Volume 13 | Issue 1 |

 Abstract

The central objective of this project is to employ machine learning techniques to forecast natural disasters proactively. Utilizing statistical models applied to historical data, the system aims to predict future occurrences, incorporating Geographic Information System (GIS) data on tectonic plates and past floods. Through this approach, the model can be trained to anticipate floods and tsunamis, enabling early predictions that play a pivotal role in facilitating timely evacuation and disaster preparedness.Throughout the course of history, natural disasters have consistently posed a global-level threat, unleashing catastrophic events that cause significant harm to both lives and property. These disasters have not only eradicated entire civilizations but have also steered the course of human history. The toll exacted by natural disasters is immense, resulting in the loss of hundreds of thousands of lives and billions in property damage. Despite advancements in physical, mental, and technological aspects, there are challenges that humans cannot fully overcome, with natural disasters standing out as one such formidable force. While humanity may not possess the ability to prevent natural disasters entirely, adequate preparation lies within our control. Natural disasters can be broadly classified into geological and meteorological types. Despite ongoing efforts to predict earthquakes and other natural disasters, achieving precise predictions remains an enduring challenge.

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 Keywords

Machine learning Algorithms, Rainfall induced disasters, Hyperparameter Tuning, Time series forecastin.

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The most prevalent birth abnormality affecting the orofacial region that results from abnormal embryonic development is cleft lip and palate. It is a congenital condition that is characterized by the child's facial and oral cavity splits. Van der Woude syndrome (VWS) is an uncommon craniofacial abnormality that exhibits exceptional expression heterogeneity and autosomal dominant inheritance. The small salivary glands of the lips are the primary target of cheilitis glandularis (CG), an inflammatory disease with an unclear etiology. Melkersson-Rosenthal syndrome, which consists of recurrent orofacial edema, recurrent facial nerve palsy, and tongue fissuring, is another name for cheilitis granulomatosa.

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 Keywords

Cleft lip and palate, Van der Woude syndrome, Cheilitis glandularis, Cheilitis granulomatosa

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 Abstract

Koronavin Ruthra Thandavam

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Koronavin Ruthra Thandavam

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Abstract This research examines how the marketing mix influences brand personality, focusing on the effects of brand loyalty and brand communication. A causal-predictive design was used, and data were gathered from 183 entrepreneurs in Central Bicutan, Taguig City, through a structured questionnaire. The results show a strong relationship between the elements of the marketing mix: product, price, place, promotion, people, process, and physical evidence and brand loyalty. Each element contributes uniquely to how consumers develop attachments to brands, enhancing their overall loyalty. Furthermore, brand loyalty is found to significantly impact brand personality, which affects how consumers perceive brands in the marketplace. This perception shapes their buying decisions and influences their emotional connection to the brand. The study concludes that aligning marketing strategies with brand personality can enhance customer engagement and improve brand performance. Recommendations include customizing marketing mixes to build brand loyalty and using effective brand communication strategies to strengthen consumer relationships. By implementing tailored marketing strategies, businesses can better connect with their target audiences and foster brand advocates. This research provides valuable insights for businesses seeking to improve their marketing strategies and for academics interested in understanding branding dynamics more deeply. By exploring these connections, the study contributes to a better understanding of marketing practices and encourages future research in this field, ultimately aiming to bridge the gap between theoretical frameworks and practical applications in marketing. Keywords: marketing mix, brand personality, brand loyalty, brand communication

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marketing mix, brand personality, brand loyalty, brand communication, Polytechnic University of the Philippines.

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Mercury, in a natural and highly toxic element that is used in medicine, dentistry and traditional healthcare practices in many cultures over millennia. Due to its unique properties a number of applications have been made including dental amalgam and medical instruments; and traditional medicines such as Cinnabar in Chinese, Tibetan, Mongolian systems. Mercury's therapeutic benefits however, has been dulled by the its environmental persistence, high toxicity and health risk (acutely to chronically) typically resulting in increasing restriction and calls for safer alternatives. This review explores mercury's historical and contemporary uses, chemistry, pharmacology, toxicology and the environment in which it takes place, and addresses global efforts led by WHO (World Health Organization) and UNEP (United Nations Environment Programme) to minimize its dangers. The importance placed on education, policy innovation and sustainable practices in order to find a balance of mercury's utility versus the possibility of harm is stressed.

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Mercury, Medicine, Dentistry, Traditional Healthcare Practices, Dental Amalgam, Cinnabar, Environmental Persistence, Pharmacology, Toxicology.

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This document provides guidance for performance qualification of a RLAF. These article will be help full to person who engaged in dispensing of material specially where the quality, quantity, safety, efficacy & purity is big concern. It outlines objectives, responsibilities & requirements for qualifying the RLAF to ensure it performs reproducibly within specifications. Key areas covered include performance qualification procedures, acceptance criteria, documentation requirements, does & don'ts with respect to RLAF. Based on the objective, the aims of study were decide as "PQ of RLAF system". Due to its efficacy removal of 99.97% of airborne particles as small as 0.3 micron,8 this equipment is widely used in life science research, mushroom cultivation, microbiology, IVF, IUI and histopathology / pathology lab, plant tissue and cell culture and pharmaceutical and electronics industry and many more. Considering its susceptibility of operation here we also focused its cleaning procedure with safety precaution. In this article annexure for recording of qualification test reading also provided.

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RLAF, Dispensing Booth, airborne particles

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Strategy analysis and formulation are critical components of strategic management, enabling organizations to achieve their objectives in competitive and dynamic environments. This paper explores the theoretical foundations, tools, and processes involved in strategy analysis and formulation. By examining frameworks like SWOT, PESTLE, Porter's Five Forces, and the Resource-Based View (RBV), this paper outlines how organizations assess internal and external environments to develop effective strategies. Case studies and empirical insights highlight the practical applications of strategic tools, emphasizing their role in decision-making, competitive advantage, and long-term success.

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Strategy Analysis, Strategy Formulation, SWOT Analysis, Competitive Advantage

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When the wind power is connected to an electrical grid effects of the power quality. The effects of power quality measurements are the active power, reactive power, variation of voltage, flickers, harmonics and electrical behavior of the switching operation. The installation of the wind turbine with grid causes power quality problems determined by studying this paper. For this static compensator (STATCOM) with a battery energy storage system (BESS) at the point of common coupling to mitigate the power quality problems. The grid connected wind energy generation system for the power quality improvement by using STATCOM control scheme is simulated using SIMULINK power system blocked set. This is relives the main supply source from the reactive power demand of the load and induction generator in this proposed scheme. The improvement in the power quality on the grid has been presented here according to the guidelines specified in IEC-61400 standard (International Electro-technical commission) provides some norms and measurements.

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International Electro-technical commission (IEC), STATCOM, Battery Energy Storage System, (BESS), Total Harmonic Detection (THD), and Point of Common Coupling (PCC)

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This study investigates the evolving relationship between renewable energy consumption, economic growth, and sustainable development in India. As a key player in the global renewable energy market, India is actively transitioning towards cleaner energy sources to address environmental concerns and meet rising energy demands. The research analyzes the factors influencing renewable energy consumption, including renewable energy output, energy efficiency, economic growth (proxied by real GDP per capita), and carbon emissions. Findings suggest that these factors significantly impact the adoption and utilization of renewable energy. The study concludes by emphasizing the critical need for increased investment in renewable energy generation, energy efficiency technologies, and environmentally sound innovations to further accelerate the transition towards a sustainable energy future in India.

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Renewable Energy, Sustainable Development, Economic Growth, Carbon Emissions, Environmental Impact, Energy Consumption, Climate Change.

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detecting tax fraud is the highest priority of almost all of the tax administrations aiming at the maximization of revenues and most importantly, the high level of compliance. These methods, including data mining, machine learning and the other methods, such as traditional random auditing, have already been applied to a large proportion of studies to address tax fraud. Work of this study is to apply Artificial Neural Networks in order to detect tax fraud factors in income tax data. The findings indicate that the Artificial Neural Networks show strong behaviors in the tax fraud detection with an accuracy at 92%, a precision at 85%, a recall score at 99% and an AUCROC value at 95%. All businesses, cross-border or domestic, the size of the business, small businesses or corporate businesses, are to be found among the factors considered by the model to be of greater saliency for income tax fraud detection. In this work, the paper is in agreement with the previous closely related to the previous tax fraud feature that covered all tax types jointly using various machine learning models. To our knowledge, this paper is the first to apply Artificial Neural Networks in the detection of income tax fraud in Rwanda, by different parameters such as layers, batch size, and epochs tuning, to select the most appropriate parameters which perform better than other parameters, in terms of accuracy. In this work, for this subject, it is found that with a simple model, no hidden layer, softsign activation function, performs more excellent. The findings of this work will assist auditors to get a grasp of the factors contributing to income tax fraud in order to decrease audit work generated, to decrease audit costs, and to recover the money lost due to an income tax fraud.

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 Keywords

INCOME TAX FRAUD DETECTION

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