Multi-level quantum Diesel engine of non-interacting fermions in a one-dimensional box

We consider the toy model of quantum Diesel cycle without temperature constructed from non-interacting fermions, which are trapped in a one-dimensional box. The work and energy are extracted from the cycle are by changing the expectation value of Hamiltonian. We analytically calculated the efficiency of the cycle and efficiency at maximum work as a function of compression ratio. We found that the efficiency of the engine depends on both compression ratio and cut-off ratio. In contrast, the efficiency at the maximum work can be written as a function of the compression ratio only. Moreover, we calculate the Clausius relation of the cycle. The degree of the irreversibility of the cycle depends only on the cut-off ratio. We also study the relation between power and efficiency of the cycle. The power output is also studied as the function of the compression ratio. It is found that for a given value of the cutoff ratio, the dimensionless power output decreases as the compression ratio increases

EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier handling is vital for predictive maintenance as the variables distribution initially showed a right-skewed pattern but after rectifying, it became more centralized, with correlations between specific sensors showing potential for further exploration. Q2) Data balancing through SMOTE and feature engineering is essential due to the rarity of actual failure instances. Substantial challenges are observed when predicting \u27Failure\u27 instances, with a lower true positive rate (73%), resulting in low precision (0.02) and recall (0.73) for \u27Failure\u27 predictions. This is further reflected in the low F1-Score (0.03) for \u27Failure,\u27 indicating a trade-off between precision and recall. Despite a commendable overall accuracy of 94%, the class imbalance within the dataset (92,200 \u27Running\u27 instances vs. 126 \u27Failure\u27 instances) remains a contributing factor to the model\u27s limitations. Q3) Machine learning algorithm performance varies, with Catboost excelling in accuracy and failure detection. The choice of algorithm and continuous model refinement are critical for enhanced predictive accuracy in industrial contexts. The main conclusions are: Q1) Addressing outliers in data preprocessing significantly enhances the accuracy of machine failure prediction models. Q2) focuses on addressing the issue of equipment failure parameter imbalance. It was found in the research findings that there was a significant imbalance in the failure data, with only 0.14% of the dataset representing actual failures and 99.86% of the dataset pertaining to non-failure data. This extreme class disparity can result in biased models that underperform on underrepresented classes, which is a common problem in machine learning. Q3) Catboost outperforms other algorithms in predicting machine failures with remarkable accuracy and failure detection rates of 92% accuracy and 99% times it is correct, and further exploration of diverse data and algorithms is needed for tailored industrial applications. Future research areas include advanced outlier handling, sensor relationships, and data balancing for improved model accuracy. Addressing rare failures, enhancing model performance, and exploring diverse machine learning algorithms are critical for advancing predictive maintenance

Integrated Environmental Process Planning for the Design & Manufacture of Automotive Components

Advanced Product Quality Planning (APQP) logic is widely used by manufacturers for the design and manufacture of automotive components. Manufacturers are increasingly finding difficulties to incorporate environmental considerations in the broad range of products that they manufacture. Therefore, there is a need for a systematic method for environmental process planning to evaluate product configurations and their associated environmental impact. The framework and models discussed in this paper can deal with a variety of product characteristics and environmental impacts through a selection of Environmental Performance Indicators (EPIs) for a final product configuration. The framework and models have been applied in a real-life application and have proven that changes in product design or process selection can reduce the product's environmental impact and increase process efficiency. Hence, manufacturers can use the framework and models during the Advanced Product Quality Planning (APQP) process to benchmark each product variation that they manufacture in a standardised manner and realise cost saving opportunities

DESIGN, DEVELOPMENT AND TO FORMULATE ANTIMICROBIAL GEL OF TOONA CILIATA ROEM. LEAVES AND FICUS BENGALENSIS LINN. STEM BARK.

The aim of the present study is to investigate the antimicrobial property and to formulate an antimicrobial Gel of Toona ciliata Roem. leaves and Ficus bengalensis Linn. stem bark. The antimicrobial activity was evaluated by using agar cup plate method and minimum inhibitory concentration against four microorganisms was determined. Soxhlet apparatus was used for successive extraction using solvents - petroleum ether, chloroform, methanol and water. And  to formulate polyherbal antimicrobial gel carbopol 940 was used as gelling agent. Petroleum ether extract was found to be the most effective of the three extracts. The antimicrobial activity was observed against the gram positive bacteria Staphylococcus aureus, Bacillus subtilis and gram negative bacteria Pseudomonas aeruginosa; and the fungus Candida albicans. The minimum inhibitory concentration (MIC) of Toona ciliata petroleum ether extract ranged from 40mg/ml-50 mg/ml and that of Ficus bengalensis petroleum ether extract from 10mg/ml-16 mg/ml at which selected organisms showed inhibition. Then using a range of concentrations of carbopol 940 different gel formulations were formulated with petroleum ether extracts of both plants and tested for their antimicrobial potential. Out of these F2 and F6 formulation were combined in different ratios  and one with ratio 3:7 was further tested for antimicrobial activity by Agar Cup method. It was found that ratio 3:7 have synergistic effect and possessed considerable antimicrobial activity and may serve as promising antimicrobial gel formulation. From this study, it can be concluded that Toona ciliata Roem. leaves and Ficus bengalensis Linn. stem bark exhibited antimicrobial activities against selected microorganisms.Keywords: Toona ciliata Roem., Ficus bengalensis Linn., Antimicrobial activity, In-vitro diffusion study, Agar cup plate method, MIC, Carbopol 940, Hydrogel, Cold method, Topical

Analysis of Hardware Descriptions

The design process for integrated circuits requires a lot of analysis of circuit descriptions. An important class of analyses determines how easy it will be to determine if a physical component suffers from any manufacturing errors. As circuit complexities grow rapidly, the problem of testing circuits also becomes increasingly difficult. This thesis explores the potential for analysing a recent high level hardware description language called Ruby. In particular, we are interested in performing testability analyses of Ruby circuit descriptions. Ruby is ammenable to algebraic manipulation, so we have sought transformations that improve testability while preserving behaviour. The analysis of Ruby descriptions is performed by adapting a technique called abstract interpretation. This has been used successfully to analyse functional programs. This technique is most applicable where the analysis to be captured operates over structures isomorphic to the structure of the circuit. Many digital systems analysis tools require the circuit description to be given in some special form. This can lead to inconsistency between representations, and involves additional work converting between representations. We propose using the original description medium, in this case Ruby, for performing analyses. A related technique, called non-standard interpretation, is shown to be very useful for capturing many circuit analyses. An implementation of a system that performs non-standard interpretation forms the central part of the work. This allows Ruby descriptions to be analysed using alternative interpretations such test pattern generation and circuit layout interpretations. This system follows a similar approach to Boute's system semantics work and O'Donnell's work on Hydra. However, we have allowed a larger class of interpretations to be captured and offer a richer description language. The implementation presented here is constructed to allow a large degree of code sharing between different analyses. Several analyses have been implemented including simulation, test pattern generation and circuit layout. Non-standard interpretation provides a good framework for implementing these analyses. A general model for making non-standard interpretations is presented. Combining forms that combine two interpretations to produce a new interpretation are also introduced. This allows complex circuit analyses to be decomposed in a modular manner into smaller circuit analyses which can be built independently

The 2010 Personal Firewall Robustness Evaluation

With the advent of cheaper Internet connections, the number of Internet connections among home users is on the rise. Generally, home users have little understanding of the security concerns associated with Internet connectivity. To protect against computer attacks, generally a home user may install a personal firewall on his/her computer. To determine the effectiveness of personal firewalls, evaluation tests were performed against the ten firewall products available to users at local electronic stores and listed on popular firewall security websites. The firewalls were tested in their default and maximum security mode. The investigation was carried out by performing a port scan and vulnerability scan attacks against a computer with no firewall protection and computers running personal firewalls. The results of the investigation established that the computers running the firewalls exhibited some or all of the vulnerabilities detected on a computer with no firewall protection

EFFECT OF SPROUTING ON IN-VITRO ANTIOXIDANT POTENTIAL OF SOME VARIETIES OF CHICKPEA SEEDS (CICER ARIETINUM LINN.)

 Objective: The aim of the present study is to access the effect of sprouting on in-vitro antioxidant potential of some varieties of Cicer aurantium Linn. (Chickpea seeds).Materials and Methods: The un-sprouted and sprouted seeds of its newly developed varieties viz. PBG-1, GPF-2, PBG-5 were powdered and then was extracted with methanol by direct maceration method and their antioxidant activity was evaluated using four different methods (Reducing power, Free radical scavenging activity (DPPH), total antioxidant activity and ferric reducing antioxidant power activity). Results: Sprouting clearly increased the flavonoid content of seeds to 104%, 117%, 341% respectively. There was visible increase in total antioxidant activity of methanol extracts of sprouted seeds which was evident by increased activity from 0.298 to 0.397 at concentration 60 µg/ml in var. PBG-5 and increased ferric reducing antioxidant power from 0.332 to 0.387 at concentration 40 µg/ml in var. PBG-1. Conclusion: Thus it may be concluded from the study that sprouting visibly increased the in-vitro antioxidant activity of selected chickpea seeds varieties