bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the Effects of Intrinsic Foot Muscle Exercises on Dynamic Balance after Sub-Acute Ankle Sprain
Authors: Sekkehchi Sepideh , Tajali Siamak Bashardoust , Ashnagar Zinat , Majdi Fatemeh
Published: November 2024

Introduction: This study compares the efficacy of routine treatment plus short foot exercises (SFE) with routine treatment alone on dynamic balance and pain in subjects with sub-acute ankle sprain.
Materials and Methods: A total of 32 patients (18-45 years old) with primary ankle sprain were randomly enrolled into the control group performing routine treatment alone or the SFE group performing routine treatment plus SFE. The dynamic balance was assessed using the star excursion balance test. Pain and ankle dorsiflexion range of motion were assessed using the numeric pain rating scale and a goniometer. The outcomes were evaluated at the baseline and after the end of treatment. The groups were compared using the one-way analysis of variance/analysis of covariance test. The effect sizes also were calculated to determine the efficacy of the SFE.
Results: Dynamic balance in the SFE group had a significant increase in the anterior, lateral, medial, and anteromedial directions compared to the control group (P=0.001, P=0.002, P=0.014, P=0.0001). No significant differences were observed between the groups in other directions of the star excursion balance test (P>0.05). Additionally, pain intensity was lower significantly in the SFE group than in the control group (P=0.0001). In the SFE group ankle dorsiflexion range was significantly more than the control group (P=0.025). The effect sizes also showed a high efficacy favoring SFE.
Conclusion: The combination of routine treatment and SFE can be an effective intervention for managing ankle sprain.
Investigating the Effectiveness of Music Therapy on Anxiety Caused by Stuttering in Children Aged 4 to 12 Years in Yazd City, Iran
Authors: Moqhimi Fateme , Saeidmanesh Mohsen , Yousefi Mohammad Reza
Published: November 2024

Introduction: This study evaluates the effectiveness of music therapy on anxiety caused by stuttering in children aged 4 to 12 years in Yazd City, Iran.
Materials and Methods: This is an experimental study with a pre-test/post-test design and a control group. The statistical population of the study consisted of 4-12-year-old children with stuttering referred to speech therapy centers in Yazd City, Iran, who were selected based on available sampling and divided into two equal groups, experimental and control, using a simple random method. The instrument of this research included the Spence children’s anxiety scale, parent version (SCAS-P). Children with anxiety in the experimental group underwent music therapy for ten 1-h sessions. To analyze the data, the SPSS software, version 24, was used and the significance level of the tests was considered 0.05. The statistical method of covariance analysis was used to analyze the research data.
Results: Based on the results of the analysis of covariance between the two control groups and the music therapy experiment, there was a significant reduction in the average level of anxiety (P<0.001, F=157.045) of children in the post-test stage.
Conclusion: Music therapy is effective in reducing the anxiety level of children with stuttering; therefore, it can be considered a complementary method to reduce children’s anxiety
Association of Flat Feet with Knee Moments and Western Ontario and McMaster Universities Arthritis Index in Knee Osteoarthritis
Authors: Sohrabi Maryam , Torkaman Giti , Bahrami Fariba
Published: November 2024

Introduction: Flat feet are prevalent among individuals with medial compartment knee osteoarthritis (KOA), showing a correlation with elevated knee pain and cartilage degeneration. This study investigates the relationship between calcaneal eversion angle (CEA) and medial longitudinal arch angle (MLAA) with knee kinetics and pain.
Materials and Methods: This analytical observational study included 30 volunteers with moderate KOA. The Vicon motion analysis system and two synchronized force plates were employed to capture level walking and the static standing position to measure CEA and MLAA. The study assessed the first and second peaks of the knee adduction moment, knee adduction moment impulse, peak knee flexion moment, and the peak knee flexion angle at heel strike (PKFA-HS). The Western Ontario and McMaster Universities arthritis index (WOMAC) pain and physical function were evaluated.
Results: A significant positive correlation was found between CEA and the knee pain sub-score (Pearson correlation [PC]=0.446, P=0.011) and WOMAC total score (PC=0.363, P=0.049). Additionally, a significant negative correlation was observed between CEA and peak knee flexion moment/PKFA-HS (PC=-0.418, P=0.022, and PC=-0.479, P=0.001, respectively). The results also indicated a negative significant correlation between MLAA and WOMAC pain sub-score (PC=-0.389, P=0.034).
Conclusion: Increased CEA and decreased MLAA are associated with elevated WOMAC pain sub-score and decreased PKFA-HS in individuals with moderate KOA. Addressing flat feet should be considered in KOA management to enhance pain relief and functional outcomes
Teamwork Skills, Satisfaction, and Knowledge Level in Rehabilitation Students Through Team Observed Structured Clinical Examination
Authors: Amiri Marzieh , Orakifar Neda , Taheri Fatemeh , Khajeali Nasrin , Pirayeh Nahid , Tavakoli Mojtaba , Bidokhti Masoume Hosseini , Saadat Maryam , Behdarvandan Amin , Soltani Majid , Bayat Arash , Heidari Parisa
Published: November 2024

Introduction: Team-observed structured clinical examination (TOSCE) is a powerful approach to formative assessment resources, regarded as an essential element of students’ performance in a team format. Considering the importance of teamwork in rehabilitation, this study implements an intervention focused on enhancing teamwork skills within diverse groups of rehabilitation students, employing TOSCE as a tool for assessment and improvement.
Materials and Methods: A total of 49 fourth-year undergraduate students specializing in audiology, physical therapy, and speech therapy constituted three groups in our study. Students in each major were randomly divided into four groups. The students were asked to complete the teamwork (19-item scale of Lencioni) and satisfaction (researcher-made scale) questionnaires after TOSCE. A knowledge assessment questionnaire was also conducted at pre- and post- TOSCE time points.
Results: There was no significant difference between the average score of the teamwork (F=1.508, P>0.05) and satisfaction (F=3.508, P>0.05) levels across different majors. All participants had high scores on both teamwork and satisfaction scales. By comparing the pre-test and post-test knowledge assessment, in all groups, there was a statistically significant difference between the pre-and post-test results (P<0.05). The students obtained a higher score after the implementation of the TOSCE.
Conclusion: The TOSCE method provides a valuable and viable educational opportunity for rehabilitation students to receive feedback on their clinical performance, enhance their knowledge, and, most importantly, lead to a positive feeling of teamwork among students.
Investigating the Effects of Nintendo Wii on Ankle Spasticity in Patients with Stroke: A Randomized Clinical Trial
Authors: Farahmand Saeideh , Ghasemi Majid , Basiri Keivan , Ghasemi Ehsan
Published: November 2024

Introduction: Stroke is one of the most common causes of disability. Spasticity is a common clinical impairment that occurs after a stroke. This study investigates the effect of Nintendo Wii games on the outcomes of spasticity, functional mobility, and quality of life in patients with stroke.
Materials and Methods: In this single-blind clinical trial study, 30 patients with stroke were randomly assigned to one of the experimental and control groups. Subjects of both groups received conventional physiotherapy. In addition, the experimental group also received Nintendo Wii games for 30 min, three times a week for a total of 12 sessions. The primary outcomes spasticity and functional mobility that respectively evaluated using the modified modified Ashworth scale (MMAS) and the timed up-and-go test before and after the treatment. The assessor was blind about group assignment. Meanwhile, the secondary outcome included quality of life. All statistical analyses were performed using the SPSS software, version 20.
Results: The intragroup results showed that in the experimental group, spasticity was significantly reduced (P=0.001), functional mobility improved (P=0.001), and the quality of life increased significantly (P<0.001); however, in the control group, only a significant improvement in functional mobility (P=0.04) was observed. The comparison between the two groups showed that there is no statistically significant difference in the results between the experimental and control groups (P>0.05).
Conclusion: Considering these results, the use of Nintendo Wii can be suggested as a treatment modality alongside the usual treatments to achieve more and faster effectiveness in patients with stroke.
Investigating the Effects of Transcutaneous Electrical Nerve Stimulation on Lumbar Fascia Tissue and Lumbar Curvature in Healthy People
Authors: Tamartash Hassan , Dadarkhah Afsaneh , Najafi Sharif , Shouraki Jalal Kargar , Tabatabaee Seyed Morteza , Azizi Sirous
Published: November 2024

Introduction: Transcutaneous electrical nerve stimulation (TENS) is commonly used for pain management. Recent studies have shown that TENS can improve the condition of low back pain by influencing the elastic coefficient of the lumbar fascia and the balance status; however, the effect of TENS on the lumbar fascia of healthy people has not been investigated. Accordingly, this study examines the effect of TENS on the lumbar fascia of healthy people.
Materials and Methods: A total of 60 healthy participants in two groups, underwent 10 sessions of conventional TENS intervention. The first group (intervention group) received TENS with an intensity at the tolerance level, and the second group (control group) received sham TENS. Ultrasonography and a spinal mouse device were assessed lumbar fascia thickness and lumbar curvature before and after the TENS.
Results: Significant changes were observed after TENS in the intervention group in reducing the lumbar fascia thickness (P=0.006) and increasing the lumbar curvature (P=0.000). Between- group changes after the intervention sessions indicated a significant difference between the lumbar fascia thickness and curvature (P≤0.003).
Conclusion: TENS in healthy people can lead to a decrease in the thickness of the lumbar fascia and improve lumbar curvature. Also, a strong correlation was found between lumbar fascia thickness reduction and increased lumbar curvature.
Optimizing Volleyball Bottom Passing Skills: Unveiling the Impact of the STAD Learning Model on Learning Outcomes
Authors: Nurodin Dede , . Samsudin , Asmawi Moch , Nurfitri Wendah , Lubis Johansyah , . Junaidi , Humaid Hidayat
Published: November 2024

The study focused on improving the lower passing skills of 8th-grade female students at SMP Negeri 6 Sumedang in volleyball. Recognizing the significance of passing in enhancing team attacks and overall match performance, the researcher employed the student teams achievement division (STAD) learning model to address existing skill deficiencies. The research identified common issues in lower passing execution, including imperfect movement stages, improper ball contact in the hands, ball deviation from the intended target, and an inability to control the ball’s direction. The results indicated a positive impact, with a significant increase in the lower passing skills observed during the volleyball underpassing tests. The t-test results, with T_count = 16.4 > T_table = 1.725, demonstrated a statistically significant improvement, confirming that the STAD learning model contributed to enhanced learning outcomes. In summary, the research concluded that the application of the STAD learning model positively influenced the learning outcomes related to lower passing skills in volleyball among 8th-grade female students at SMP Negeri 6 Sumedang. The findings emphasized the efficacy of employing cooperative learning strategies, such as STAD, to address specific skill challenges in sports education. Keywords: STAD, bottom passing, volleyball
The Relatioship between Leg Muscle Strength Arm Muscles and Self-Confidence with 50 Metres Swimming Speed Free Style
Authors: Matitaputty Johanna
Published: November 2024

The purpose of this study was to determine the relationship between leg muscle strength and self-confidence, arm muscle strength and self-confidence, and leg muscle strength, arm muscle strength and self-confidence with 50-meter freestyle swimming speed. The sample of this study was 30 male students. In accordance with the formulation of the problem and the objectives to be achieved, the research method used in this study is a quantitative method with correlation and descriptive techniques, namely the researcher will see the relationship between one variable and another. Based on the results of the analysis of research data and discussion, it can be concluded that the following research results (a) there is a relationship between leg muscle strength and 50-meter freestyle swimming speed of 3.61 (b) there is a relationship between arm muscle strength and 50-meter freestyle swimming speed of 3.47. (c) there is a relationship between self-confidence and 50-meter freestyle swimming speed of 4.95 which then the relationship between leg muscle strength and arm muscle strength and self-confidence with 50-meter freestyle swimming speed of 14.99. Thus, the stronger the leg and arm muscles, as well as good self-confidence, the better the 50-meter freestyle swimming speed of male students of the Health and Sports Study Program, FKIP Unpatti Ambon.
Keywords: Swimming 50 meters freestyle, leg muscle strength, arm muscle, self-confidence
Project -Based Steam Learning Based on Sound of Green: Empowering Students Creative Thinking
Authors: Papilaya Pamella Mercy , Salhuteru Chtherine P.
Published: November 2024

This research aims to investigate the ability of teachers in designing sound of green-based PjBL STEAM learning to empower creative thinking skills and to determine the differences in creative thinking abilities in junior high school students. This study emphasizes the assessment of the effectiveness of project-based STEAM learning based on sound of green in empowering students' creative thinking in natural resource processing, involving 162 students in schools in music tourism attraction villages in Ambon city. The study reports that the respondents' results and creativity levels, including fluency, flexibility, originality, and elaboration among students, were excellent. Overall research results indicate that the majority of students are at a good level after being taught with the sound of green-based PjBL STEAM model. The increase in students' creativity with guidance and direction from teachers, especially in the technical, art, and mathematics stages in STEAM, is evident. Initially, many students lacked fluency, flexibility, originality, and elaboration dimensions in detail, resulting in an inability to generate more ideas and provide answers. Consequently, their originality decreased, and they lacked ideas to further elaborate on the given answers. However, findings from several meetings show a significant improvement among students. This is because implementing project-oriented learning models that empower the environment around students enhances every domain of creativity in science subjects. Additionally, the researcher suggests conducting further research on students' creative thinking processes based on the unique advantages of each school's geographical location. Teachers are expected to better understand students' cognitive, affective, and psychomotor abilities, improving students' creative thinking skills in every dimension of creativity according to the geographical conditions of the learners' environment. Keywords: sound of green STEAM, project learning, creative thinking
Sustainable Manufacturing Engineering: Enhancing Product Quality through Green Process Innovations
Authors: Ghelani Harshitkumar
Published: November 2024

The imperative for sustainable practices within manufacturing has become increasingly critical, driven by environmental concerns, regulatory pressures, and the evolving expectations of eco-conscious consumers. This paper investigates the role of sustainable manufacturing engineering in enhancing product quality through the implementation of green process innovations. Specifically, the study explores how sustainable manufacturing methods, including energy-efficient systems, additive manufacturing, zero-waste production, and renewable energy integration, can drive quality improvements while reducing the environmental impact of production processes.
The research is grounded in a comprehensive analysis of current green innovations in the manufacturing sector, drawing on case studies of companies that have successfully balanced quality and sustainability. The findings indicate that companies adopting sustainable practices not only reduce waste and lower emissions but also experience notable gains in product durability, customer satisfaction, and brand reputation. For instance, additive manufacturing is shown to improve precision and material efficiency, while zero-waste initiatives lead to a more efficient resource cycle, which in turn supports consistent quality standards.
This paper further delves into the challenges of implementing green manufacturing processes without compromising product quality, examining factors such as initial costs, technological readiness, and market adaptation. Visual data in the form of tables and graphs are presented to compare different green process innovations and their respective impacts on quality and sustainability metrics. In addition, case study summaries provide real-world examples of how leading companies have enhanced product quality through sustainable engineering approaches.
This study underscores the value of sustainable manufacturing engineering as a dual-benefit strategy that can drive environmental stewardship alongside quality enhancement. The paper recommends that future research focus on the development of new, cost-effective green technologies and the application of advanced data analytics for improved process control in sustainable manufacturing. Through these efforts, the manufacturing sector can continue advancing toward a more sustainable, high-quality production paradigm.
Analysis of Technology for Mitigation of The Effects of Climate Change on Buildings in Kaloum (Guinea-Conakry)
Authors: Dr Alseny CONTE  , Pr M.L DIALLO  , Dr Ibrahima KEITA
Published: November 2024

Information and communications technologies (ICTs) can, in a number of ways, contribute to meeting the requirements of the three main pillars of the Bali Action Plan, which was developed at the thirteenth Conference of the Parties in December 2007. These pillars concern enhanced action on adaptation, concerted action to reduce greenhouse gas (GHG) emissions and climate change mitigation measures. ICTs can support these actions and address the climate change issues that all countries (and particularly developing countries) face. ICTs can also be used to mitigate the influence of other sectors on GHG emissions and to help countries adapt to climate change.
UART IP core Design and Verification using WISHBONE Interface
Authors: CHANDUPATLA TEJASWI , Sotoudeh Hamedi-Hagh
Published: November 2024

The organization of communication between the devices is greatly aided by the communication protocol. These protocols have a clear set of guidelines that have been agreed upon by the tools needed for effective communication. A popular protocol for serial communication is UART. The hardware description language for the SV/Verilog UART functional module is designed in this paper. The serial connection capabilities offered by this UART IP CORE enable interaction with modems and other external devices. The Universal Verification Technique is used to undertake functional verification of the UART. By comparing the collected response to the intended response via the scoreboard mechanism, the reusable UVM test bench architecture is meant to push the randomized stimuli to the unit under test to assess the functional correctness. This core is made to be as compatible as possible with commercially accepted designs. The WISHBONE INTERFACE WITH 8-BIT OR 32-BIT SELECTABLE DATA BUS MODES is one of this design's primary characteristics. Interface debugging in 32-bit data bus mode. Functionality and register-level compatibility. FIFO procedure. The UVM methodology is used to validate the design. For optimal functional coverage, the test bench is written with regression test cases. The overall execution time will be shortened if the stimulus may be reused.
Advantages of general parametric solution Approach in dynamics courses
Authors: Pakdemirli Mehmet
Published: November 2024

Problems of dynamics are treated using a general parametric solution approach. The advantages of treating problems in the parametric way is discussed.  The approach has many advantages over the numerical calculations starting from the beginning: 1) Solutions are expressed in the general form suitable for algorithmic programming 2) Unit check on the correctness of the results are easier 3) Effect of each input parameter on the desired output parameter can be easily observed 4) Results can be evaluated for the limiting cases to recognize the soundness of the solutions. 5) Mathematical principles can be more exploited on the solutions 6) Solutions give more insight on the design principles. The ideas are outlined using three sample problems. The advantages of the approach have been tested on Mechanical Engineering Sophomore students for over 16 years with success.
Predicting Counterfeits from Smartphone Multi-Images Using Deep Learning
Authors: Wood Dominic , Umer Sufan  , Cesar Villamil  , Dr Richard Wood
Published: November 2024

Counterfeiting is a pervasive issue in the collectible card market, posing significant risks to collectors, investors, and the industry at large. The trade of counterfeit cards not only undermines the value of genuine collectibles but also deceives consumers who invest their resources into fraudulent items. Despite advancements in counterfeit detection technologies, many counterfeit cards remain undiagnosed and continue to circulate in the marketplace, primarily due to the reliance on traditional methods of verification, which are often time-consuming and require expert evaluation.
This study aims to leverage the increasing capabilities of widely available consumer technologies, specifically smartphones, to develop an innovative approach for the early detection of counterfeit collectible cards. By utilizing the multi-image signals acquired from smartphone cameras, we hypothesize that significant differences in color, type font, and material reflectiveness associated with counterfeit cards can be identified through machine learning techniques, particularly convolutional neural networks (CNNs).
To evaluate this hypothesis, we analyzed a comprehensive dataset of 22,298 individual trading cards collected through the Digital Grading Company smartphone app. The dataset consisted of user-submitted images, which were systematically categorized into training, development, and test datasets to facilitate model training and validation. A robust 34-layer CNN architecture was employed to analyze these multi-image signals and predict the prevalence of counterfeit cards. The model's performance was measured using the area under the receiver operating characteristic curve (AUC), providing a quantitative assessment of its discriminatory ability.
Our results revealed that of the total cards analyzed, 6.0% were identified as counterfeit, with the CNN model achieving an AUC of 0.772 (95% CI 0.747 - 0.797) in the test dataset. This indicates a reasonable level of discrimination in detecting counterfeit cards based solely on the multi-image data. The findings suggest that deep learning technologies can significantly enhance counterfeit detection processes, providing collectors and industry stakeholders with a powerful tool to combat fraud.
This study represents the first proof-of-concept demonstration of utilizing smartphone-based imaging for the detection of counterfeits in collectible trading cards. By validating the effectiveness of deep learning in this context, we pave the way for future research that can explore more sophisticated algorithms and techniques to further improve detection accuracy. Ultimately, our approach could lead to the development of accessible applications for collectors and consumers, empowering them to make informed decisions and protect their investments in the collectible market.
An efficient Automatic COVID-19 Detection from Medical Images using hybrid CNN-RNN and GAN network
Authors: Kadhim Noor
Published: November 2024

Coronavirus, commonly known as COVID-19, is a viral illness caused by the SARS-CoV-2 virus, which stands for severe acute respiratory syndrome coronavirus 2. The global spread of COVID-19 has had a deleterious effect on both the public health and economy. One major step in the battle versus COVID-19 is detecting the virus in patients through positive chest X-rays. Early studies have identified abnormalities in the chest X-rays of infected patients that are indicative of the disease. Research has shown high accuracy in identifying COVID-19 patients using chest X-rays, which has spurred the development of various deep learning algorithms. Convolutional neural networks (CNNs), a type of deep learning model, require large amounts of training data. However, due to the recent emergence of the pandemic, gathering a substantial dataset of radiographic images in a short time has been difficult. To address this, our study introduces a model called CovidGAN, which employs a Generative Adversarial Network (GAN) to generate synthetic chest X-ray (CXR) images. Additionally, we propose a hybrid CNN-RNN network for identifying COVID-19 in X-ray images, achieving a classification accuracy of 98.75%.
Enhancing Cybersecurity Awareness Training for Mitigating Human-Induced Cybersecurity Breaches
Authors: Al-Kuwari Reema
Published: November 2024

In a world where 95% of cybersecurity breaches are due to human error, and a rapidly evolving cybersecurity landscape, the effectiveness of employee’s proper security awareness stands as a pivotal concern [1]. Especially when the employees are the first line of defense for any organization. This highlights the importance of having robust cybersecurity awareness that is dynamic to face the ever-changing threats in the world. However, traditional employee security awareness training programs often fall short, perceived as dull, ineffective, and unable to keep pace with the dynamic cybersecurity landscape [2]. These shortcomings not only diminish engagement but also lead to decreased knowledge retention, rendering employees more vulnerable to cyber threats.
Behavior of Slab-on-Grade Resting on Expansive Soil
Authors: Layas Fathi M , Karakale Vail , Suleiman Ramadan E , Hachem Fatma
Published: November 2024

Abstract
This paper discusses a case study of a residential building in Benghazi that features slab-on-grade foundations supported by expansive soils. Although such structures are uncommon in the area, they can suffer serious damage due to volumetric changes in the soil caused by wetting and drying. The building has a total plan area of 200 m², with tie beams located 1.2 m above the formation level, and expansive soil used as backfill. After being moistened and lightly compacted, 100 mm thick concrete slabs were poured between the tie beams, resulting in varying slab sizes.
Following a month of heavy rainfall, significant cracking—longitudinal, diagonal, and warping—occurred in the slabs due to uneven deformation from the expansive soil. The paper includes photographs of these deformations and cracks, along with an analysis of the geotechnical properties of the expansive soil, including its swelling and shrinking characteristics. It also employs an approach to predict the pressure exerted on the slabs due to differential heave.
A Personalized Multimodal Approach to Music Recommendation for Music Therapy
Authors: Mandal Shreyam , Shrivastava Aryan , Nagarajan Sidharth , Gulabani Vinay , Chauhan Ayush , Verma Kritika
Published: November 2024

Mental health disorders and suicidality are rising among adolescents and young adults (A-YA) while rates of treatment engagement continue to remain low. Though the effectiveness of music therapy to enhance mental health is yet unknown for A-YA, the power of music to evoke emotions, facilitate communication, and promote relaxation suggests its promise. In this research study we discuss a multimodal music recommendation system which is capable of taking inputs in the form of a BFI-10 questionnaire, a series of self-view questions in the form of a survey, and an image of the user’s face in order to determine music preferences based on existing correlations between personality traits and taste in music, as an effort to make advancements in the field of music therapy.
DEVELOPING A WEB BROWSER EXTENSION TO PREVENT THE SPREAD OF FAKE NEWS
Authors: Taşcı Merve Esra , BAYRAKDAR YILMAZ Yonca
Published: November 2024

Fake news causes huge social problems such as misdirecting and provoking the masses, creating an atmosphere of chaos by spreading fear. Detecting and stopping the dissemination of fake news has been an important and priority issue due to its rapid spread, difficulty to detect, and negative effects. In our study, a new Chrome extension that detects fake news has been developed in order to detect and prevent the spread of fake news. In the study; natural language processing, data mining methods, and various machine learning techniques for instance Passive Aggressive, Random Forest, Support Vector Machine, AdaBoost, XGBoost, and Long-Short-Term Memory (LSTM) algorithms are used. The accuracy rates of the algorithms, confusion matrices, AUC rates, and ROC curves were compared. The created machine learning model has been implemented in the online internet environment by using Flask and Rest API in the Python program. Finally, the Chrome extension interface was built using Javascript, HTML, and CSS. The LSTM algorithm gave the highest prediction result with a rate of 90.72% compared to other algorithms.
IoT and Edge Computing for Smart Manufacturing: Architecture and Future Trends
Authors: Subramanian Sunthar
Published: November 2024

The integration of the Internet of Things (IoT) and Edge Computing is revolutionizing the manufacturing industry, ushering in the era of smart manufacturing as part of Industry 4.0. This paper explores the synergy between IoT and Edge Computing, focusing on their combined architecture and the future trends driving innovation in smart factories. IoT enables the connection and communication of machines, sensors, and systems, allowing for real-time data collection and monitoring. However, traditional cloud-based approaches face challenges such as latency, bandwidth limitations, and security risks, which can hinder real-time decision-making in fast-paced manufacturing environments. Edge Computing addresses these issues by processing data closer to the source, minimizing latency and reducing dependence on cloud infrastructures. By combining IoT and edge solutions, smart manufacturing systems can make faster, data-driven decisions, leading to improved efficiency, reliability, and operational flexibility. This paper delves into the architectural design of IoT and edge computing in manufacturing, outlining how data flows from IoT devices to edge nodes and cloud services. Several real-world use cases and industry examples are analyzed to highlight the practical benefits of these technologies.
Additionally, this research identifies key challenges such as security vulnerabilities, the need for robust network infrastructures (e.g., 5G), and issues related to data standardization. The future of smart manufacturing is also explored, emphasizing trends like the adoption of artificial intelligence (AI) and machine learning (ML) at the edge, digital twins for real-time monitoring, and the role of IoT and edge computing in fostering sustainability through energy-efficient production processes.This study provides a comprehensive overview of IoT and edge computing architectures in smart manufacturing and offers insights into future technological trends that will shape the industry.
Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing
Authors: Ghelani Harshitkumar
Published: November 2024

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste.
Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.
Sentiment analysis in infodemic management: leveraging the eppm Risk communication model
Authors: James Orevba Aigboje  , Dr Prashant Priyadarshi  , Prof. Yahaya Mohammed  , Mahmud Muhammad Yahaya  , Oliver Lorkase  , Olayinka Badmus
Published: November 2024

Infodemics are the rapid spread of false or misleading information related to public health emergencies, often through digital platforms. They can cause confusion, fear, and even harm to public health. This study investigates the application of sentiment analysis for infodemic management during disease outbreaks. Leveraging the Extended Parallel Process Model (EPPM) of risk communication, the research aims to categorize rumors based on their perceived threat level (high, medium, or low). Machine learning is employed to analyze infodemic text data collected from two Nigerian states (Oyo and Bauchi) to assess threat appraisal according to the EPPM model. The findings can inform targeted interventions for effective infodemic management before, during, and after outbreak of diseases.
Roadmap for Enterprise Information Management: Strategies and Approaches in 2019
Authors: Syed Shakir
Published: November 2024

Purpose: This ongoing research explores the strategy and frameworks employed by key organizations and provides a current roadmap for enterprise information management technologies in today's digital workplace. Theoretical basis: Content and context are examined against published research, new case studies, and actual implementations. Methodology: Research includes ongoing personal correspondence, analysis of existing documents, case studies, and application of these technologies and frameworks during advanced graduate and professional seminars. This research project will combine the results of ongoing research on information management strategies and approaches observed throughout 2019. It suggests that these observations will become trends for the upcoming year. Results: Key stakeholders address the strategic elements of enterprise information management to demonstrate the contemporary importance and reliance for today's organization. Strategies should encompass and harmonize people, processes, data, and technology to address and resolve the global demands and cultural challenges of anyone involved in data capture, creation, information sharing, or analysis. Implications: Technical approaches rigorously aligned with the success of the business will include the people involved. They need to be rewarded for accurate and appropriate data entry, not punished. Analysis of the results is necessary to tweak the processes. Accordingly, adjusting the organizational structures addressing the business benefits of their data will drive long-term success.
Six Sigma and Continuous Improvement Strategies: A Comparative Analysis in Global Manufacturing Industries
Authors: Ghelani Harshitkumar
Published: November 2024

In today's competitive global manufacturing landscape, quality improvement and operational efficiency are critical for companies aiming to sustain growth and remain agile. This paper provides a comparative analysis of Six Sigma and continuous improvement strategies, focusing on their methodologies, applications, and effectiveness within manufacturing industries worldwide. Six Sigma, developed by Motorola and widely adopted by firms like General Electric, emphasizes defect reduction through data-driven processes and statistical analysis. In contrast, continuous improvement encompasses various incremental improvement frameworks such as Kaizen, Lean Manufacturing, and Total Quality Management (TQM), which target waste reduction, process efficiency, and holistic quality enhancement.
This study examines both approaches in terms of implementation costs, organizational impact, and measurable outcomes, using real-world case studies from the automotive, electronics, and consumer goods sectors. The analysis reveals how Six Sigma's structured, project-based approach makes it suitable for complex, high-stakes production environments, while continuous improvement strategies promote a culture of daily efficiency and adaptability. Data insights illustrate the adoption trends and performance metrics associated with each method, highlighting their strengths, limitations, and areas where they intersect. Through a series of comparative tables and graphs, the paper quantifies key performance improvements—such as defect reduction rates, cost savings, and time efficiency—achieved by companies employing these strategies.
Ultimately, this paper provides practical insights for manufacturing leaders and quality managers, offering a guide to selecting, implementing, and potentially integrating these methodologies to optimize operational performance. The study also discusses emerging trends, including the integration of digital technologies like AI and automation, which are reshaping quality and process improvement in manufacturing.
Advances in Lean Manufacturing: Improving Quality and Efficiency in Modern Production Systems
Authors: Ghelani Harshitkumar
Published: November 2024

This research paper explores recent advancements in lean manufacturing, focusing on how these developments improve quality and efficiency within modern production systems. Lean manufacturing, originally developed by Toyota to minimize waste and optimize processes, has evolved significantly with technological integration and the demands of contemporary industrial environments. The paper begins with an overview of foundational lean principles, such as waste reduction, continuous improvement, and value maximization, followed by a discussion of widely used lean practices like 5S, Kaizen, Just-in-Time (JIT), and Kanban.
Key advancements in lean manufacturing are examined, including the impact of Industry 4.0 technologies—automation, Internet of Things (IoT), and data analytics—which allow real-time data monitoring and streamlined decision-making. The paper also investigates the integration of Lean Six Sigma and hybrid methodologies that combine lean principles with statistical tools to further improve quality control and efficiency.
To illustrate the practical benefits of these advancements, several real-world case studies are presented, highlighting companies that have successfully implemented modern lean practices. Quantitative data from these case studies demonstrate improvements in production speed, waste reduction, and quality enhancement. Furthermore, the paper analyzes the impact of lean practices on key performance indicators, such as production lead time, defect rates, and overall cost savings.
Challenges in adopting advanced lean manufacturing methods, such as initial implementation costs, resistance to change, and skill gaps, are also discussed. Finally, the paper provides insights into future trends, including the potential of artificial intelligence, predictive analytics, and sustainable manufacturing within lean frameworks.
This study concludes that the integration of lean manufacturing with emerging technologies continues to transform production processes, positioning lean as a crucial methodology for companies aiming to stay competitive in a rapidly evolving industrial landscape. Tables and graphs throughout the paper support these findings by showcasing data-driven results and visual comparisons of performance metrics before and after lean implementation.
Improvements in the manufacture of benzoic acid obtained by catalytic oxidation of toluene
Authors: Poço João Guilherme Rocha , Cella Rodrigo , Derenzo Silas
Published: November 2024

This study investigates improvements in benzoic acid production through the catalytic oxidation of toluene with cobalt octoate. The oxidation process, which occurs under controlled conditions with temperatures ranging from 130 °C to 165 °C, typically results in a conversion rate of 50% toluene with a selectivity of 80% over benzoic acid. However, various by-products such as benzyl alcohol, benzaldehyde, and benzyl benzoate are also formed, which reduces the overall yield. The work identifies methods to minimize these byproducts, particularly benzyl benzoate, which forms in the distillation column. One strategy investigated involved using additives to inhibit the esterification reaction that produces benzyl benzoate, aiming to improve the selectivity of the oxidation process. Experimental results show that maintaining reaction temperatures between 135 °C and 145 °C in combination with using sodium benzoate as an additive effectively reduces the formation of benzyl benzoate and increases the yield of benzoic acid. Furthermore, increasing the O2/toluene ratio increases the oxidation efficiency.
Evaluating Environmental Risks and Waste Management in Chemistry Laboratories: Integrating FMEA and Cleaner Production
Authors: Almeida Elinilcia Ribeiro de , Vieira Adalena Kennedy , Vieira Raimundo Kennedy
Published: November 2024

The issue of environmental waste production has increased substantially, and educational institutions have become initiating agents because of their research and practical activities. This research work has the following objectives: To assess and analyze the environmental aspects and environmental impacts created by the chemistry laboratories at the Institute of Health and Biotechnology (IHB) in Coari, Amazonas, using management methodologies. The FMEA and Cleaner Production (CP) approaches were applied for risk assessment and decision-making to identify risks and propose preventive measures. It becomes clear that the Environmental Risk Index (ERI) guides activities toward managing or reducing risk. In the 67 cases of the process, 32 of them were assessed as priorities, two of which are high risk and require action. The results include the following objectives for a Waste Management System (WMS) suited to the environment in the institution: compliance with current environmental laws or regulations and the creation of a safer environment for students to work and learn.
Ética e integridade na pesquisa em educação na perspectiva da complexidade
Authors: Da Silva Sidinei Pithan , Cossetin Vânia Lisa Fischer , González Fernando Jaime
Published: November 2024

O presente texto reflete acerca do tema da ética e da integridade na pesquisa em educação na perspectiva da complexidade a partir da relação entre educação, ciência e ética. Metodologicamente, este escrito orienta-se por uma perspectiva crítico-hermenêutica e baseia-se, teoricamente, na filosofia de Edgar Morin e de seus intérpretes. A referida opção teórica permite situar a problemática no bojo do pensamento complexo pela consideração da amplitude e da dinâmica do fenômeno educativo, sobretudo de uma ética que implique a relação entre o mundo, a pesquisa, a humanidade e o conhecimento. Assume relevância, no âmbito da perspectiva complexa, a própria problemática do conhecimento, de seu alcance e validade, ou seja, do paradigma que comanda e organiza a pesquisa. O enfoque paradigmático moderno, ao excluir o sujeito do objeto, em sua ambiciosa busca pela certeza, isolou e separou a subjetividade do investigador do objeto investigado, e com isso, aboliu a responsabilidade moral do pesquisador. Toda a forma de pesquisa em educação, para se haver com o tema da ética e da integridade, precisa lutar contra as cegueiras, a fragmentação e a simplificação do conhecimento, que colaboram para a tecnificação e objetificação do mundo. O problema cognitivo, que é tarefa da educação, tem um significado antropológico, político, social e histórico, como também, ético. O ato de conhecer se entrelaça com o ato moral, no sentido de que implica uma responsabilidade, a qual emerge no seio de um ato individual de religação com um outro, com uma sociedade, em escala planetária.
Catalytic Performance of Newly Synthesized Heterocyclic Hydrazone Derivatives For Production of High Yield Neem Biodiesel
Authors: Jabeen Mussarat , Razzaq Irum , Noureen Sajida , Hussain Syed Ubaid , Aslam Noreen , Zafar Ansa , Mehmood K , Khan Misbah Ul Ain
Published: November 2024

Biodiesel, a sustainable and environmentally friendly substitute for diesel, has attracted growing attention in recent years. The reuse of non-edible neem oil as a feedstock for biodiesel production is affordable and naturally safe. This study aimed to understand the understudied benefits of using heterocyclic organic hydrazone derivatives as catalysts for high yield biodiesel production. The catalysts were characterized using techniques such as EIMS, NMR, CHN and FTIR analysis, which revealed the morphological and functional characteristics of the catalyst. The optimum process conditions were found to be catalyst concentration of 50 mg/10 mL, methanol-to-oil molar ratio of 3:1, reaction temperature of 60 °C, and reaction duration of 60 min; these conditions yielded 95% biodiesel. The produced biodiesel was analyzed using FTIR, and different parameters like moisture content, saponification value, density, acid value, iodine value, and FFA value. The use of neem oil and organic based catalysts for biodiesel production is an economical and environmentally sustainable process.