machine (INSTRON 8801) at a crosshead speed of 1mm/min. One-way analysis of variance (ANOVA) was used to compare the bond strengths within groups and Tukey’s multiple post hoc analysis was used for pair-wise comparison of bond strengths. Results: AH Plus exhibited higher micro-push-out bond strength than BioRootRCS though they did not differ significantly (p>0.05). Preparation of root canals with 6% taper rotary instruments showed higher bond strength than 4% though they did not differ significantly (p>0.05). Conclusion: There was no significant difference between micro-push-out bond strength values of BioRoot RCS and AH Plus. The bond strength values were high in 6% taper canals than 4% canals though the difference was not significant statistically.Objetivo: El objetivo de este estudio fue evaluar la fuerza de unión por micro-expulsión de un sellador de conductos radiculares de base mineral, BioRoot RCS, en conductos preparados por sistemas rotativos K3XF con dos conos diferentes. Material y Métodos: En este estudio se utilizaron 80 incisivos centrales superiores libres de caries. Las muestras se distribuyeron en cuatro grupos (n = 20) de acuerdo al sellador del conducto radicular y al cono de los instrumentos rotativos. Las muestras se obturaron mediante la técnica de obturación de un solo cono. De cada raíz se recogieron rodajas de 1 mm de grosor en los tercios coronal, medio y apical utilizando un micrótomo de tejido duro con refrigeración continua por agua. Posteriormente, se realizó una prueba de expulsión para estas secciones utilizando una máquina de prueba universal (INSTRON 8801) a una velocidad del cabezal transversal de 1mm/min. Se utilizó el análisis de varianza unidireccional (ANOVA) para comparar las resistencias de la unión dentro de los grupos y el análisis post hoc multiple de Tukey se utilizó para la comparación por pares de las resistencias de la unión. Resultados: AH Plus exhibió una fuerza de unión de micro-expulsión más alta que BioRootRCS, aunque no difirieron significativamente (p>0,05). La preparación de los conductos radiculares con instrumentos rotativos ahusados al 6% mostró una fuerza de unión superior al 4%, aunque no difirieron significativamente (p>0,05). Conclusión: No hubo diferencias significativas entre los valores de fuerza de unión de micro-expulsión de BioRoot RCS y AH Plus. Los valores de la fuerza de unión fueron más altos en canales cónicos al 6% que en canales al 4%, aunque la diferencia no fue significativa estadísticamente

A prospective double blind randomized comparative study of safety and efficacy of Tapentadol versus Tramadol in post-operative orthopaedic patients

Background: Pain following surgery is an unwanted effect which is to be relieved for the better outcome of surgery and anaesthesia. The study was conducted to compare the efficacy and safety of Tapentadol with Tramadol for post-operative pain relief in orthopaedic surgeries.Methods: After approval of IEC, patients of either sex aged between 18-55 years who had undergone orthopaedic surgeries at Government General Hospital, Kakinada between February 2013 to January 2015 were selected for study. It is a prospective double blind randomised active controlled parallel group comparative study. Sample size is 80 who fulfilled all criteria but there are 6 who discontinued drug in Tramadol group, so 74 were analysed. Written informed consent was taken from all patients, screening, pre anaesthetic evaluation done. Tablet Alprazolam 0.5mg was given at 9PM on previous day of surgery. Both Tramadol and Tapentadol were given 100mg orally 6th hourly. Pain intensity was analysed by visual analogue scale. Sedation was assessed with Modified Wilson sedation scale. Safety and tolerability was assessed by side effects reported by patients. Patients were assessed at 0, 2, 4, 6, 8, 10, 12, 18, 24, 30, 36, 42 and 48 hours post-operatively.Results: Pain reduction was higher in Tapentadol group on chi square test where p value is less than 0.001.Conclusions: Both Tramadol and Tapentadol are effective in treatment of post-operative pain. The pain reduction was higher in Tapentadol group on chi-square test where p value is <0.001. So, we can conclude from our study findings that Tapentadol is efficacious compared to Tramadol in reducing Post-operative pain. Both the drugs were well tolerated by the patients

Image based Chili Crop Disease Prediction Using Deep Transfer Learning

Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challenge, with deep learning becoming as the preferred approach due to its outstanding performance.This research describes a method for detecting chili leaf diseases using a deep convolutional neural network. We compared performances of four architectures: MobileNet, Inception-ResnetV2, EfficientNetB0, and DenseNet. The proposed approach evaluated the findings using measures such as accuracy, loss and time. Our model compares favorably to EfficientNetB0 with an accuracy of 0.995, a loss of 0.023, and time is 5 minutes 45 seconds. EfficientNetB0, a compact deep learning architecture has fine tuned to classify two forms of chili leaf diseases. The method was tested on 2475 photos from the Plant Village dataset