Industry Professionals Research Articles

Application of Internet of Things and Artificial Intelligence for Risk Mitigation of Accidents in Construction Sites: A Case Study of Brazilian Construction Companies

The construction industry constantly faces challenges related to workplace safety. Managing work safety is crucial to ensuring worker protection and successful project completion. In this context, this study investigates the applicability of Internet of Things technologies and Artificial Intelligence models, such as YOLO, to enhance safety on construction sites. The research identifies significant cultural resistance to the adoption of disruptive technologies in the Brazilian construction sector, and interviews with industry professionals reveal a low level of awareness and interest in these technological innovations. Through case studies and quantitative analyses, the study demonstrates YOLO's potential in identifying risks, such as detecting PPE and structural hazards. The results highlight the need to overcome conservative barriers and invest in technological solutions to increase safety and efficiency in construction.

Visionary constructs: revolutionizing students internships through BIM and extended reality synergy

ABSTRACT This study investigates the integration of Building Information Modeling (BIM) and Extended Reality (XR) technologies in construction education, focusing on their application in internship programs. Traditional internships often face limitations such as restricted exposure to complex scenarios and geographical constraints. To address these challenges, this research examines how BIM-XR technologies can bridge the gap between theoretical knowledge and practical application in the Architecture, Engineering, and Construction (AEC) disciplines. A mixed-methods approach was employed, combining qualitative data from interviews with students, educators, and industry professionals alongside quantitative survey data. This comprehensive methodology enabled an in-depth assessment of BIM-XR's impact on student learning outcomes and operational efficiency. The findings revealed that BIM-XR integration significantly enhances educational experiences by providing immersive simulations that help students visualize complex construction processes. This leads to better preparedness for real-world challenges, increased engagement, and more effective internships. Additionally, BIM-XR offers practical benefits for industry practices, such as improved project planning, real-time decision-making, and enhanced stakeholder collaboration. However, the study also highlights challenges, including the steep learning curve, limited access to necessary technologies, and high implementation costs. These limitations point to the need for comprehensive training programs, affordable technology options, and institutional support to ensure broader adoption. Looking ahead, the study suggests that further integration of BIM-XR with emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) could further enhance educational outcomes and industry practices.

Barriers to implement energy efficiency strategies to heating, ventilation, air conditioning of airside system in commercial buildings in Sri Lanka

At present, debates on climate change, fossil fuel depletion, and energy saving highlight the need for a sustainable built environment to reduce energy consumption and emission trends. The Heating, Ventilation, and Air Conditioning (HVAC) system is a priority due to its significant share of a building’s electricity consumption. While energy efficiency strategies for central air conditioning systems focus on both waterside and airside components, implementing airside efficiency strategies is notably lacking in Sri Lanka. This is a concern because airside inefficiencies lead to excessive energy use, higher operational costs, and compromised indoor air quality. Thus, the study aims to address this gap by identifying the barriers to implementing energy efficiency strategies for HVAC airside systems in Sri Lankan commercial buildings. To explore these barriers, a qualitative research approach was employed, involving interviews with 17 industry experts. The study identified 27 barriers, with key barriers being a lack of technical expertise, rapid technological advancements, misalignment with organisational objectives, limited financial resources, inadequate training and technical knowledge, and insufficient regulatory support. The insights gained from this study can assist industry professionals in enhancing HVAC energy performance and encourage further academic research on this sub-branch of HVAC systems in the Sri Lankan context.

Research on Proprietary Intellectual Property Management and Marketing Model of Film and Television Companies: A Case Study Based on Warner Bros

This research explores the proprietary intellectual property (IP) management and marketing model in the film and television industry, with a particular focus on Warner Bros. The study aims to elucidate the complex strategies employed by Warner Bros. in the management, monetization, and marketing of its exclusive IPs, demonstrating the company's innovative approaches in this competitive landscape. The research is structured sequentially and examines Warner Bros.' IP management model, revenue streams, and marketing strategies. By closely examining these key elements, the study aims to identify the fundamental factors that contribute to Warner Bros.' success in intellectual property management, revenue generation, and marketing prowess. The insights derived from this comprehensive analysis are intended to provide valuable lessons and insights for industry professionals and researchers within the broader entertainment sector, offering a deeper understanding of the strategic intricacies and successful practices employed by Warner Bros. in navigating the complexities of the entertainment industry's IP-driven landscape.

Organizational Factors Influencing Data Analytics Adoption in SMEs

Small and medium-sized enterprises (SMEs) can use analytics tools to use data for strategic decision-making, but their adoption remains limited because of several issues. This study conducts a systematic literature review spanning the years 2018 to 2024 to investigate how the adoption of data analytics factors influences the widespread utilization of Technology-Organization-Environment (TOE) and Diffusion of Innovation (DOI) frameworks within SMEs. After a rigorous selection procedure, 25 relevant publications were found on Web of Science and Google Scholar, providing insight into significant factors impacting adoption initiatives. The study highlights the significance of top management support, organizational readiness, rivalry, and relative advantage in fostering successful adoption campaigns within SMEs. By arranging and categorizing these variables, this study offers important perspectives to scholars and industry professionals, empowering them to formulate informed choices and tactical plans for the implementation of data analytics in SMEs.

Pathway of Online Impulsive Buying Tendency Research (2007-2024): Bibliometric Analysis

The goals of this study were to assess the body of knowledge about the propensity for online impulsive purchasing, to rectify the shortcomings of past reviews, and to provide academics and industry professionals with an up-to-date, thorough overview of the state of research in the field of online impulse buying tendency. We employed bibliometric analysis approaches to analyze the body of literature on online impulsive purchasing propensity. It was useful for summarizing data through significant contributions, the most prolific writer, the most powerful organization, output within a specific time frame, the rise of keywords, and so on. Article searches were conducted using the Scopus database from 2007 until 2024. The number of studies undertaken in this sector has significantly increased in recent years, as evidenced by bibliometric studies, with China and the United States being the leading contributors. The literature frequently mentioned hedonistic consumption habits, the impact of social media, website design, and promotion. Future studies should look more closely at how emerging technologies like virtual reality and artificial intelligence affect impulsive purchasing behaviour. More comprehensive insights could also be obtained by conducting in-depth studies on cultural variations and the ways in which cultural elements influence impulsive internet purchases.

Towards Ethical AI Moderation in Multiplayer Games

AI is increasingly being used to moderate player behaviour in online multiplayer games, working to identify and respond to toxic and problematic conduct with greater efficiency and accuracy than existing automated systems. However, little work has explored the application of AI moderation in the gaming ecosystem, despite growing ethical concerns about AI applications in other domains. In this study, we conducted 2 expert workshops and interviewed 26 players and industry professionals on their understandings, perceptions and experiences with AI moderation in multiplayer games. Applying a metaphorical frame via template analysis, we outline four metaphors that capture participants' views on the roles of AI and automation in moderation: the Unreliable Police Force, the Unscrupulous Governor, the Uncaring Judge, and the Untiring Assistant. We discuss these roles as exacerbating a top-down, punitive online justice system and identify ethical concerns around transparency, fairness and inclusion, privacy, and human-AI collaboration. To address these concerns, we put forward a set of ethical design considerations and alternative roles for AI moderation in multiplayer games.

A lean approach to comparing the mooring systems of Suezmax tankers

AbstractMooring Suezmax tankers is a process requiring skilled ship crews and terminal workers to accomplish the time-consuming, hazardous task of securing the ship to a jetty to load or discharge oil cargoes. The advent of new equipment technologies has enabled industry professionals to view the mooring process from a new perspective. A lean approach is applied to analysing alternative mooring processes of Suezmax tankers. A reduction in mooring time of about 30 min is noted when replacing steel wire with high modulus synthetic fibre (HMSF) ropes. Further, a potential time saving of 57 min is noted if automated mooring is used instead of steel wire, and 27 min compared with HMSF. The automated mooring concept reduces the manpower required to perform the mooring process, both on board and ashore. If HMSF lines replace steel wires, a significant reduction is noted in ship and crew time costs incurred with mooring maintenance onboard during sailing. A single case study addresses the two research questions: (1) What is the mooring process of Suezmax tankers? (2) How can the mooring process of Suezmax tankers be enhanced?

Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method.

Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Streptococcus thermophilus (S. thermophilus) are commonly used starters in milk fermentation. Fermentation experiments revealed that L. bulgaricus-S. thermophilus interactions (LbStI) substantially impact dairy product quality and production. Traditional biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations, an artificial intelligence-based method for screening interactive starter combinations is necessary. However, in the current research on artificial intelligence based interaction prediction in the field of bioinformatics, most successful models adopt supervised learning methods, and there is a lack of research on interaction prediction with only a small number of labeled samples. Hence, this study aimed to develop a semi-supervised learning framework for predicting LbStI using genomic data from 362 isolates (181 per species). The framework consisted of a two-part model: a co-clustering prediction model (based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset) and a Laplacian regularized least squares prediction model (based on K-mer analysis and gene composition of all isolates datasets). To enhance accuracy, we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate LbStI prediction results, which were verified through milk fermentation experiments. Validation through milk fermentation experiments confirmed a high precision rate of 85% (17/20; validated with 20 randomly selected combinations of expected interacting isolates). Our data suggest that the biosynthetic pathways of cysteine, riboflavin, teichoic acid, and exopolysaccharides, as well as the ATP-binding cassette transport systems, contribute to the mutualistic relationship between these starter bacteria during milk fermentation. However, this finding requires further experimental verification. The presented model and data are valuable resources for academics and industry professionals interested in screening dairy starter cultures and understanding their interactions.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms.

Physics Guided Neural Networks with Knowledge Graph

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN.

Visualizing Walmart's Supply Chain Management: A Case Study on Detailed Warehouse Management Practices

Supply chain management (SCM) plays a critical role in the success of modern businesses. Visualization techniques have become increasingly important in improving these processes. These methods have become increasingly significant in recent years, offering new ways to understand and optimize complex supply chain processes. This study focuses on how Walmart uses visualization in its supply chain management to boost efficiency and decision-making. This paper examines the integration of technologies like real-time tracking and data analysis into Walmart's practices, discussing both the advantages and challenges they bring. Walmart has embraced visualization technologies to streamline its SCM processes and make more informed decisions. Furthermore, the paper explores how visualization can help with inventory management, demand forecasting, and logistics optimization in Walmart's supply chain. Combined with relevant literature and case studies, it sheds light on how visualization enhances supply chain operations, offering insights for academics and industry professionals alike, that give good suggestions to the market.

International Approaches to the Development, Validation, and Change Management of Analytical Procedures (Review)

INTRODUCTION. In 2023, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) published a new Guideline on Analytical Procedure Development (ICH Q14) and a revised version of the Guideline on Validation of Analytical Procedures (ICH Q2(R2)). Consequently, there is a need for a considerable revision of the approach to the development and validation of analytical procedures that is currently used in the Eurasian Economic Union (EAEU). A revision is also needed for the processes for evaluating and introducing variations to the analytical procedures described in medicinal product registration dossiers.AIM. This review aimed to analyse the significant changes made to international approaches to the development of analytical procedures, as well as to study the advantages and disadvantages of these approaches for pharmaceutical manufacturers and regulatory agencies in the EAEU.DISCUSSION. This review covers the key provisions and practical aspects of the enhanced approaches to the development of analytical procedures introduced by the ICH Q14 guideline. In particular, the review addresses the concepts of the analytical procedure life cycle (APLC) and the modified analytical qualityby-design (AQbD) approach; the development of the analytical target profile (ATP); analytical quality risk management; planning of the design of experiments (DoE) and the analytical procedure control strategy; and the validation, subsequent verification, transfer, and change management of analytical procedures. Additionally, the review describes the ICH Q2(R2) updates that accompany this new regulatory paradigm.CONCLUSIONS. The above guidelines fill the existing gap in recommendations for the development of analytical procedures. The use of the APLC and AQbD concepts provides both pharmaceutical companies and regulatory authorities with flexible approaches that are applicable to analytical procedures both during the development phase and once they have been implemented. Effective implementation of these international approaches in the Russian pharmaceutical industry and regulatory system requires a broad discussion between pharmaceutical industry professionals and regulatory agency experts, possibly, as part of a pilot project. After that, there will be a necessity to provide training for specialists involved in the development of analytical procedures and to amend the EAEU Rules for Marketing Authorisation and Expert Assessment of Medicinal Products for Human Use.

Agile project management for sustainable residential construction: A study of critical success factors

Sustainability principles should be incorporated into all decision-making stages for residential construction projects to ensure maximum revenue while maintaining essential residential building services. This study identifies and analyzes the critical success factors (CSFs) necessary for implementing agile project management (APM) in residential construction projects. Data were collected from 120 professionals in the Nigerian construction industry through questionnaire surveys to understand the implementation of APM. The CSFs were obtained from previous research and analyzed within the specific context of the Nigerian construction industry through questionnaire surveys. The CSFs were grouped into two main categories using exploratory factor analysis: dynamic project optimization and agile project foundations. The model for the CSFs was developed using partial least squares structural equation modeling. The study found that the dynamic project optimization element had the most significant impact on the model, highlighting its importance as a key CSF in APM implementation. These results support the adoption of APM within Nigeria’s construction industry as stakeholders and professionals seek effective strategies to reduce costs and improve sustainability.

Design for steel structures deconstruction: an analytics system for construction waste minimization in a circular economy through BIM technology

This study focuses on shifting from traditional demolition methods to more sustainable deconstruction procedures in the steel structures building sector, specifically through Design for Deconstruction (DfD) techniques. The aim is to identify factors for BIM for deconstruction (BIMfD) implementation in Egypt’s construction industry and create a BIM-based Deconstructability Assessment Score (BIM-DAS). A literature review is conducted to determine the design principles for effective building deconstruction, the performance evaluation of DfD, and existing views on BIM implementation. The development of the BIM-DAS & SP includes mathematical modelling rooted in material demand planning. The model was implemented in Building Information Modelling (BIM) environment and it was tested using a case study design. Custom parameters related to deconstruction are added and used in the mathematical model to calculate DAS and salvage performance (SP), followed by a questionnaire and analytical hierarchy process analysis. These findings highlight demountable connections and reusable components as crucial drivers of DfD. Also, the results show that the key variables affecting BIMfD deployment include the availability and acceptance of BIM tools for deconstruction technology among industry professionals, and collaboration between project design and deconstruction teams through integrated platforms. This study demonstrates the usefulness of BIM-DAS and SP as indicators of efficient disassembly of buildings. This study will benefit all stakeholders in designing steel structure deconstruction by providing baseline specifications and a hierarchical model for implementing the BIMfD using current software tools. The results provide the needed technological support for developing tools for BIM-compliant DfD tools in Egypt to encourage deconstruction.

Utilities and the Future Grid

It is widely accepted among energy industry professionals that the industry is undergoing a significant and rapid transformation. There are increasing challenges facing the industry including extreme weather events, widespread aging of the electric grid infrastructure, rapidly increasing electricity demand, as well as the impact of climate change policy initiatives. This has spurred an innovation rebirth in an industry that has traditionally been viewed as outdated, and most importantly inaccessible for participation by average customers. However, as the challenges of the changing industry and effects of climate change are impacting their day‐to‐day lifestyles, energy customers are now demanding more visibility, choices, and flexibility from their energy providers, putting a spotlight on the roles and services of utility companies.

Digital twin technology (DTT) in a developing construction industry: evaluation of the awareness and usage among professionals in Nigeria

Technological innovations play a crucial role in project delivery across all sectors of the economy. This research seeks to assess the awareness of digital twin technology (DTT) among construction industry professionals to promote the sustainable completion of construction projects. A quantitative approach was adopted by using a survey questionnaire with convenience sampling. Questionnaires were self-administered to gather information about the industry professionals’ awareness and utilization of DTT in construction projects. Mean Item Score (MIS), Standard Deviation (SD), Spearman, and Kruskal-Wallis tests were used to analyze the collected data. Professionals within the construction industry have demonstrated a high level of familiarity with various technological advancements with computer systems, Wi-Fi, 2D Graphics, Vertex Digital twin platform, and GPS identified as the most commonly utilized tools in the field. Spearman correlation analysis revealed a significant relationship between DTT awareness and usage levels. The study provided valuable information on the present awareness and DTT utilization within the construction sector. It proposed methods through which professionals can familiarize themselves with empowering tools to enhance productivity and performance within the industry.

Origin of the Fourth Industrial Revolution: manufacturing predictions preceding Industrie 4.0

Purpose The Fourth Industrial Revolution is a prominent area of interest in the field of manufacturing that is bringing about revolutionary changes. In this study, the authors sought to determine whether professionals in academia or industry could have predicted the composition of advanced technologies associated with Industry 4.0 before Germany's Industrie 4.0 policy announcement. The purpose of this paper is to use the process for identifying technologies that can be included in industrial policy to improve national competitiveness in manufacturing. Design/methodology/approach Relevant documented research from 2000 to 2012 was identified and captured using a systematic literature review. The significant technological advancements during this period were analyzed to determine how technological innovations may have affected predictions about the future of manufacturing. The identified predictions were analyzed using an open-source natural language processing code that clustered relevant topics in the predictions that indicated common themes. The results were then compared to the ideas within “Industry 5.0”. Findings The results of this study showed that an aggregate analysis of manufacturing predictions would have preemptively revealed the Fourth Industrial Revolution and could have been used to inform industrial policy that could accelerate technology adoption. Also, contrary to popular belief, the popular Industry 5.0 is a sematic exemplification of a concept already embedded within the origins of the Fourth Industrial Revolution. Practical implications By examining the provenance of the Fourth Industrial Revolution, lessons are learned that bring light to Industry 4.0 and the measures that can be taken to enable the advancements that it brings. The results of this study show that is would be wise for government policymakers to enact programs that monitor the manufacturing predictions coming out of academia and to analyze them aggregately using natural language processing as a means to identify the next evolutions and revolutions and to mobilize policymakers to enhance outcomes of enacting policy. Originality/value Despite high hopes for the realization of Industry 4.0, there has been little discussion about the technological innovations and events that occurred to enable it. To the best of the authors’ knowledge, this is the first study that has determined that an aggregate analysis of manufacturing predictions would have preemptively revealed the Fourth Industrial Revolution. The success of the methodology used in this study has theoretical implications in support of natural language processing (NLP) being used to inform national policy.

Preface

1st National Conference on Integrating Research in Agriculture, Food and Communicable Diseases Towards Achieving Sustainable Development Goals (IRAFCD 2024) This proceedings volume features a curated selection of articles from the National Conference on Integrating Research in Agriculture, Food, and Communicable Diseases Towards Achieving the Sustainable Development Goals (IRAFCD 2024). The conference, held on June 10-11, 2024, at the Universiti Tunku Abdul Rahman (UTAR) Kampar Campus in Malaysia, brought together participants from Malaysia and overseas in a hybrid format. IRAFCD 2024 was convened to unite experts, researchers, industry professionals, and stakeholders across diverse fields. This conference aimed to explore innovative solutions, best practices, and collaborations within the intersections of communicable diseases, agriculture, food research, and biomedicine, emphasizing sustainable development’s critical role in addressing global health challenges. The objectives of this conference were multifaceted. We sought to foster interdisciplinary collaboration by providing a platform for knowledge sharing and encouraging innovative approaches towards achieving the Sustainable Development Goals (SDGs). Additionally, the conference highlighted the importance of sustainable development in improving health outcomes and socio-economic progress. Key research and policy issues, such as disease prevention, sustainable agriculture, and the role of technology in health challenges, were addressed through keynote speeches, plenary sessions, and panel discussions. Finally, the conference facilitated networking and partnerships, offering opportunities for participants to connect and collaborate across academia, industry, government, and nongovernmental organizations. These interactions are expected to contribute significantly to the implementation of sustainable development initiatives. We express our sincere appreciation to the organizing committees, sponsors, moderators, speakers, reviewers, and all participants who contributed to the success of IRAFCD 2024. Your dedication and support have been instrumental in advancing the conference’s goals and making a meaningful impact on global health and sustainable development. List of Editors, Organizing Committee, Scientific Committee, Moderators, Speakers and Sponsors are available in this Pdf.