The Effect of Adopting Green Technology on Small and Medium Enterprises: A Case Study of Govindpura Industrial Area of Bhopal

GT encompasses a continuously evolving group of methods or materials, from techniques for generating energy to non-toxic cleaning products. It is that innovation which reduces waste by changing patterns of production and consumption. It is also defined as environmental healing technology, which reduces environmental damages created by the products and technologies for peoples' conveniences. Most of the businesses may not be very excited to “go green” by reducing emissions since their motto is to make money not to save the planet. The global strategy should be to help businesses do both. Reliable data on emerging technologies for sustainability assessment are still inadequate; the recommendations are largely based on qualitative methods and on an operational definition of sustainability using priority indicators. When 1901 industrialization started and today there is a globalization in whole world market there is an issue of sustainable development in all industrial development views. From energy conservation to the utilization of renewable energy in every area there is a urgent need for sustainable industrial development for sustainable and eco friendly social all dimensional growth .In this paper this has been discussed that the importance of sustainability for eco friendly growth in industries area of Govindpura region of Madhya Pradesh which is the main industrial area of Bhopal region. I had tried to calculate the carbon foot print of that region & the energy conservation with the help of omnipresent, freely available solar PV panels. Key Words – Green technology (GT), Non toxic cleaning product, Sustainability, carbon emission

Effect of arbuscular mycorrhizal (AM) fungi inoculation on enzymatic activity and zinc uptake under direct seeded rice system

The application of treatment T3 (Glomus mosseae + 100 % RDF NK) produced significantly more root volume by 72.60 %, 17.80 %, 12.25 %, 14.13 % over the application of treatment T1 (Control), treatment T5 (Glomus coronatum+ 100 % RDF NK), T6 (Gigasporadecipein + 100 % RDF NK) and T7 (BAU AM-1(Glomus sp + 100 % RDF NK), respectively. Similar trend shows at harvesting stage, here the maximum root volume (23c.c) was recorded by the application of T3 (Glomus mosseae + 100 % RDF NK). Maximum AM colonization and spore count was observed at panicle initiation stage with the application of treatment T3 (Glomus mosseae + 100 % RDF NK). This treatment also gave maximum dehydrogenase activity (55.86 µg TPF g-1 24 hr-1), acid phosphatase activity (0.299 mg PNP g-1 hr-1) and alkaline phosphatase activity (0.54 mg PNP g-1 hr-1) at panicle initiation stage. Application of treatment T3 (Glomus mosseae + 100 % RDF NK) significantly increased DTPA extractable Zn in soil and Zn content in plant when compared with all the treatments except treatment T6 (Gigasporadecipien+ 100 % RDF NK). The maximum zinc uptake (0.056 mg pot-1) by grain was recorded under treatment T3 (Glomus mosseae + 100 % RDF NK) followed by application of treatment T6 (Gigasporadecipien + 100 % N and K). Highest grain yield (14.08 g pot-1) was found with the treatment T3 (Glomus mosseae + 100 % RDF NK). As evident from the results, the AM fungal inoculation can effectively modify the soil microbe population and community structure by increasing the soil enzymatic activities and significantly increased the zinc uptake by grain in direct seeded rice (DSR)

Exploring Privacy-Preserving Methods via Perturbation Data Mining Employing Diverse Noise Strategies

Knowledge discovery from data, commonly referred to as data mining. it involves the extraction of significant information, which may be previously unknown, concealed, or relevant, from extensive data sets or databases through the utilization of statistical methodologies. With the introduction of enhanced hardware technologies, there has been a proliferation in the storage and recording of personal data pertaining to individuals. Sophisticated organizations employ data mining algorithms to uncover hidden patterns or insights within data. Data mining techniques find application in diverse fields such as marketing, medical diagnosis, forecasting system, and national security. However, in scenarios where data privacy is paramount, mining certain types of data without violating the privacy of data owners presents a formidable challenge, sparking growing concerns among privacy advocates. To address these concerns, it is imperative to advance data mining procedures that are complex to individual privacy considerations. Perturbation of data plays a pivotal role in Privacy-Preserving Data Mining (PPDM). Additive data safeguard data privacy. In contrast, multiplicative data perturbation involves a series of transformations, including rotation, translation, and the addition of noise components to the perturbed data copy

THE ECOSYSTEM OF THE MIND: EVALUATING THE INTERFACE OF BIODIVERSITY AND MENTAL WELLNESS THROUGH GREEN PSYCHO-CHEMISTRY

This abstract offers a succinct summary of a thorough investigation into the complex interrelationships between biodiversity, psychological well-being, and the newly developing discipline of green psycho-chemistry. There is a growing understanding of the significance of natural surroundings and their ability to have a positive impact on mental health as society struggles with the rising prevalence of mental health illnesses. This research explores the holistic effects of the variety of plant and microbial life within ecosystems on the functioning of the human mind. The multidisciplinary study examines the psychotropic substances made by various plant and microbial species. It draws on ecology, psychology, and chemistry. It tries to learn how these substances could improve mental health, lessen stress, and lessen the signs of mental health issues. The process includes a lot of laboratory analysis, psychological testing, and fieldwork. As they investigate various ecosystems, researchers will take samples of microbes and plants in order to separate and examine bioactive chemicals. Participants will also take part in controlled experiments to assess the advantages of exposure to these substances for mental health, taking into account aspects like stress reduction, mood enhancement, and cognitive performance. The study will also evaluate the cultural and socioeconomic elements that affect how people perceive and use natural areas to better their mental health. Additionally, it will look at the moral and environmental ramifications of using biodiversity for psycho-chemical purposes. The results of this study have the potential to revolutionize methods for fostering psychological well-being by underlining the critical function that ecosystems play in doing so. The research may also pave the way for the creation of brand-new therapeutic approaches and environmentally friendly medicines that draw inspiration from nature. In the end, this research advances our knowledge of the intricate interactions between the environment and the human mind, paving the way for a more comprehensive and long-lasting approach to mental health care

Composability of transactions using closed nesting in software transactional memory

With the boom in the development of multi-core machines and the development of multi-threaded applications as such, concurrent programming has gained increasingly more significance than ever before. However, concurrent programming using traditional methods such as locks, mutex and monitors is not easy, as they require a programmer to predetermine the lock management scheme for each case. This approach is error-prone. Besides, it is very difficult to trace the bugs in such programs. Software transactional memory (STM) is a new technology that solves this problem by offering automatic management of locks. As such, in recent years STM has gained a lot of attention in both industry and academia. However, most of the work in STM is restricted to non-nested transactions, while the domain of nested transactions remains largely unexplored. One of the striking features of STM is its ability to support composability of transactions through three types of nesting, namely at nesting, closed nesting and open nesting. In this thesis, we study the complexities involved in designing STM protocols for closed nested transactions. To this end, we extend Imbs and Raynal's STM protocol [1], which is designed for non-nested transactions, to closed nested transactions. We propose several extensions, employing different modes of concurrency for subtransactions in the transaction tree : (i) serial execution (no concurrency) of subtransactions at each level; (ii) pessimistic concurrency control at all nodes; (iii) optimistic concurrency control at all nodes; and (iv) a mixture of optimistic concurrency control at some nodes while pessimistic concurrency control at other nodes in the same transaction tree