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Very limited studies have forecast the gas concentration and faults of high voltage direct current (HVDC) converter transformers. In this study, a hybrid algorithm consisting seasonal autoregressive integrated moving average-convolutional neural network-gated recurrent unit (SARIMA-CNN-GRU) was proposed to forecast the seven gas concentrations, i.e., hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon-dioxide (CO2), and carbon monoxide (CO). Moreover, extreme gradient boost (XGBoost), adaptive boosting (AdaBoost), and light gradient boosting machine (LightGBM) were utilized to predict the fault inside the HVDC converter transformer. Two types of datasets collected from different sources were used to train the regression and classification models. The performance of the regression model was evaluated by root mean squared error and mean absolute error, whereas the performance of the classification model was evaluated by accuracy, precision, and recall. Finally, a comparative analysis was conducted to showcase the superiority of the proposed methodology.

In this article, the lightning impulse breakdown (LIBV) behavior of two low pour point synthetic ester liquids is presented in comparison to a typical synthetic ester (TSE) at both positive and negative polarities. Traditional mineral insulating oil has also been considered for reference purposes. A detailed breakdown behavior analysis of the four test liquids under a nonuniform field (medium gap, point-plane electrode system) and quasi-uniform field (smaller gap, U-plane electrode system) is envisaged. The LIBV measurements based on the source voltage waveforms and light activity during the discharge process are presented. The Weibull breakdown failure rates and streamer velocity during the breakdown of different liquids for all the cases (± polarities and both electrode configurations) are reported in support of the discussions. In the case of nonuniform fields, the lightning breakdown voltage of the low pour point liquids is found to be higher than TSE s and is comparable to mineral oil (MO) under both polarities. While in the case of quasi-uniform field, the lightning breakdown voltage of the low pour point liquids is found to be lower than MO and comparable to the TSE under both polarities. These findings add to limited knowledge on the application of esters in cold countries and allow insulation designers to estimate the behavior of the low pour point synthetic ester liquids under lightning conditions.

Mechanical micro-machining, in general, and micro-end-milling, in particular, has become a very good technique for fabricating 3D micro-features in a variety of materials. To optimize and control the process, prediction of the cutting force accurately is very important. In this work, a force prediction model is developed by a combination of analytical method and finite element (FE) simulations. The model predicts the cutting force components for micro-end-milling process successfully which is compared with experimental force signal obtained by using Al2024-T3 and AISI 4340 as workpiece materials. The predicted and experimental cutting forces are in very good agreement for both the amplitude and trend of the cutting force. The percentage deviation of the predicted force from the experimental force values for both feed force (Fx) and transverse force (Fy) is around 15% (except one case) for Al2024-T3. For the AISI 4340 material, the percentage deviation for Fxis around 25% and for Fyis approximately 10%. The methodology followed here is general in nature and it can be applied to any other machining process as well.

The furan prediction in the transformer oil can provide information about the oil degradation rate and overall health of the oil-filled transformer. For this study, the furan content in mineral oil of a 300-MVA converter transformer installed at ±500-kV high-voltage direct current (HVDC) inverter station is taken. Six features are taken as an input to the machine learning (ML) model to predict the furan content for predictive maintenance of the converter transformer, i.e., carbon monoxide, carbon-di-oxide, acidity, breakdown voltage (BDV), total combustible gases, and water content. A hybrid ML model is developed consisting of a convolutional neural network-long short-term memory network (CNN-LSTM) and extreme gradient boosting (XGBoost). Then, the mean absolute percentage error with recursive weighting (MAPE-RW) technique is employed to combine the predictions of two models to achieve better outcomes than a single model. For this purpose, a total of 350 data are collected from the local utilities for training and testing. Moreover, a total of 41 numbers of data are collected from the converter transformer for validation of the proposed model. The performance of the proposed algorithm is evaluated by mean absolute error (MAE), root means squared error, and coefficient of determination. The hybrid model is also compared with other models to show its efficacy and robustness. Depending upon the furan prediction, converter transformer insulation health is classified into three classes for preventive maintenance.

One of the major issues with natural ester fluid as transformer insulation is encountered in the oxidation process, which forms a thin gel layer due to polymerization. This latter can hinder the electrical and heat transfer properties. In order to understand the process, methodical thermal aging of natural esters is carried out for a 500-h duration under atmospheric conditions at 160 °C. The characteristic variation of fluid-forming gel is investigated by nuclear magnetic resonance (NMR) with thermal aging, through which molecular weight degradation and iodine index of thermally aged oil are estimated for transformer applications. The molecular weight and iodine index of thermally aged oil are reduced to 8.31% and 43.016%, respectively, after 500 h of aging. Thermogravimetric analysis (TGA) is carried out to further comprehend this, and heating calorific value is estimated through bomb calorimetric studies. Heat calorific value (HV) for 500-h aged oil and gel is nearly similar. The electrical discharge performance of the oil is estimated through corona inception voltage (CIV) studies under nonuniform electric field configuration adopting ultrahigh frequency (UHF) sensor techniques. The iodine index is in direct correlation with the CIV measurement.

The prediction of the remaining useful life (RUL) of transformer oil helps in condition monitoring and health monitoring of oil-filled power transformers. However, the prediction of RUL depends on the aging condition of the insulation system. In this article, a novel hybrid machine learning (ML)-based regression model is developed for predicting the RUL of the insulating oil in years. A total of 26 features have been taken from different chemical and physical properties and indices of mineral oil. Later, features are selected using the Pearson correlation coefficient ( $P_{c}$ ) and conditional mutual information-based feature selection (CMIFS) techniques. Finally, a hybrid algorithm consisting of support vector regression (SVR), k-nearest neighbor (k-NN), multiple layer perceptron (MLP), ridge regression (RR), ElasticNet, adaptive boosting (AdaBoost), and extreme gradient boost (XGBoost) are used to predict the RUL of the oil. The performance of the hybrid model is analyzed by root mean square error (RMSE), root mean square logarithmic error (RMSLE), mean absolute error (MAE), and correlation coefficient ( ${R}^{{2}}$ ). The comparison with the individual base regression algorithm showed that the hybrid model performed better. This study adds to the arguments that data-driven intelligent monitoring systems are essential for the safe and efficient health monitoring of transformers.

Purpose: To assess the various patterns of the inter-proximal contact areas of the permanent dentition using the available cone beam computed tomography (CBCT) images. Methods: A retrospective cross-sectional study was performed on existing CBCT images from a University hospital and teaching department. Three thousand two hundred and fifty inter-proximal contacts were assessed from 125 existing CBCT scans of patients (age range: 14 years 3 months–29 years 9 months) who had matched the eligibility criteria. The types of inter-proximal contacts of the permanent dentition were scored at occlusal, middle, and cervical thirds, according to the OXIS inter-proximal contacts classification. Chi-square test was used to assess the contacts and gender/arches. Results: The most prevalent contact was type I for the posterior teeth contacts, and types S1 and S2 for the anterior teeth contacts. Statistical significance was not observed between the contacts on the left and right sides (P> 0.05), while statistical significance was observed between the inter-proximal contacts of the maxilla and mandible (P< 0.05). Conclusion: The CBCT-based three-dimensional assessment of the inter-proximal contact areas of the permanent dentition is representative of the OXIS classification. The occlusal level contact was predominantly representative of the overall type of the contact.

Industry 4.0 and circular economy are two energetic aspects of operations management and production economics. The current study extracts the factors for adopting an industry 4.0-based circular economy to measure sustainability in SMEs. Eight hypotheses are proposed by identifying one independent variable, four mediating variables and one dependent variable. Survey-based research is carried out, and the sample size for the current study is 296 from different SMEs. The structural equation modeling approach is utilized for testing the hypotheses. The construct identified is industry 4.0-based circular economy; the four mediating variables are: economic performance, environmental performance, social performance, and operational performance, and the dependent variable is sustainability. The mediating variables have a positive impact on the dependent variable. The study is unique as the joint adoption of industry 4.0 and circular economy is tested, and also it reveals the importance of sustainability achievement in the operating firms. The prior studies have not considered the joint adoption of industry 4.0 and circular economy.

In the present study, rheological studies dealing with the flow behavior of thermally aged natural ester fluid under laboratory-controlled conditions are performed. Visual observation of gelling is witnessed due to thermal aging. An enhancement in viscosity of about 188% is observed with the 500-h aged fluid compared to the nonaged fluid. It is estimated that the flow behavior altered by gel follows non-Newtonian behavior with a shear-thinning effect on applied shear rate conditions. The dependency of storage and loss modulus to amplitude and frequency sweep under oscillatory shear flow was investigated. The viscous state of gel dominates over its initial elastic state, beyond the gel point. The flow models that govern the behavior of the gel were applied, adopting nonlinear least square methods to investigate the rheological parameters in terms of flow behavior index and yield stress. The results of the thermally aged natural ester fluid demonstrate a positive correlation with Bingham’s model and Mizrahi–Berk’s model based on the yield stress and flow behavior index, respectively. The phase transition of gel investigated in the present study can be used as a time-based preventive measure, by the transformer manufacturer, in the heat transfer performance of natural ester-filled transformers.

Dibenzyl disulfide (DBDS) is the most prevalent corrosive sulfur in transformer oil. It reacts with the transformer windings to produce copper sulfide (Cu2S) and gets deposited on the insulating paper’s surface, leading to interturn faults within the transformer windings. Hence, this article proposes a deep neural network (DNN) to predict the DBDS content in transformer oil. The parameters like interfacial tension (IFT), breakdown voltage (BDV), water content (WC), oxygen, neutralization number (NN), color, furan content, and specific gravity (SG) were used as features to train and test the DNN model. The performance of the regression model was evaluated using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( ${R}^{{2}}{)}$ . Moreover, extensive analysis is carried out by varying feature combinations and test-train ratios to obtain the best prediction model. The values of DBDS predicted by DNN were further used to determine the corrosive sulfur concentration in transformer oil. The proposed method is validated on real-life transformer data obtained from the online dataset and on data obtained from the local power utilities. A comparative study showed better efficacy of the proposed DNN model than other prediction models for accurate DBDS prediction.

In this article, some investigations on the prebreakdown and breakdown phenomena of low pour point insulating liquids under negative lightning impulse (LI) voltage are reported. The tested liquids include mineral oil (MO), a typical synthetic ester (TSE), and two low-pour point synthetic esters. These liquids underwent accelerated thermal aging. The nonaged and aged samples were subjected to LIs using a point-plane electrode arrangement. The discussions are focused on the initiation of partial discharges, propagation of streamers, and breakdown behavior in the nonaged and aged liquids. The investigated parameters include inception voltage, LI breakdown voltage, streamer acceleration voltage, and streamer velocity. The results are supported by the oscillographs of the light activity that is recorded during the discharge process. The prebreakdown phenomenon noticed in the TSE vis-à-vis mineral insulating oil is in line with the existing literature. Importantly, it is noticed that the inception and breakdown voltages of the nonaged low pour point synthetic esters are similar to nonaged MO. In addition, the inception and breakdown voltages of the aged low pour point synthetic esters are noticed to be higher than that of the aged MO. These results add to the arguments in favor of replacing MOs in power transformers.

The main problem is to raise the standard of construction, rethinking concrete's expanding potential as a construction material. The scarcity of river sand as a fine aggregate ingredient is currently a widespread issue for many concrete plants. As a result, quarried stones that were accessible locally were used as fine aggregate. Crushed Rock Particles, also known as crushed sand, performs better in terms of fresh characteristics since there are more fines present in CRF than in river sand. The current study additionally examines the relationship between plastic viscosity and the fresh and hardened SCC properties. As a partial substitute for Ordinary Portland Cement, Fly Ash and Ground Granulated Blast Slag blends are used to generate binary and ternary. The experimental tests satisfactorily validate the suggested blend design. The results shown that SCC mixture with ternary blend, binary blend with GGBS, fly ash, and blend with pure OPC are suitable for creating fresh characteristics. Additionally, it was found that a cohesive and workable mix was produced when 100 % CRF was used in place of sand.

Dissolved gas analysis (DGA) of insulating oils is one of the most popular methods to detect incipient faults in power transformers. However, appropriate feature selection is crucial for accurately detecting incipient faults using DGA data. Another issue is the unavailability of a balanced DGA dataset, which can hamper the fault classification accuracy. Considering these two issues, this article proposes a novel and accurate fault classification framework using gas ratios as features obtained from the DGA data of power transformers. The obtained unbalanced DGA data was initially balanced using the synthetic minority oversampling technique (SMOTE) in the data pre-processing stage. Following this, an efficient feature selection algorithm, namely, recursive feature elimination (RFE) was used to select the best possible features prior to the fault classification using three benchmark machine learning (ML) classifiers, namely, ${k}$ -nearest neighbor (KNN), multiclass support vector machines (SVMs), and extreme gradient boost (XGBoost). The proposed classification model was tested on the DGA data obtained from the local power utility and on the benchmark IEC TC-10 database. Investigations revealed that the proposed classification model delivered detection accuracy of 98.84% and 97.43%, respectively. The proposed method may be reliably used to diagnose incipient faults in power transformers.

The application of artificial intelligence algorithms for transformer incipient fault classification using dissolved gas analysis (DGA) is an interesting engineering approach. However, there are various factors that affect the performance of artificial intelligence algorithms. This article presents the influence of the data balancing approach on transformer DGA fault classification with the machine learning (ML) approach. In this work, a total of 4580 DGA samples from in-service transformers are considered for training various ML models. The main challenge for the DGA problem lies in the availability of the normal degradation transformer data and its uniformity corresponding to different faults is almost impossible. This is because DGA is not an exact science, but an empirical approach subjects to variability. Thus, it is a usual practice to apply data sampling techniques that largely influence the efficiency of the algorithms. The present work reports the impact of the data balancing schemes on the performance of the fault classification and demonstrates that a careful choice of the data sampling method and ML algorithm is essential for DGA problems. To demonstrate the global scale ability of the propose model, the model is tested on the IEC TC ten data (field inspection data), while these data are not exposed to the machine during the learning stage. The ability of the ADASYN method in significant enhancement of the global scale capability of AI-based transformer DGA fault classification is reported. This approach will be helpful for condition monitoring engineers in transformer insulation diagnosis in implementing the monitoring modules for large transformer fleets and understanding the insulation oil behavior over years.

The present work is focused on the oxidation aspects (focused on gelling) of natural esters and its influence on the performance of transformer insulation systems. Even though natural esters are not suitable for breathing units, the scenario considered simulates a defect in the sealing system and thus bringing the liquid in direct contact with atmospheric air. Commercially available natural ester liquids are thermally degraded under open beaker conditions in the presence of cellulose with a controlled aging history. The accelerated aging conditions are maintained until the formation of sol and gel is evident in the bulk of the liquid. Later, the insulating liquids and insulating papers are analyzed to understand the behavioral impact of sol and gel on the insulation. The discussions in this article are supported by the physicochemical and electrical characterizations of the degraded liquids and papers. The characterizations include Fourier transform infrared spectroscopy (FTIR), ac breakdown voltage (BDV) failure probability rate, X-ray diffraction (XRD), degree of polymerization (DP), and viscosity. It is found that before the formation of gelling, sol is witnessed in the bulk of the liquid. The presence of the sol highly influences the viscosity of the liquid and aids in rapid acceleration of the oxidation process. It is understood that that the gelling phenomena in natural esters do not have a noticeable impact on the liquid BDV failure rate and paper degradation.

Various techniques of fault diagnosis in oil immersed power transformers have been utilized for the last two decades and are well reported in the literature. The traditional and widely accepted dissolved gas analysis (DGA) methods include IEC gas ratio code, IEEE Key gas, Doernenburg ratios, and the Duval triangle. Amongst these methods, Duval triangle method (DTM) is reported to be the most prominent. A new fault gas interpretation approach has been proposed in the present work. This method combines three Duval triangles with triangular membership functions (MFs) evaluated empirically for diagnosing the incipient faults. Like the existing Duval triangles, five gases, namely, hydrogen, methane, ethylene, ethane, and acetylene, are considered for the present study. Initially, the relevant gas percentages for the three Duval triangles are calculated and later converted into triangular fuzzy MFs. Finally, an expert rule-based method synthesizes the outcomes into a specific main fault type or subfault type. Test data of 139 transformers are used to compare the proposed method with the conventional DTM. The proposed approach is simple and can potentially determine the main faults and subfaults simultaneously based on numerical indices in a single fuzzy logic (FL) system. In addition, the proposed method has overcome the ambiguity in overlapping faults in the traditional methods.

In India, half the population depends on agriculture for a livelihood. Microbial diseases are a significant threat to food security, but their rapid identification remains difficult due to limited infrastructure. With AI, automatic detection of plant diseases from raw images is possible using deep learning and transfer learning. This paper aims to detect and classify Grapes and Mango leaf diseases, employing a dataset of 8,438 images of diseased and healthy leaves collected from the Plant Village dataset and acquired locally. The deep convolutional neural network (CNN) is trained to identify diseases or their absence. A pre-trained CNN architecture called AlexNet is modeled for automatic feature extraction and classification. The system is developed with MATLAB achieves an accuracy rate of the detection of 99% and 89% for Grape leaves and Mango leaves respectively. An app named "JIT CROPFIX" is developed to implement the same on an Android Smartphone.

Glass fiber–cement–polyester composites with different weight percentages (wt.%) of glass fiber and cement were fabricated in the present work through hand lay-up process. Composites were made to undergo different soaking conditions viz., water soaking at room temperature, and boiling water soaking. Unsoaked (as prepared) composites, water-soaked at room temperature, and boiling water soaked samples were tested as per ASTM standards for tensile and flexural strengths. The design of experiments (DOE) based on the full factorial design (33=L27) is applied to optimize the response variables using MINITAB-18. Results revealed that increasing the wt.% of glass fiber enhanced flexural and tensile strengths. Increasing the wt.% of cement deteriorated the tensile strength but increased the flexural strength. However, specimen subjected to water soaking conditions exhibited a decline in both tensile and flexural strengths.

In this research, the results of different weight percentage of glass fiber (30, 40 & 50), cement (0, 3 & 6) and polyester resin (70, 60 & 50), on the properties of glass fiber-cement-polyester composites are investigated. The specimens are prepared by hand lay-up technique. All the specimens are tested for tensile and flexural strength as per ASTM standards. Results showed that escalation in glass fiber wt.% improved the tensile strength (by 9% at 40 wt.% and 17% at 50 wt.%) and flexural strength (by 10% at 40 wt.% and 16.5% at 50 wt.%). Whereas an increase in cement weight percentage decreases tensile strength and increases flexural strength. The failure of the sample is due to glass fiber pull out and rupture of the matrix, under tensile load.

In the present work efforts are made to confirm the presence of actual weight percentage of reinforcements and its effect on aluminium alloy composites during stir casting technique. Two types of reinforcements, such as Silicon carbide and Boron carbide, were used by varying weight to manufacture Al6061 composites. The manufactured composites after stir casting show uniform distribution of hardness on the entire cross-section measured from top to bottom. The dissolved composites during the acid solubility test show approximately 95% of reinforcements present in the aluminium composites. Presence of reinforcements and quantity play a very important role to augment the properties of aluminium alloy by altering the microstructure during solidification and induces strain energy in the metallic matrix alloy. Almost 50–80% increase in hardness and 40–50% increase in tensile strength were observed in the as-cast composites as compared to aluminium alloy. Uniform distribution of reinforcements and hardness distribution on the entire cross-section clearly reveals that stir casting is an effective method for manufacturing aluminium composites.

In the characterization of natural fiber-reinforced composites, it is crucial to consider the nonuniform cross-section of fibers extracted from plants or leaves, which results from the inherent variation in fiber diameter. This variation is a key aspect of the actual reinforcement behavior, and disregarding it by assuming a uniform cross-section can violate the physical understanding of the composite’s mechanics. This study focuses on characterizing the elastic properties of a natural tapered sisal fiber-reinforced epoxy composite, taking into account the variation in fiber diameter along its length. To achieve this, both experimental, analytical, and micromechanics methods are employed. The experimental approach involves conducting tests to measure the mechanical properties of the composite, while the micromechanics analysis provides a framework to understand the reinforcement behavior and predict the composite’s mechanical response. The study aims to identify and present various elastic properties of the composite, including the longitudinal modulus, transverse modulus, in-plane and out-of-plane shear modulus, and major and minor Poisson’s ratios. The fiber taper angle has a significant effect on longitudinal and transverse modulus and interfacial stresses. The longitudinal modulus experiences a reduction of 25.9% when increasing the taper angle from 2∘to 10∘at a higher fiber volume fraction (40%). However, at a lower volume fraction of the same fiber (10%), the reduction in modulus is limited to 5.74%. Similarly, the transverse modulus is also affected by the taper angle. At the higher fiber volume fraction, there is a decrease of 24.52%, whereas at the lower volume fraction and higher taper angle of the fiber, the decrement is 4.94%.

Comparison of human genome sequences from different individuals has unraveled that genes involved in the drug efficacy and metabolism are polymorphic, harboring mutations, splicing variations and other alterations. These data provide a reasonable explanation for the inter-individual variations observed in drug therapy. Thus, a detailed molecular analysis and an in-depth knowledge of these genes is a prerequisite to practice pharmacogenomics-based medicine. We have introduced a 6-week laboratory research rotation to train students in the expression analysis of different pharmacogenes combined with bioinformatics tools. Students were first introduced to the bioinformatics tools to identify appropriate DNA primers to amplify specific pharmacogenes from the laboratory cancer cell lines. The amplified DNA fragments were sequenced. Finally, students were trained in bioinformatics tools to establish the identity of these DNA sequences. The possible implications of this laboratory training in developing problem-solving skills needed in the implementation of pharmacogenomics knowledge in the clinic, are discussed.

ABSTRACTIn this paper, evolution of ester dielectrics highlighting the significant contributions towards replacement of mineral oils with ester oils was discussed. The citations listed in this survey provide a representative sample to current scenario pertaining to ester dielectrics in transformer insulation technology. Additionally, X-ray diffraction analysis highlighting the degradation rate of insulation paper in the presence of typical dielectric liquids, UV spectroscopy analysis of oils, interfacial tension variations, and diffusion characteristics of insulation paper were experimentally found by simulating accelerated thermal stresses. Furthermore, future challenges and outlook in accommodating ester dielectric liquids for safer and effective transformer technology were also discussed.

Traditional stock trading methodologies (ARMA, ARIMA, regression, etc.) are used to analyse price behaviour and then produce signals namely BUY, SELL or HOLD. Our proposed work is to develop a one-dimensional cellular automata model that helps new investors who have zero knowledge on stock market trades. The one-dimensional cellular automata (1D CA) model processes the existing recommendations or advices from the experts who possess a very good knowledge on stock market trades. Based on different expert recommendations, the CA model will give financial alerts that will help the investor to take buy/hold/sell decision on stocks. Hence new investors can either make a profit or at least keep them in safe zone (minimum loss). The trading rules in this model give highest priority to sell the share and lowest priority to buy the share. This model gives recommendations to investors regarding the selling, buying and holding of a stock/stock segment.

Tool wear has been a potential problem that costs high for the manufacturing industry. In the past there has been application of signal processing techniques to cutting operation and monitor the tool wear. This paper relates the suitability of application of wavelet transform to tool wear prediction. The suitability of fast Fourier transform (FFT) and discrete wavelet transform (DWT) to process the signals from machining was investigated. To accomplish this objective, experiments were carried out to collect signals from machining experiments and thereafter signals were processed using FFT and wavelet transform. It is observed that at higher cutting speeds, wavelet transform is a more effective signal processing technique compared to FFT as signal gets corrupted by high frequency noise at higher speeds. Effect of wavelet packet decomposition for de-noising and filtering was also studied to remove high frequency noise from the accelerometer signals.

This study focuses on the implementation of moving average technical indicator on a one dimensional cellular automata. The moving average is an indicator that shows the average price of a security at a given period of time and it is the oldest and most widely used technical analysis tool. Each cell in the automata compares the relationship between moving averages of the security's price with the security price itself. During the simulation each cell in the automata produces two output states either BUY or SELL, that will help the traders to take better decision on stocks. A Buy signal is generated by the cellular automata when the security price rises above its moving average, and a Sell signal is generated when the security price falls below its moving average.

In mechanical micro-machining (MMM), the tool edge cannot be considered to be sharp due to the finite edge radius observed at the micro-scale. The uncut chip thickness is comparable to the tool edge radius and sometimes it is less than the tool edge radius. In MMM, below certain critical uncut chip thickness, only elastic deformation takes place and no chip is formed. This is known as minimum chip thickness effect. This affects the machining process significantly and needs to be understood properly in order to develop a successful force model. In the present work, a finite element model using Johnson-Cook fracture model is developed to find the minimum uncut chip thickness in MMM which takes into account the material properties and tool edge radius. It is estimated from the height of the element layer required ahead of the tool for no chip formation. From the simulations, the minimum uncut chip thickness (MUCT) has been found which is in the order of values available in literature.

Nowadays the market is increasingly demanding more customized products, thus making the manufacturers under the pressure of cost reduction, to survive in the highly competitive market. Working on this, Robert Bosch INdia (RBIN) Limited, Bangalore Plant (BanP) designs and produces Diesel systems which make vehicles more cleaner and economical. To be the world’s leading manufacturer of diesel injection systems, Bosch has to manufacture its very own design to fulfill the requirements for every customers, economically. Fuel Injection Equipment in diesel engine is the “Heart of the Engine” and plays a major role in its performance, emissions and reliability. Hot Forged Rail (HFR) finds its application in common rail fuel injection system, which stores the fuel at high pressure (1600bar). And at the same time, the pressure oscillation, which is generated due to the high pressure pump delivery and the injection of fuel are damped by the rail volume. Its design varies according to customer requirement and engine design. Therefore, BanP has to produce different types of rails to complete diesel market in India. In order to be customer centric and productive, BOSCH follows Bosch Production Systems (BPS), the elements of BPS in Lean manufacturing are Value Stream Mapping (VSM), Value Stream Design (VSD) and Value Stream Planning (VSP). The Single Minute Exchange of Die (SMED) is one important lean manufacturing tool to reduce waste and improve production flexibility, allowing lot size reduction and manufacturing flow improvements. Quick Change Over (QCO) is a set of activities (preparation, changing and adjustment) which are being carried out in between the production of two varieties to reduce changeover time and to reach the optimum production running. The proposed SMED approach was tested for injection machines changeovers in the automotive industry and the implementation had enabled reduction in setup time, through company’s internal resources reorganizations without the need for significant investment. The objective of the present study is to implement the SMED/QCO tool approach in RBIN, BanP, HFR production line to reduce the changeover time by >65% and improve availability by 15%, thereby eliminating the possibility of investing on new production line to meet customer demand.

In mechanical micro–machining, the feature size ranges from tens of micrometres to less than a millimetre in size. At this level of cutting, the tool cannot be considered as a sharp tool, as the uncut chip thickness is comparable to the tool edge radius. Due to technological limitations, it is not possible to manufacture a perfectly sharp tool. Researchers have studied various aspects of edge radius of tools, such as size effect, minimum chip thickness effect, ploughing phenomena, etc. In the present study, FEM analysis of the orthogonal machining is presented to study the stress state ahead of the tool, in the machined surface and in the layers encompassing the chip. The uncut chip thickness in this study is chosen to be more than the edge radius of the tool. The study indicates, an optimum value for edge radius can be found and used for processes of constant uncut chip thickness.

Renal tubular dysfunction could be involved in the increased sodium and water reabsorption in chronic heart failure (CHF). The goal of the present study was to examine the molecular basis for the increased renal sodium and water retention in CHF. We hypothesized that dysregulation of renal epithelial sodium channels (ENaC) could be involved in the pathogenesis of CHF. The left coronary ligation-induced model of heart failure in the rat was used. Real-time PCR and Western blot analysis indicated that the mRNA and protein abundance of α-, β-, and γ-subunits of ENaC were significantly increased by in the cortex (mRNA: α-ENaC Δ104 ± 24%, β-ENaC Δ47 ± 16%, γ-ENaC Δ55 ± 18%; protein: α-ENaC Δ114 ± 28%, β-ENaC Δ150 ± 31%, γ-ENaC Δ39 ± 5% compared with sham rats) and outer medulla (mRNA: α-ENaC Δ52 ± 18%, β-ENaC Δ38 ± 8%, γ-ENaC Δ39 ± 13%; protein: α-ENaC Δ88 ± 16%, β-ENaC Δ94 ± 28%, γ-ENaC Δ45 ± 9% compared with sham rats) of CHF compared with sham-operated rats. Immunohistochemistry microscopy confirmed the increased labeling of α-, β-, and γ-ENaC subunits in the collecting duct segments in rats with CHF. Furthermore, there was a significant increase in diuretic (7-fold compared with sham) and natriuretic responses (3-fold compared with sham) to ENaC inhibitor benzamil in the rats with CHF. Absence of renal nerves produced a greater contribution of ENaC in sodium retention in rats with CHF. These results suggest that the increased expression of renal ENaC subunits may contribute to the renal sodium and water retention observed during CHF.

The principles of Total Quality Management (TQM) have been successfully adopted in the field of higher education by developed countries such as Japan, USA and UK. Ultimately, the objective of TQM is to build an institution that performs services, which fulfill customer requirements, and thereby delights them. The purpose of this paper is to study the practice of Integrated Total Quality Management (ITQM) in engineering institutions in Tamilnadu, India, from the stakeholders' (government, faculty, parents, students, and the public at large) perceptive. ITQM is nothing but a bundle of global management practices such as ISO 9001:2000, Six-Sigma (DMAIC), TQM, Knowledge Management and Lean Thinking. The data were obtained from a survey of 250 members of stakeholders and analysed. Based on the analysis, recommendations have been made. The result of this study can also be applied internationally in the institutions of professional learning.

We propose a molecular model for phospholipid membrane lysis by the ubiquitous plant toxins called thionins. Membrane lysis constitutes the first major effect exerted by these toxins that initiates a cascade of cytoplasmic events leading to cell death. X-ray crystallography, solution nuclear magnetic resonance (NMR) studies, small angle X-ray scattering and fluorescence spectroscopy provide evidence for the mechanism of membrane lysis. In the crystal structures of two thionins in the family, α1- and β-purothionins (MW: approximately 4.8 kDa), a phosphate ion and a glycerol molecule are modeled bound to the protein. 31P NMR experiments on the desalted toxins confirm phosphate-ion binding in solution. Evidence also comes from phospholipid partition experiments with radiolabeled toxins and with fluorescent phospholipids. This data permit a model of the phospholipid–protein complex to be built. Further, NMR experiments, one-dimensional (1D)- and two-dimensional (2D)-total correlation spectroscopy (TOCSY), carried out on the model compounds glycerol-3-phosphate (G3P) and short chain phospholipids, supported the predicted mode of phospholipid binding. The toxins’ high positive charge, which renders them extremely soluble (>300 mg/mL), and the phospholipid-binding specificity suggest the toxin–membrane interaction is mediated by binding to patches of negatively charged phospholipids [phosphatidic acid (PA) or phosphatidyl serine (PS)] and their subsequent withdrawal. The formation of proteolipid complexes causes solubilization of the membrane and its lysis. The model suggests that the oligomerization may play a role in toxin's activation process and provides insight into the structural principles of protein–membrane interactions.

Atherothrombotic disease places a huge financial and social burden on the nation, and antiplatelet therapy is important in preventing this. Aspirin is most widely used but newer compounds are valuable, especially on top of standard therapy in patients with unstable angina or non-ST segment elevation myocardial infarction. This article reviews the changing use of antiplatelets.

Benzyne and its 3,4,5,6tetraphenyl, 3 and 4methyl, 3methoxy and 4,5difluoro derivatives react with acetic acid 2selenoxo2Hpyridin1yl esters 4ae to give benzobseleno2,3bpyridines 10–15 in modest yields. The benzynes were generated by one or more of the following methods: diazotization of anthranilic acids 5ag with isoamyl nitrate; mild thermal decomposition of 2diazoniobenzenecarboxylate hydrochlorides 6ad treatment of phenylotrimethylsilylphenyliodonium triflate 7 with tetrabutylammonium fluoride; and treatment of 2trimethylsilylphenyl triflates 8ac with cesium fluoride. In all the reactions, the corresponding 2methylselenenylpyridines 16ad were also obtained suggesting that these reactions may involve selenium addition to benzyne viaa SET single electron transfer.

Platelets play a key role in the pathogenesis of atherothrombotic conditions, e.g. acute coronary syndromes, cerebrovascular and peripheral vascular events. Antiplatelet agents interfere with platelet activation and aggregation and, as such, would be expected to modify the natural history of cardiovascular disease. In this review we explore the evidence to support the use of such drugs, singly or in combination, in a variety of situations characterised by thrombosis and summarise some of the concerns inherent in their use.

Mass analyzed highly charged ion beams of energy ranging from a few keV to a few MeV plays an important role in various aspects of research in modern physics. In this paper a unique low energy ion beam facility (LEIBF) set up at Nuclear Science Centre (NSC) for providing low and medium energy multiply charged ion beams ranging from a few keV to a few MeV for research in materials sciences, atomic and molecular physics is described. One of the important features of this facility is the availability of relatively large currents of multiply charged positive ions from an electron cyclotron resonance (ECR) source placed entirely on a high voltage platform. All the electronic and vacuum systems related to the ECR source including 10 GHz ultra high frequency (UHF) transmitter, high voltage power supplies for extractor and Einzel lens are placed on a high voltage platform. All the equipments are controlled using a personal computer at ground potential through optical fibers for high voltage isolation. Some of the experimental facilities available are also described.