Research Paper Research Articles

Optimizing Indoor Air Quality to mitigate Sick Building Syndrome

- In current scenario, where individuals spend the majority of their time indoors, the quality of the indoor air has become increasingly crucial. This research paper investigates the role of optimizing Indoor Air Quality (IAQ) as a proactive strategy for mitigating Sick Building Syndrome (SBS) within built environments. It examines the relationship between indoor air quality (IAQ) and Sick Building Syndrome (SBS) in workplace settings and explores strategies for enhancing IAQ to mitigate SBS symptoms. Indoor air quality (IAQ) significantly impacts human health and well-being, with implications for productivity, comfort, and overall quality of life. To ensure safe and healthy indoor environments, various organizations, including the World Health Organization (WHO), Environmental Protection Agency (EPA), and Occupational Safety and Health Administration (OSHA), have established IAQ standards and guidelines. This research aims to conduct a comprehensive analysis of these standards. Moreover, the multifaceted nature of IAQ management is explored through the inclusion of parameters like radon (Rn), relative humidity (RH), and temperature. These factors not only contribute to indoor air quality but also affect occupants' comfort and well-being. By considering both chemical and environmental factors. Key Words: Indoor air quality, mitigate, policies, Sick building syndrome

Beetaloo’s gas bounty: potential for LNG exports and domestic supply

Recent drilling in the Beetaloo Basin has yielded promising results for the emerging shale gas play, enabling the explorers to secure funding and establish a range of non-binding commercial agreements. Notably, Beetaloo wells have exhibited initial production rates over 30 days (IP30), spanning from 1.1 to 5.2 mmcf/day, and there is an evident trend of increasing frac stages with each new well as operators continuously optimise stimulation. This research paper presents comprehensive Beetaloo modelling results, encompassing well type curves, a comparative analysis with the renowned Marcellus shale play in the United States (frequently used as a reference point for Beetaloo), production forecasts, economic assessments, and an examination of potential commercialisation pathways. Based on our modelling findings, Beetaloo’s estimated ultimate recovery (EUR) per well ranges from 7 to 14 bcf, with deeper wells into the Velkerri B Shale demonstrating relatively higher productivity. We anticipate that, with full-scale development, Beetaloo could attain peak production rates of 1 bcf/day, supported by a peak annual drilling rate averaging 50–60 wells. Overall, the Beetaloo Basin possesses the potential to recover close to 8 tcf of low reservoir CO2 gas, opening doors to the possibility of liquefied natural gas (LNG) exports reaching up to 4.5 mtpa and domestic gas supplies of around 94 mmcf/day, limited to the existing infrastructure capacity connecting to the east coast gas markets.

Digital Transformation and E-commerce Adoption: A Comparative Analysis with a Focus on Azerbaijan

This research paper thoroughly investigates the dynamics of digital transformation and e-commerce on a global scale, with a spotlight on Azerbaijan's unique journey. A key focus is placed on Azerbaijan's regulatory landscape, examining the enactment of progressive policies to foster a secure and robust digital commerce environment. This includes assessing laws and regulations introduced to protect online consumers, facilitate digital payments, and encourage the growth of e-commerce startups. Moreover, the contribution of international organizations, such as the World Bank Group and the Asian Development Bank (ADB), in supporting Azerbaijan's digital economy is critically evaluated. These efforts are positioned within the broader context of enhancing digital infrastructure, improving internet accessibility, and promoting digital literacy, which is pivotal for overcoming existing barriers to e-commerce adoption in the country. The paper culminates in offering strategic recommendations tailored to Azerbaijan's context. It suggests pathways for overcoming persistent challenges in e-commerce adoption, enhancing the digital infrastructure, and leveraging emerging opportunities to drive economic growth and digital inclusivity. The recommendations aim to support policymakers, businesses, and international partners in harnessing the full potential of digital transformation as a catalyst for economic development and competitive advantage on the global stage.

Towards a semi-automatic classifier of malware through tweets for early warning threat detection

This paper presents a method for developing a malware ontology structure by detecting malware instances on Twitter. The ontology represents a semi-automatic classifier fed by the data extracted from tweets. In particular, the automatic part of the presented methodology relies on a pattern-based approach to detect trigger expressions leading to new information about malware, whilst the manual one covers the evaluation of the results by domain-experts, who also validate the reliability of the semantic relationships within the ontology framework. We present preliminary results on the application of our methodology to tweets extracted from MalwareBazaar database showing how the documents’ collection analysis, through Natural Language Processing (NLP) tasks, can support the knowledge retrieval and documents’ classification procedures for building early warning system of detected malware. Results obtained from this research paper within the time framework of 2023 are referred to the previous version of the current social network X.

Ethical Issues in Educational Research

Ethical issues are important aspects of research to make research practice honest and transparent. The researcher must follow specific rules and ethical guidelines to meet the expectations of the research community and academia. These rules and regulations are for fairness and integrity in the field of research. In this research paper, the authors have made a solemn effort to delineate the critical ethical issues and how they can degrade the quality of research and cause trouble to any investigator. This paper focuses on ethical issues, ethical standards, guidelines, and concerns of artificial intelligence so that no researcher faces any problem before, during, or after the study. As technology expands and its integration into education, teaching, and research emerges, the authors have stipulated that artificial intelligence can be a concern and threat to ethical standards in educational research.

Handwritten Character Recognition: A Comparative Study

Handwriting recognition is a technique used to interpret intelligible handwritten input and convert it into digital text using Machine Learning tools. This research paper provides a comparison of the application of CRNN and CNN for handwriting recognition, using a dataset containing about 370,000 handwritten names. Our experiments demonstrate that the CRNN hybrid model produces the highest accuracy compared to the CNN model. This paper summarises contributions reported on the A-Z Handwritten Alphabets in .csv format dataset for handwritten character recognition. This dataset has been extensively used to validate novel techniques in computer vision. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset

Misspellings or "miscellings"-Non-verifiable and unknown cell lines in cancer research publications.

Reproducible laboratory research relies on correctly identified reagents. We have previously described gene research papers with wrongly identified nucleotide sequence(s), including papers studying miR-145. Manually verifying reagent identities in 36 recent miR-145 papers found that 56% and 17% of papers described misidentified nucleotide sequences and cell lines, respectively. We also found 5 cell line identifiers in miR-145 papers with misidentified nucleotide sequences and cell lines, and 18 cell line identifiers published elsewhere, that did not represent indexed human cell lines. These 23 identifiers were described as non-verifiable (NV), as their identities were unclear. Studying 420 papers that mentioned 8 NV identifier(s) found 235 papers (56%) that referred to 7 identifiers (BGC-803, BSG-803, BSG-823, GSE-1, HGC-7901, HGC-803, and MGC-823) as independent cell lines. We could not find any publications describing how these cell lines were established. Six cell lines were sourced from cell line repositories with externally accessible online catalogs, but these cell lines were not indexed as claimed. Some papers also stated that short tandem repeat (STR) profiles had been generated for three cell lines, yet no STR profiles could be identified. In summary, as NV cell lines represent new challenges to research integrity and reproducibility, further investigations are required to clarify their status and identities.

Analysis and Design of Water Distribution Network for Jabalpur Cantonment Board Area

In this research paper focuses on studying and improving the water distribution network in the Jabalpur Cantonment Board Area. Water distribution networks are crucial infrastructures for ensuring reliable water supply to communities. However, many areas, including Jabalpur Cantonment, face challenges related to water distribution efficiency, reliability, and equitable access. The study begins with a comprehensive analysis of the existing water distribution network in the Jabalpur Cantonment Board Area. Factors such as network layout, pipe material, age of infrastructure, and water demand patterns are thoroughly examined to identify existing inefficiencies and areas for improvement. Using advanced engineering tools such as WaterGems and methodologies, an optimized design for the water distribution network is proposed. This design considers factors such as hydraulic efficiency, pressure management, and resilience to minimize water losses and ensure consistent water supply to consumers. Furthermore, the study incorporates considerations for future growth and development in the Jabalpur Cantonment Board Area. By employing sustainable design practices and incorporating modern technologies, the proposed water distribution network aims to meet the present and future water needs of the community while minimizing environmental impact. By implementing the proposed design improvements, the Jabalpur Cantonment Board can achieve a more sustainable, efficient, and reliable water supply network, ultimately improving the quality of life for its residents.

SHOP-EZ

The rapid evolution of technology has transformed the landscape of commerce, ushering in an era dominated by electronic transactions and online marketplaces. This research paper aims to provide a comprehensive analysis of the emerging trends and challenges in the field of e-commerce. The study encompasses a broad spectrum of topics, including technological advancements, consumer behavior, regulatory frameworks, and the impact of global events on e- commerce ecosystems. The first section of the paper explores the latest technological trends shaping the e-commerce industry, such as artificial intelligence, blockchain, and augmented reality. Special attention is given to their influence on enhancing user experience, personalization, and security. The paper also delves into the integration of these technologies and their potential to revolutionize supply chain management and logistics in e-commerce operations. Consumer behavior is a dynamic aspect of e-commerce that requires constant scrutiny. The second section of the paper examines the evolving preferences and expectations of online consumers. It investigates factors influencing purchasing decisions, the role of social media, and the growing significance of sustainable and ethical practices in shaping consumer choices.

Enhancing Fault Identification, Classification and Location Accuracy in Transmission Lines

This research paper presents a proposed system for fault identification, classification and location in transmission lines using a Support Vector Machine (SVM)-based technique in conjunction with a Positive Sequence Analyzer. The objective is to develop an accurate and reliable method for identifying, classifying and locating different fault types in transmission lines. The proposed system leverages the capabilities of SVMs in handling high-dimensional feature spaces and the fault signature extraction capabilities of the Positive Sequence Analyzer. Experimental evaluations are conducted to assess the performance and effectiveness of the proposed system, comparing it with existing fault identification and classification methods. The results demonstrate the superior performance and robustness of the SVM-based technique utilizing the Positive Sequence Analyzer, providing a valuable contribution to fault management and system reliability in transmission line networks.

Synthesis of highly luminescent core-shell nanoprobes in a single pot for ofloxacin detection in blood serum and water.

Antibiotics are commonly used as antibacterial medications due to their extensive and potent therapeutic properties. However, the overconsumption of these chemicals leads to their accumulation in the human body via the food chain, amplifying drug resistance and compromising immunity, thus presenting a significant hazard to human health. Antibiotics are classified as organic pollutants. Therefore, it is crucial to conduct research on precise methodologies for detecting antibiotics in many substances, including food, pharmaceutical waste, and biological samples like serum and urine. The methodology described in this research paper introduces an innovative technique for producing nanoparticles using silica as the shell material, iron oxide as the core material, and carbon as the shell dopant. By integrating a carbon-doped silica shell, this substance acquires exceptional fluorescence characteristics and a substantial quantum yield value of 80%. By capitalising on this characteristic of the substance, we have effectively constructed a fluorescent sensor that enables accurate ofloxacin analysis, with a detection limit of 1.3 × 10-6 M and a linear range of concentrations from 0 to 120 × 10-6 M. We also evaluated the potential of CSIONPs for OLF detection in blood serum and tap water analysis. The obtained relative standard deviation values were below 3.5%. The percentage of ofloxacin recovery from blood serum ranged from 95.52% to 103.28%, and from 89.9% to 96.0% from tap water.

Exploring Gender Dynamics in Indian English Poetry: A Feminist Literary Study

This research paper offers an extensive exploration of gender dynamics within Indian English poetry. The study adopts a chronological approach, tracing the evolution of gender portrayal from the early to the contemporary phase. The research objectives are to identify recurring themes and narratives related to gender, to compare these findings with existing literature, and to analyze their broader implications. The methodology involves a thematic analysis of selected poems, drawing from various literary periods. The key findings reveal a transformative shift in gender portrayal, from stereotypical depictions in early poetry to nuanced narratives in contemporary works. The study identifies specific themes such as the emerging female voice, exploration of gender identity and intersectionality, evolving portrayal of masculinity, and the depiction of female empowerment and feminine spaces. The findings also fill gaps in the existing literature by identifying new themes such as caste and gender intersectionality and queer narratives. The research has significant implications for understanding the evolution of gender portrayal and feminist discourse within Indian English poetry. It also offers a richer understanding of the gender dynamics within the South Asian context, thereby contributing to the fields of literary studies, gender studies, and feminist theory.

Neutrosophic linear fractional optimization problem using fuzzy goal programming approach

This research paper develops an algorithm to solve a neutrosophic linear fractional optimization problem, where the cost of the objective functions, the technology coefficients, and the resources are neutrosophic numbers (triangular and trapezoidal numbers). Two defuzzification methods are used here to convert the neutrosophic linear fractional optimization problem into a multi-objective linear fractional optimization problem (MOLFOP). The fractional objective function is defuzzified using the component-wise optimization method, and the linear constraints are defuzzified using the aggregation ranking function strategy. Hence, the MOLFOP is then solved using the fuzzy goal programming method. In order to further explain our suggested algorithm, two numerical examples are solved at the end, and the outcomes obtained by our method are compared with the outcomes obtained using the other algorithm.

RISK EVALUATION OF BLOCKCHAIN POWERED SUPPLY CHAIN FINANCING

This paper seeks to conduct a comprehensive risk assessment of blockchain-enabled supply chain financing serving as the basis for supply chain operations of Tomorrowland. The purpose is to identify main risks associated with blockchain implementation in supply chain finance and ways to mitigate them to enable the most secure and reliable financial processes possible. This research paper delves into the critical examination of risk factors associated with blockchain-enabled supply chain financing. Through a thorough literature review and empirical analysis, this study identifies and evaluates the various risks inherent in utilizing blockchain technology for supply chain financing. The findings provide valuable insights for stakeholders to mitigate risks and optimize the efficiency and security of supply chain finance processes. KEYWORDS: Research Paper, Blockchain, Management, Risk Evaluation, Empirical Analysis

An Innovative Study on Stock Price Prediction for Investment Decision Through ARIMA and LSTM with Recurrent Neural Network

The securities market is extremely volatile and difficult to prognosticate. Stock prices are depending upon numerous factors. To reduce the risk of volatility, it is very important to apply an accurate mechanism to forecast stock prices. The importance of share price prediction forecasting in finance and economics has sparked researchers’ interest in creating more reliable forecasting models over time. In this research paper, the researchers try to explore two different applications based on linear and nonlinear (RNN) functions. The criteria for the stock price predictions are evolved using Auto Regressive Integrated Moving Average (ARIMA) which takes the linearity function from the past share prices. The ARIMA model assumes the future prices usually be similar to past. Sudden changes may not reflect in this model. The nonlinearity Recurrent Neural Network (RNN) is going to be applied for share price prediction so that it can be taken into account the quick changes that are occurring in the market environment. To test the RNN, the study used the Long Short Term Memory (LSTM) model which takes the support of Artificial Intelligence. Taking the sample of share prices of banks listed in the NIFTY index, the ARIMA and LSTM have been performed and analyzed. Stock price predictions for banks listed in the NIFTY bank index are found better with the ARIMA model than with the LSTM model.

Discover Use Linen Back Baby Carriers in Social Science of Ancient Egypt

This article presents a thought-provoking perspective on a possible link between ancient Egyptian design and ergonomics. Remember that this is an ongoing field of research, and new discoveries or interpretations may challenge or refine the presented ideas. Always strive to critically evaluate sources and consider alternative perspectives when exploring historical topics.The Pharaohs of Egypt innovate the science of ergonomics and the design of infant carriers. Definitive proof of photos ancient Egypt, the explicit evidence does of back baby carriers being used in ancient Egypt, even within the royal household. Considering the cultural context, practical advantages, and potential adaptations for royalty, further research holds promise in unlocking this fascinating aspect of childcare in a bygone era.This article is intended for research informational purposes and should be depended of research professional ergonomics medical advice. And this research paper is a starting point and can be further expanded upon by including specific citations, additional research findings, and personalized discussions based on your specific interests and areas of focus.

A Literary and Interpretive analysis of Bihu Songs' Identity

A Literary and Interpretive analysis of Bihu Songs' Identity

Enhancing the performance of zero energy buildings with boosted coyote optimization and elman neural networks

This research paper aims to optimize electrical system modeling to minimize CO2 emissions, improve reliability, and reduce costs. It proposes integrating Elman Neural Network (ENN) and metaheuristic algorithm to evaluate environmental impact and optimize system performance. The goal is to create a mathematical model considering renewable energy sources, grid energy, CO2 emissions, and maintenance costs, and use metaheuristic algorithms to determine the optimal combination of wind turbines and photovoltaic panels. To train the ENN model and optimize the system, a three-stage approach is proposed. Quasi-random numbers are generated using Sobol techniques to create input data, which is then processed by TRNSYS for simulation. The proposed approach incorporates an ENN with Boosted version of the Coyote optimization algorithm to accurately model and predict the behavior of Hybrid Renewable Energy Systems for Zero Energy Buildings. The results demonstrate the effectiveness of the proposed approach. Our method significantly reduces energy consumption, costs, and system reliability, achieving a 25% reduction in energy consumption and 12% reduction in system cost, and is effective in optimizing hybrid solar/wind and hydrogen storage systems. The optimal system configuration significantly reduces CO2 emissions, achieves a high level of system reliability, and minimizes costs. Comparative analysis with previous studies showcases the superiority of the proposed method in achieving comprehensive optimization. In conclusion, this study combines AI techniques and metaheuristic algorithms to optimize electrical system design, considering environmental, technical, and economic factors. The proposed approach overcomes limitations in previous studies and offers an effective solution to address the optimization problem.

The Impact of Music on Orchid plants Growth in Polyhouse Environments

Nature and music, two seemingly disparate elements of human experience, are deeply interconnected in ways that resonate across cultures and time. Both evoke emotions, stir the soul, and inspire creativity, offering profound insights into the human condition and our relationship with the world around us. In essence, nature and music are two expressions of the same universal impulse—the longing to connect with something greater than ourselves, to find beauty and meaning in the world around us. Whether we're listening to the songs of birds at dawn or the symphonies of Beethoven, we are reminded of our place in the vast tapestry of creation, where every note, every sound, is a testament to the wonder and mystery of existence. By carefully considering the environmental factors and designing experiments that account for them, researchers can gain a better understanding of how music influences plant growth and optimize its use in agricultural and horticultural practices. This research paper investigates the potential effects of music on orchid growth within polyhouse environments. Orchids are renowned for their delicate beauty and are cultivated worldwide, often in controlled environments like polyhouses. While studies have explored the influence of various environmental factors on orchid growth, the impact of music remains relatively unexplored. This study aims to fill this gap by examining how exposure to music may affect the growth, development, and overall health of orchids in polyhouse conditions. The research employs a combination of experimental methods, including controlled trials, measurements of plant growth parameters, and analysis of physiological indicators. Additionally, the study considers factors such as the type of music, frequency, volume, and duration of exposure. The findings of this research contribute to our understanding of the complex relationship between plants and their acoustic environment, with potential implications for optimizing orchid cultivation practices in polyhouse settings.

Empowering Communication: A Raspberry PI-Based Smart Noticeboard System

This research paper explores the implementation of a Smart Notice Board that leverages IoT and single-board computers, such as Raspberry Pi, to revolutionize traditional communication systems. It delves into the technical aspects of integrating these technologies to create a dynamic platform for real-time information dissemination. Additionally, it investigates how features like remote updating, authentication mechanisms, and environmental considerations enhance the functionality and sustainability of the Smart Notice Board. Ultimately, the paper aims to showcase the potential of this innovative solution in improving communication efficiency, security, and environmental impact within organizational and institutional settings.