Synthetic studies of diphenyl ether and anthraquinone natural products

Organic chemistry is the most important branch of chemistry as it serves many aspects of human life. As the involvement of organic chemistry in our life increases, the need for the development of new synthetic methods for biologically active molecules also increases. The development of synthetic pathways for some biologically active compounds is explored in this thesis. Chapter one describes the first total synthesis of littorachalcone. This method is straightforward and operationally convenient so that it can be easily scaled up and applied for the preparation of littorachalcone and related compounds. Chapter two outlines a synthetic approach towards topopyrone-D. Metal-hydrogen exchange and metal-halogen exchange reactions were studied as key steps. Chapter three describes an approach towards the synthesis of rubianine which is a C-glycoside. In this chapter, various reactions were studied to make the C-glycoside bond to the anthraquinone moiety in an efficient manner. Chapter four describes a flexible synthesis for indoles. The effect of different substituents at the ortho-position of the starting aniline compound was studied

An Empirical Analysis of Training Facilities in Micro-Small-Medium Enterprises (MSME) for Self-Help Groups (SHG)

The article presents the findings from the study on the training facilities in Micro-Small-Medium Enterprises (MSME) for Self-Help Groups (SHG) in the Union Territory of Puducherry region. The research variables were adopted in accordance to the legal provisions of SHG. Primary data of 127 random sample of MSMEs was collected through a survey method using structured questionnaire. Results show that 63% of the MSME has not undertaken any training for their suppliers, while a good proportion of the companies consisting of 15.7% have provided between 1-2 trainings. The study argues that the policy maker should identify large number of economically development-oriented skills and include them in training programmes to encourage increased development of microenterprises

A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing

In the current industry 4.0 scenario, good quality cutting tools result in a good surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays a crucial role in manufacturing quality components. In addition to tool monitoring, wear prediction assists the manufacturing systems in making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model to predict the turning tool wear. Cutting forces, the surface roughness of a specimen, and flank wear of tool insert are measured for corresponding spindle speed, feed rate, and depth of cut. Those turning test datasets are applied in machine learning for tool wear predictions. The test was conducted using SNMG TiN Coated Silicon Carbide tool insert in turning of EN8 steel specimen. The dataset of cutting forces, surface finish, and flank wear is extracted from 200 turning tests with varied spindle speed, feed rate, and depth of cut. Random forest regression, Support vector regression, K Nearest Neighbour regression machine learning algorithms are used to predict the tool wear. R squared, the technique shows the random forest machine learning model predicts the tool wear of 91.82% of accuracy validated with the experimental trials. The experimental results exhibit flank wear is mainly influenced by the feed rate followed by the spindle speed and depth of cut. The reduction of flank wear with a lower feed rate can be achieved with a good surface finish of the workpiece. The proposed model may be helpful in tool wear prediction and making tool-changing decisions, which leads to achieving good quality machined components. Moreover, the machine learning model is adaptable for industry 4.0 and cloud environments for intelligent manufacturing systems

Exploring the Potential of Integrating Machine Tool Wear Monitoring and ML for Predictive Maintenance - A Review

This research review article explores the potential of integrating machine tool wear monitoring and ML algorithms for predictive maintenance. It synthesizes the latest research in the field, while discussing the benefits and challenges of various approaches. Specifically, this review examines the applications of sensors in machine tool condition monitoring, the use of ML algorithms to detect wear patterns and predict maintenance needs, and the potential of integrating ML and predictive maintenance. The article also evaluates the potential of using ML algorithms in conjunction with sensor data to improve tool performance and reduce maintenance costs. Finally, the article provides scope for future research to expand the potential of ML for predictive maintenance in machine tools. Overall, this review highlights the potential of integrating ML with predictive maintenance for machine tool applications

Impact of Food Safety and Standards Regulation on Food Business Operators

The main aim of the study is to understand food regulation and food safety in India context and global aspects. It also explains the major health problem and recent past reports of FSSAI. The primary data were collected from food business operators (mobile food vendors, many small scale hotels, and local restaurants). It also helps to ensure the knowledge and awareness level of the food business operators. This study is to understand the view of food-business operators in a B-grade city of India “Salem” in Tamil Nadu about the FSSAI regulations and the status of performance of the food safety department in maintaining safety and quality of food and also to know about the level of awareness among the usage of fortified foods among food-businesses. The Study concludes that the Food and Safety Officers should regularly visit for inspection and monitor the food business operators for the betterment of public health. There should be proper awareness about the ingredients are used in fortified food among food-businesses to eliminate malnutrition and avoid food adulteration. The future rules and regulations of FSSAI should be strictly implementing all over India and properly monitor their activities of food business operators to fulfil the FSSAI standards

Digital Twin Technology for Tool Condition Monitoring: A Review of Recent Research

This research review article examines the use of Digital Twin technology (DT) in Tool Condition Monitoring (TCM). DT is a powerful technology that enables the creation of an exact digital replica of real-world entities, such as machines and tools, providing an integrated representation of various physical and virtual components. By combining real-time data with digital models of the tools, DT can be used to monitor tool condition and detect potential issues before they become serious. This review article surveys recent research on the use of DT in TCM and discusses the challenges that need to be addressed in order to make DT a viable solution for industrial tool monitoring. It also provides insight into future directions for research in this field. The results of this review suggest that DT has great potential to revolutionize tool monitoring in the manufacturing industry

Clinical study on ocular manifestations of intracranial tumours

INTRODUCTION: Neuro-ophthalmology, an offshoot of the neurosciences, is a subspeciality which has not received its due share of recognition, probably because it does not entirely belong to one faculty, comprising of a team of Ophthalmologists, Neurologists, Neurosurgeons, Radiologists and even includes Primary care Physicians. This field is rapidly expanding with vastly improved and refined diagnostic and therapeutic modalities for various afflictions of the visual apparatus of neurologic origin. AIM OF THE STUDY: To study the incidence of ocular signs and symptoms in intracranial tumours. To correlate the ocular symptoms and signs with the location of the brain tumour. To study the value of ocular symptoms and signs in predicting the presence of an intra cranial tumour. MATERIALS AND METHODS: The study was conducted at Govt. Stanley Medical College and Hospital, Chennai involving the Departments of Ophthalmology, Neurosurgery, Neuromedicine and Radio Diagnosis. The period of the study was from October 2004 to October 2006. A total of 79 patients who presented with either ocular, neurological or both complaints, suggestive of a brain tumour, were thoroughly evaluated and followed up. Out of these 12 patients were discarded in the final analysis based on the guidelines followed and therefore a total of 67 patients were included in this study. SUMMARY: Out of the initial 79 patients, 67 were finally selected based on the criteria specified for the study. There were 32 male patients (47.76%) and 35 female patients (52.2%). The maximum incidence of brain tumours was seen in the 21-30 years age group (18 patients – 26.8%). CONCLUSION: Among the 67 patients studied, 21-30 year age group was the most commonly affected. Overall, there was no sex predilection on the whole in this study, though females were more commonly affected in the younger age group and the males more affected in the elderly group while the middle aged group were equally distributed. This study re-emphasizes the importance of Ophthalmologists, Neurosurgeons, Radiologists and Neurologists working as a unit towards early and accurate diagnosis with prompt management and patient care as far as intra cranial tumours are concerned to help reduce the morbidity