Research Paper Research Articles

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network

ABSTRACT Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the “Symmetric Convolution-based Residual Attention Network (SC-RAN)” is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.

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.

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.

Research on the Differences Between the Laws Designed to Protect Brands and Traditional Trademark Laws

Brand protection is an important and challenging issue in the contemporary market, as brands are valuable and influential intangible assets that create and sustain competitive advantage. However, there is a lack of a clear and comprehensive legal category that can adequately address the diverse and complex aspects of brand protection. This paper aims to explore how a law designed to protect brands might differ from traditional trademark law, which mainly focuses on the registration and protection of distinctive signs that identify the source and quality of goods or services. The paper adopts a qualitative and comparative approach, using online sources, such as web articles, research papers, and guides, as well as examples of famous brands and cases, to illustrate and analyze the possible ways, characteristics, challenges, and benefits of brand protection. The paper concludes that brand protection is a useful or viable legal category that recognizes and respects the value and importance of brands as intangible assets that create and sustain competitive advantage in the market, adapts and innovates to changing market trends, consumer preferences, and competitive forces, and harmonizes and cooperates with the existing trademark laws and standards across different countries and regions.

The State of Knowledge of CCA Diversity in the Caribbean Coral Reefs

Crustose coralline algae (CCA) are a diverse and ecologically important species found in most of the world’s oceans. The current lack of taxonomic knowledge and relative abundance compromises our ability to predict species diversity numbers and, thus, their ecological roles and impacts on coral reefs. To gather a better understanding of the state of knowledge of crustose coralline algae taxonomy in the Caribbean, 107 different research papers, and other primary and secondary literature were studied; any source with taxonomical information, species identification, or genetic markers for identification was recorded. All Genebank codes were collected and sorted by supposed species marker and then ran through the National Center of Biotechnology Information. The location these genetic markers were gathered from was compared to the natural habitat range of the species, based on the Algaebank habitat description. Of the supposed 83 described species of crustose algae in the Caribbean, based on morphological characteristics, only 24 total were confirmed by DNA markers. This leaves at least 59 species of CCA to be confirmed in the Caribbean Sea with molecular markers. This indicates the importance of DNA barcode survey studies to assess the accurate diversity of this group in the region. With this limited knowledge apparent, it should be seen that a CCAs phylogenetic and taxonomical review must be done. An in-depth assessment should be conducted on CCA collections to identify the Caribbean species correctly and thus know their biodiversity in local habitats.

Solubility-permeability interplay of hydrotropic solubilization of piroxicam

Objectives In this research paper, an investigation has been made to assess the simultaneous effect of a solubility enhancement approach, i.e., hydrotropy on the solubility and apparent permeability of piroxicam. The solubility of piroxicam (PRX) a BCS (biopharmaceutics classification system) class II drug has been increased using a mixed hydrotropy approach. This study is based on identifying the pattern of solubility-permeability interplay and confirming whether every solubility gain results in a concomitant decrease in permeability or permeability remains unaffected. Method Solid dispersions of PRX were formulated using two hydrotropes, viz., sodium benzoate (SB) and piperazine (PP) by solvent evaporation method. A comprehensive 32factorial design was employed to study the effect of hydrotropes on the solubility and permeability of PRX. Subsequently, PRX tablets containing these solid dispersions were formulated and evaluated. Key findings SB and PP displayed a significant increase in the solubility of PRX ranging from 0.99 to 2.21 mg/mL for F1–F9 batches attributed to the synergistic effect of hydrotropes. However, there is a reduction in PRX permeability with increasing hydrotrope levels. The decline in permeability was notably less pronounced compared to the simultaneous rise in aqueous solubility of PRX. Conclusion An evident tradeoff between permeability and solubility emerged through the mixed hydrotropic solubilization for PRX. As PRX has generally higher intrinsic permeability, it has been assumed that this permeability loss will not affect the overall absorption of PRX. However, it may affect the absorption of drugs with limited permeability. Therefore, solubility permeability interplay should be investigated during solubility enhancement.

Can Plant Extracts Help Prevent Hair Loss or Promote Hair Growth? A Review Comparing Their Therapeutic Efficacies, Phytochemical Components, and Modulatory Targets

This narrative review aims to examine the therapeutic potential and mechanism of action of plant extracts in preventing and treating alopecia (baldness). We searched and selected research papers on plant extracts related to hair loss, hair growth, or hair regrowth, and comprehensively compared the therapeutic efficacies, phytochemical components, and modulatory targets of plant extracts. These studies showed that various plant extracts increased the survival and proliferation of dermal papilla cells in vitro, enhanced cell proliferation and hair growth in hair follicles ex vivo, and promoted hair growth or regrowth in animal models in vivo. The hair growth-promoting efficacy of several plant extracts was verified in clinical trials. Some phenolic compounds, terpenes and terpenoids, sulfur-containing compounds, and fatty acids were identified as active compounds contained in plant extracts. The pharmacological effects of plant extracts and their active compounds were associated with the promotion of cell survival, cell proliferation, or cell cycle progression, and the upregulation of several growth factors, such as IGF-1, VEGF, HGF, and KGF (FGF-7), leading to the induction and extension of the anagen phase in the hair cycle. Those effects were also associated with the alleviation of oxidative stress, inflammatory response, cellular senescence, or apoptosis, and the downregulation of male hormones and their receptors, preventing the entry into the telogen phase in the hair cycle. Several active plant extracts and phytochemicals stimulated the signaling pathways mediated by protein kinase B (PKB, also called AKT), extracellular signal-regulated kinases (ERK), Wingless and Int-1 (WNT), or sonic hedgehog (SHH), while suppressing other cell signaling pathways mediated by transforming growth factor (TGF)-β or bone morphogenetic protein (BMP). Thus, well-selected plant extracts and their active compounds can have beneficial effects on hair health. It is proposed that the discovery of phytochemicals targeting the aforementioned cellular events and cell signaling pathways will facilitate the development of new targeted therapies for alopecia.

Partial Endorsement in Commercial Paper: Reasons for Rejection and Justifications for Acceptance (Critical Study)

This research paper strives to demonstrate how commercial paper trades through endorsement and for this trading to be correct and effective for its impact, the endorsement must meet the necessary objectives and formal conditions for that. One of the special objective conditions for endorsement is that it should be on the full immutable right in the commercial paper, but opinions have emerged from jurisprudence and the judiciary considered that partial endorsement is valid and effective to raise, so this study is done to elaborate the reasons for the endorsement invalidity in general and to study partial endorsement in specific, to find out the motives adhered to by jurisprudence to consider partial endorsement invalid, as well as provided a response by balancing between acceptance of endorsement and the reasons for rejection. In conclusion, the introduction of a new exception to the rules of endorsement will not be unusual.

ADVANCED BUSINESS ANALYTICS IN TEXTILE & FASHION INDUSTRIES: DRIVING INNOVATION AND SUSTAINABLE GROWTH

This research paper explores the strategic implementation of advanced business analytics within EcoFashions Ltd., a mid-sized company in the textile and fashion industry. The central question it addresses is: How can a specific company leverage advanced analytics in the fashion sector for sustainable growth? Through a qualitative case study approach, the research examines the strategic implementation of analytics at EcoFashions to enhance operational efficiency, drive innovation, and promote sustainability. The findings reveal that the integration of analytics at EcoFashions led to a 25% reduction in material waste and a 15% increase in production efficiency, while also fostering product innovation responsive to market trends. These outcomes not only highlight the potential of business analytics to transform traditional practices within the fashion industry but also suggest that other firms can adopt similar strategies to achieve substantial improvements in sustainability and operational performance. The paper concludes with reflections on the limitations of a single case study and proposes directions for future research to validate and expand upon these findings across the broader industry. This study contributes to understanding how data-driven strategies can facilitate significant sector-wide advancements in sustainability and innovation in the textile and fashion industries.

In-depth characterization of particulate matter in a highly polluted urban environment at the foothills of Himalaya-Karakorum Region.

In recent years, the rising levels of atmospheric particulate matter (PM) have an impact on the earth's system, leading to undesirable consequences on various aspects like human health, visibility, and climate. The present work is carried out over an insufficiently studied but polluted urban area of Peshawar, which lies at the foothills of the famous Himalaya and Karakorum area, Northern Pakistan. The particulate matter with an aerodynamic diameter of less than 10µm, i.e., PM10 are collected and analyzed for mineralogical, morphological, and chemical properties. Diverse techniques were used to examine the PM10 samples, for instance, Fourier transform infrared spectroscopy, x-ray diffraction, and scanning electron microscopy along with energy-dispersive x-ray spectroscopy, proton-induced x-ray emission, and an OC/EC carbon analyzer. The 24h average PM10 mass concentration along with standard deviation was investigated to be 586.83 ± 217.70µg/m3, which was around 13 times greater than the permissible limit of the world health organization (45µg/m3) and 4 times the Pakistan national environmental quality standards for ambient PM10 (150µg/m3). Minerals such as crystalline silicate, carbonate, asbestiform minerals, sulfate, and clay minerals were found using FTIR and XRD investigations. Microscopic examination revealed particles of various shapes, including angular, flaky, rod-like, crystalline, irregular, rounded, porous, chain, spherical, and agglomeration structures. This proved that the particles had geogenic, anthropogenic, and biological origins. The average value of organic carbon, elemental carbon, and total carbon is found to be 91.56 ± 43.17, 6.72 ± 1.99, and 102.41 ± 44.90µg/m3, respectively. Water-soluble ions K+ and OC show a substantial association (R = 0.71). Prominent sources identified using Principle component analysis (PCA) are anthropogenic, crustal, industrial, and electronic combustion. This research paper identified the potential sources of PM10, which are vital for preparing an air quality management plan in the urban environment of Peshawar.

A Novel Framework on Real Time Chat Interface

In this extensive research paper, we delve deeply into the intricate process of developing a real-time chat interface website utilizing cutting-edge web technologies such as ReactJS, Chat Engine, sockets, hooks, props, JavaScript, and APIs. The primary aim of this project is to cater to the evolving demands for efficient communication and collaboration platforms in the contemporary digital landscape. Through meticulous analysis, detailed experimentation, and critical evaluation, we explore the functionalities, design principles, social relevance, and future implications of the developed application. This research paper serves as a comprehensive guide, providing in-depth insights into the utilization of modern web technologies for constructing interactive and engaging web applications, while also shedding light on the broader societal impacts and implications for the future of digital communication. The advancement of web technologies has facilitated the development of real-time communication platforms that transcend geographical barriers. This research paper delves into the comprehensive development process and technical intricacies involved in creating a real-time chat interface website using modern web technologies, including ReactJS for the frontend, Chat Engine for the backend, and auxiliary technologies such as sockets, hooks, props, JavaScript, and APIs. The project aims to address the growing need for efficient communication and collaboration platforms in today's digital landscape. Through detailed analysis, experimentation, and critical evaluation, this paper explores the functionalities, design principles, social relevance, and future implications of the developed application

FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

SURVEY ON FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and Mask R-CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

Improving English Language Proficiency through Podcasts: A Multifaceted Educational Approach

This paper probes into the strength of podcasts in building the English language through varied dimensions of a learning approach. The paper sought to establish the applicability of podcasts in the learning of languages among EFL students, given that podcasts are increasingly becoming an informal learning platform. The study was carried out among learners of different age groups and language proficiency levels, and the collection of statistics regarding the use of podcasts within language learning was affected through both quantitative and qualitative data. Important findings indicate that regular use of podcasts can make the process of vocabulary acquisition, listening comprehension, and general proficiency stronger. Such findings support the use of podcasts in gaining better language competence in that it not only presents rich contextual content in an accessible manner but also puts into play learner skills. In effect, the inclusion of podcasts into the learning curriculum has implications that are far-reaching in that it could offer an effective improvement in the traditional methodologies of language learning by enhancing a dynamic and flexible learning environment that meets the needs of all learners. This research paper points out the imperative nature of podcasts in modern educational strategies and suggests their application in a wider context of language learning frameworks to bring about superior linguistic competence.

Factors Influencing Bachelor’s Degree Students’ Satisfaction

This study delves into the factors influencing the satisfaction of Bachelor's Degree students in Ostrava, Czech Republic. With a focus on scholarly quality, support, and social aspects, it investigates key determinants such as academic environment, faculty support, campus facilities, and social activities. Employing a quantitative research approach, survey data from 30 participants were analyzed. Results reveal that factors like teaching methods, learning environment, and program affordability significantly impact satisfaction levels. The findings highlight the multidimensional nature of student satisfaction. For clarity purposes, it is necessary to highlight that the results of this research paper are drawn from a preliminary research study that was conducted in 2024 in the City of Ostrava in the Czech Republic.

A REVIEW ON TOTAL PRODUCTIVE MAINTENANCE (TPM)

Total Productive Maintenance (TPM) is transforming manufacturing landscapes. This review investigates its impact, revealing how TPM fosters a culture of continuous improvement through employee involvement and proactive maintenance strategies. By analyzing 30 research papers, it unveils the potent link between TPM and increased productivity alongside reduced waste. Highlighting Overall Equipment Effectiveness (OEE) as a crucial metric, the review emphasizes the importance of 5S as a foundational step for successful TPM implementation. With global adoption surging, TPM is solidifying its position as a world-class strategy for achieving peak manufacturing performance." Keywords: Total Productive Maintenance (TPM), OEE, Improve Productivity, 5S.

Snap-shop - An E-commerce Website

In the ever-evolving landscape of e-commerce, this research paper explores the transformative impact of four pivotal features: multimodal search, image-to-result technology, aug- mented reality (AR), and payment gateways. Multimodal search enhances product discovery through text and image inputs, breaking down barriers in the online shopping experience. Image- to-result technology bridges the gap between online and offline retail by enabling image-based product searches. Augmented reality revolutionizes product engagement, offering customers the ability to visualize products in their real-world environments. Payment gateways ensure secure and seamless transactions, safe- guarding the foundation of e-commerce. Through an examination of research, practical applications, and case studies, this paper provides insights into the current state and future potential of these e-commerce features. In a concise format, it offers valuable knowledge for businesses, researchers, and technology enthusiasts striving to harness the full potential of the e-commerce landscape. Index Terms—E-Commerce, Internet, Self-service, Technology, Internet banking, Marketing, Business.

Insights into Cybercrime Detection and Response: A Review of Time Factor

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration.

Knowledge, Perception, and Associated Factors of Electronic Cigarette Consumption Among the Saudi Population in Jeddah City.

Introduction Electronic cigarettes(e-cigarettes) are rising in popularity among young adults and teenagers. Previous studies have shown that among high and middle schoolers, the percentage of e-cigarette smokers was noticeably higher than tobacco cigarette smokers. Various research papers focusing on different communities have reported a low-to-moderate level of knowledge and awareness of e-cigarette's effects on health. E-cigarettes were initially advertised as devices to help people quit smoking, but the use of e-cigarettes in modern days has changed considerably. A big chunk of the population perceivedthat e-cigarettes have no harmful effects because they are nicotine-free and thus are used as a replacement for regular cigarettes rather than as a way to quit smoking. Objectives The study aimed to assess the perception of e-cigarette consumption and associated factors among the Saudi population in Jeddah city. Methodology A cross-sectional study was conducted on the Saudi population in Jeddah, Saudi Arabia, during the year 2023. The study assessed the participant's perceptions of e-cigarette consumption and its risk factors. A pre-existing online questionnaire created by Google Forms was distributed among the population through social media applications to collect data after obtaining their informed consent. Results A total of 515 participants were included in this study. Relatives and friends were the most common sources (54%) for information about e-cigarettes. Of the sample, 17.5% thought that e-cigarettes were safer than conventional cigarettes, 13.4% used e-cigarettes on a regular basis, and 65% had not smoked electronically before. Vaping pushed only 17.2% to try traditional tobacco cigarettes, and 25% stopped using traditional tobacco products after starting to smoke e-cigarettes. Gender, age group, and total family salary were the associated factors with the use of e-cigarettes. In addition, an association between the perception of e-smoking and its use was noticed, as well as a significant association between gender and withdrawal symptoms. Conclusion A minority of the participants perceived that e-cigarette smoking is safer than conventional methods of smoking. The majority did not practice e-smoking at all. Furthermore, results showed that relatives and friends were the most common sources of information. The findings from the correlation testing underscore several noteworthy associations within the studied population. Notably, gender, age, total family salary, and occupation exhibited statistically significant correlations with e-cigarette usage.

Anomaly Detection in Smart Grid Application

The smart grid, a modernized electrical grid integrating advanced communication and information technologies, enables bidirectional flow of electricity and information, offering numerous benefits such as enhanced reliability, efficiency, and sustainability. However, the complexity and scale of the smart grid introduce new challenges, including the need for effective anomaly detection to ensure its secure and reliable operation. This research paper presents a comprehensive review of state-of-the-art anomaly detection techniques in smart grid applications. We explore various methodologies, including statistical methods, machine learning algorithms, and hybrid approaches, highlighting their strengths and limitations. Furthermore, we discuss the importance of accurate data preprocessing and feature selection to enhance anomaly detection performance. Through a comparative analysis, we demonstrate the effectiveness of different techniques in detecting anomalies in smart grid datasets. Our findings provide valuable insights for researchers and practitioners in developing robust anomaly detection systems for smart grid applications, ultimately contributing to the advancement of smart grid technology.