Industrial Adoption Research Articles

A Study on Functional Requirements and Inspection Items for AI System Change Management and Model Improvement on the Web Platform

The rapid adoption of artificial intelligence (AI) on the web platform across multiple sectors has highlighted not only its inherent technical hurdles, such as unpredictability and lack of transparency, but also significant societal concerns. These include the misuse of AI technology, invasions of privacy, discrimination fueled by biased data, and infringements of copyright. Such challenges jeopardize the sustainable growth of AI and risk the erosion of societal trust, industry adoption and financial investment. This analysis explores the AI system’s lifecycle, emphasizing the essential continuous monitoring and the need for creating trustworthy AI technologies. It advocates for an ethically oriented development process to mitigate adverse effects and support sustainable progress. The dynamic and unpredictable nature of AI, compounded by variable data inputs and evolving distributions, requires consistent model updates and retraining to preserve the integrity of services. Addressing the ethical aspects, this paper outlines specific guidelines and evaluation criteria for AI development, proposing an adaptable feedback loop for model improvement. This method aims to detect and rectify performance declines through prompt retraining, thereby cultivating robust, ethically sound AI systems. Such systems are expected to maintain performance while ensuring user trust and adhering to data science and web technology standards. Ultimately, the study seeks to balance AI’s technological advancements with societal ethics and values, ensuring its role as a positive, reliable force across different industries. This balance is crucial for harmonizing innovation with the ethical use of data and science, thereby facilitating a future where AI contributes significantly and responsibly to societal well-being.

Exploring Internet of Things adoption challenges in manufacturing firms: A Delphi Fuzzy Analytical Hierarchy Process approach.

Innovation is key to gaining a sustainable edge in an increasingly competitive global manufacturing landscape. For Bangladesh's manufacturing sector to survive and thrive in today's cutthroat business environment, adopting transformative technologies such as the Internet of Things (IoT) is not a luxury but a necessity. This article tackles the formidable task of identifying and comprehensively evaluating the impediments to IoT adoption in the Bangladeshi manufacturing industry. We delve deeply into the complex terrain of IoT adoption challenges by synthesizing expert insights and a meticulously selected body of contemporary literature. We employ a robust methodology combining the Delphi method with the fuzzy Analytical Hierarchy Process to systematically analyze and prioritize these challenges. Using this methodology, we leveraged the combined expertise of domain specialists and subsequently employed fuzzy logic techniques to address the inherent ambiguities and uncertainties within the data. Our findings highlight this clear path. They reveal that among the myriad barriers, "Lack of top management commitment to implementing new technology" (B10), "High initial implementation investment costs" (B9), and "Risks associated with switching to a new business model" (B7) loom most extensive, demanding immediate attention. These insights are not confined to academia but serve as a pragmatic guide for industrial managers. Armed with the knowledge gleaned from this study, managers can craft tailored strategies, set well-informed priorities, and embark on a transformational journey toward harnessing the vast potential of IoT in the Bangladeshi industrial sector. This article provides a comprehensive understanding of IoT adoption challenges and industry leaders with the tools necessary to navigate these challenges effectively. This strategic navigation, in turn, contributes significantly to enhancing the competitiveness and sustainability of Bangladeshi manufacturing in the IoT era.

Bridging the AI Divide: Human and Responsible AI in News and Media Industries

Artificial intelligence (AI) is revolutionizing media and journalism, reshaping processes, content, and audience relationship. This editorial report discusses the opportunities and challenges AI brings to these industries raised by this collection of studies, emphasizing the importance of integrating ethical principles and human-centric approaches to its development. These articles examine how AI reshapes content creation, distribution, and audience interaction while addressing the ethical, political, and economic implications of its integration. Key contributions include discussions on the ethical challenges posed by AI—such as bias, transparency, and accountability—and the need for robust governance structures to foster trust and fairness in AI-driven media systems. A recurring theme in this issue is the AI divide, highlighting the disparity in access to AI technologies between the Global North and South. The special issue offers insights into how this divide exacerbates existing inequalities in media production and consumption. It also advocates for interdisciplinary approaches and collaborative efforts to ensure AI's benefits are equitably distributed across geographical and socio-economic contexts. By exploring both the opportunities and perils of AI adoption in media and news industries, this special issue serves as a comprehensive examination of the evolving relationship between AI, media, and society, offering pathways for responsible innovation and inclusive AI adoption worldwide.

Big Data Analytics and Innovation in the Sustainable Performance of Electronic Waste Reverse Logistics: An Empirical Study in India

Abstract Electronic waste (e-waste), encompassing discarded electrical and electronic devices, demands effective reverse logistics management to ensure optimal resource use and environmental preservation. Despite growing interest in Big Data Analytics within the scientific community, its slow practical implementation in e-waste management and the absence of validated measurement models hinder both industry adoption and empirical studies. To address these challenges and drawing upon the Resource-Capability-Advantage (RCA) theory, this study aims to investigate the interplay between Big Data Analytics (BDA) management capabilities, BDA talent capabilities, Reverse Logistics (RL) innovation, and sustainable RL performance. A conceptual model was tested using primary data from practitioners and managers in India’s e-waste reverse logistics network, with Structural Equation Modeling (SEM) as the primary analytical method. The results highlight the multifaceted contributions of Big Data Analytics Management and Talent Capabilities to Reverse Logistics Innovation and Sustainable Reverse Logistics Performance.

Exploring the viability of copper slag geopolymer concrete in structural applications: A study on strength variability and seismic risk assessment

This study explores the use of copper slag (CS) as a sustainable alternative to natural sand (NS) in geopolymer concrete (GPC) for structural applications. Despite the potential of CS to promote environmental sustainability by repurposing industrial waste, its adoption in the construction industry remains limited. To address this gap, the study focuses on two main aspects: (i) modelling the uncertainty in the strength properties of CS-based GPC, and (ii) assessing the performance of CS-based GPC in seismic-resistant reinforced concrete (RC) framed buildings. The results show that the compressive strength of CS-based GPC is best represented by the Gumbel max and Gamma distributions, while the splitting tensile strength is well-modelled by Gamma and lognormal distributions. The flexural strength follows Gumbel max and Weibull distributions. These findings provide valuable data for probabilistic structural analysis and reliability-based design. Additionally, the seismic performance of RC frames made with GPC containing CS was found to be comparable to that of control frames with NS, confirming the viability of CS-based GPC in seismic-resistant structures. This research demonstrates that CS can be effectively utilized in GPC without compromising structural safety, contributing to more sustainable concrete construction practices.

Utilizing Electrooxidation for Textile Effluent Wastewater Treatment and Simultaneous Electrocatalytic Hydrogen Production: Transforming Waste into Energy and Promoting Water Reuse in a Circular Economy Context

Textile effluent wastewater poses a serious environmental risk because of its high concentration of pollutants, which include organic compounds, heavy metals, and dyes. The present study investigates the technical and economic feasibility of hybrid water electrolysis performances. Specifically, real textile effluent wastewater was utilised to examine simultaneous abatement and electrochemical hydrogen production. The treated water can be recycled in the textile mill, offering the benefits of trash-to-treasure and cost savings through the circular economy. In addition to reducing the environmental impact of textile wastewater, the synergistic approach seeks to maximise its potential for producing hydrogen as clean energy. Here, the commercially available stainless sheet was used as the anode in the electrochemical setup system and the two-dimensional Ti3C2TX MXene was used as the catalyst embedded cathode. The optimal electrode-electrolyte parameter settings resulted in an 83% decrease in COD level and a degradation efficiency of about 88%. The potential for widespread adoption in the textile industry is highlighted by the discussion of the economic viability and environmental advantages of using wastewater from textile effluents for pollutant degradation and hydrogen production. Hence, the energy estimation was looked at and estimated in order to evaluate the process viability. For instance, the hybrid electrolysis process uses a very small amount of electricity (0.825 kWh m-3 order-1) and has an apparent operating current (30 mA/cm2). This work could serve as a guide for the methodical assessment and choice of hybrid water electrolysis using actual wastewater. The electrode's recyclability and reuse were proven for possible commercial applications. The stability of the Ti3C2TX electrode over a wide pH range was investigated in order to produce hydrogen on a big scale at a reasonable cost.

On-line inspection of lattice structures and metamaterials via in-situ imaging in Additive Manufacturing

As advanced production capabilities are moving towards novel types of geometries as well as higher customization demands, a new and more efficient approach for process and part qualification is becoming an urgent need in industry. The layerwise nature of additive manufacturing (AM) potentially allows anticipating qualification tasks in-line and in-process, aiming at reducing the time and costs devoted to post-process inspections, enabling at the same time an early detection of defects since their onset stage. Such opportunity is particularly attractive in the presence of highly complex shapes like lattice structures or metamaterials, which have been increasingly investigated for industrial adoption in various sectors, aiming to achieve enhanced mechanical properties and innovative functionalities. This paper presents a novel methodology to inspect the geometry of lattice structures while the part is being built. The method is specifically designed to tackle the natural variability affecting layerwise images gathered in laser powder bed fusion. To this aim, it combines the segmentation of in-situ powder bed images of solidified layers with a data modelling approach to synthesize the 3-D shape of each unit cell into a 1-D profile representation. Such low-dimensional representation is suitable to quickly detect undesired distortions that may have a detrimental impact on final quality and performance. By using post-process X-ray computed tomography as ground truth reference, this study shows the effectiveness of the proposed approach for in-line inspection, opening a novel and cost-efficient way to address complex shape qualification for lattice structures in AM.

Oxygen Diffusion Effects in Thermo-oxidative Degradation of Typical Tire Rubber

Thermo-oxidative degradation of tire rubber has been demonstrated as a green method for upcycling waste tire rubber. However, the complicated tire compositions present challenges to achieving the homogeneity and efficiency of the reclaimed products, which restricts their widespread industrial adoption. To address this challenge, natural rubber(NR)and natural rubber/butadiene rubber(NR/BR)were innovatively designed to simulate complex tire compositions and investigate the influence of oxygen diffusion on thermo-oxidative degradation at 150-240°C. The evolution of chemical structural changes and mechanical properties during degradation was traced by FTIR, UV-vis, and nanoindentation test. A basic reactive-diffusion model based on Fickian oxygen diffusion was used to simulate the diffusion profiles. It was found that recrosslinking decreases the oxygen permeability coefficient during NR/BR degradation as the temperature increases, making it difficult for oxygen to diffuse into the inner layer, and therefore tire rubber degrades unevenly. Lower temperatures and prolonged treatment times were recommended to enhance degradation. These findings provide substantial guidance for optimizing the recycling process of tire rubber and its sustainable utilization.

ANÁLISE SOBRE A INDÚSTRIA 4.0 NA DISTRIBUIÇÃO DE ENERGIA E O FUTURO DAS REDES INTELIGENTES

The technological evolution brought about by Industry 4.0 has significantly impacted several sectors, including the energy sector. The introduction of smart grids into the energy distribution system offers solutions to increase efficiency, integrate renewable sources, and reduce losses. These networks use technologies such as automation, big data, and the Internet of Things to modernize the way energy is managed and distributed. The overall objective of this research was to analyze the impact of Industry 4.0 on energy distribution, focusing on the implementation of smart grids in Brazil. To achieve this objective, the methodology adopted was a literature review with a qualitative approach, using articles published between 2020 and 2024, obtained from databases such as Lilacs, Periódicos Capes, and Scielo. The research addressed the benefits and challenges of modernizing the Brazilian electricity system, with an emphasis on the integration of renewable sources, such as solar and wind, and on combating non-technical losses. The results indicate that smart grids contribute to greater efficiency in the energy distribution system, enabling real-time management, in addition to promoting better security against failures and cyber threats. Another relevant point identified was the importance of training professionals capable of implementing these new technologies. The research concludes that the adoption of Industry 4.0 in the energy sector is a necessary step towards the sustainability and modernization of the electrical system in Brazil.

Challenges and Opportunities in the Adoption of Flexible Manufacturing Systems in Indian Manufacturing Industries

The manufacturing sector is increasingly adopting automation technologies to enhance productivity and flexibility. Among these technologies, Flexible Manufacturing Systems (FMS) offer substantial benefits, such as reduced lead times, improved production flexibility, and lowered operational costs. Despite its advantages, FMS adoption in Indian manufacturing industries has been slow due to several barriers, including high capital costs, technological complexity, and inadequate government support. This study investigates the current status of FMS adoption in India, identifies the key barriers hindering its implementation, and proposes a decision-making model to facilitate its wider adoption. The research gap highlights the lack of a comprehensive framework to quantify the barriers and assess the performance of FMS adoption. To address this, a two-phase Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology was used. This approach allowed for the classification and prioritization of barriers, as well as the development of a model for FMS selection based on statistical analysis. A case study was conducted using this methodology to evaluate various FMS models and provide a systematic decision-making framework for manufacturers. The results revealed that the most significant barriers to FMS adoption in India include high upfront costs, insufficient infrastructure, and the challenges posed by rapidly advancing FMS technology. By applying the AHP-TOPSIS approach, this study offers insights into overcoming these barriers and presents actionable recommendations for manufacturers and policymakers. The findings emphasize the need for increased government support and industry collaboration to accelerate FMS adoption in India. This research contributes to the development of a structured decision-making framework, which could help manufacturers implement FMS more effectively, thereby enhancing competitiveness in an increasingly dynamic global market. Keywords— Flexible Manufacturing Systems, Barriers to FMS, Indian Manufacturing, AHP-TOPSIS, Decision-Making Model

Hydraulic modeling and real-time optimization of drilling fluids: A future perspective

The evolution of drilling operations in the oil and gas industry has highlighted the need for more efficient and adaptive management of drilling fluids, particularly in complex well environments. This paper presents a comprehensive review of hydraulic modeling and real-time optimization of drilling fluids, focusing on future perspectives and industry implications. Hydraulic modeling plays a crucial role in predicting pressure, fluid flow, and wellbore stability, while real-time optimization, powered by advanced sensors, AI, and machine learning, enables dynamic fluid management. The integration of these technologies allows for continuous monitoring and adjustment of fluid properties, improving efficiency, reducing downtime, and preventing formation damage. Future technological advancements, including digital twins and enhanced downhole sensors, are expected to improve well performance and cost-efficiency further. Strategic recommendations for industry adoption include investment in advanced technologies and further research into hydraulic modeling and real-time optimization for complex wells.

The influence of business intelligence on the effectiveness of supply chain management in the Nigerian consumer goods sector

Business performance depends on supply chain management (SCM), which can be enhanced through digital transformation to enable quick decisions to increase market share. By enabling informed decision-making, business intelligence (BI) can facilitate this improvement in SCM effectiveness and competitive edge. Real-time production and inventory level monitoring is made possible by BI capabilities, which help organisations adapt to changing market trends and meet customer demands. Particularly, BI technology can increase the SCM of enterprises in the consumer products sector by providing value-added and financial gains, enabling speedy industry adoption and rapid industry growth. The study employed a quantitative methodology for gathering data, and a simple random sampling method was employed to prevent sample bias. The study considered linear curve estimation (LCE) as a method of data analysis. The results of the model estimations showed that BI recorded positive and significant influence on SC visibility, flexibility, and resilience in the Nigerian consumer goods sector. The study concludes that BI increased the effectiveness of supply chain management (SCM) by improving visibility, anticipating demand, increasing resilience, optimising processes, and boosting productivity. SCM effectiveness depends on the efficient handling of disruptions and uncertainty. The study highlights the practical policy implications following the findings of the study.

Perceptions and challenges of blockchain adoption in tourism industry: a study on trust, privacy and security

Purpose This study aims to investigate the perceptions of service providers in the travel and hospitality industry toward the adoption of blockchain technology (BCT), focusing on its impact on consumer experiences and expectations, especially in terms of trustworthiness and the management of privacy and security concerns. Design/methodology/approach The research used a quantitative methodology, collecting data from 135 industry practitioners across five tourism sectors (lodging, connected industries, entertainment, FandB, transportation) in Europe. This approach aimed to understand the diverse perspectives on the benefits and challenges of implementing BCT in their opera Findings The results indicate mixed perceptions regarding blockchain adoption. While there are positive views on BCT’s potential to enhance customer experience and service quality, there are significant concerns about its impact on trust and security. A consensus exists between customer and provider perspectives on BCT’s trustworthiness, but significant differences were observed in views on privacy and security enhancement. Originality/value This research contributes to the existing literature by providing insights into the service providers’ and customers’ perspectives on BCT within the travel and hospitality industry. It underscores the complexities of adopting new technologies and calls for more in-depth studies to address the identified concerns, thereby offering a novel viewpoint on the adoption of distributed ledger technologies in enhancing customer experience and service delivery.

Adoption of Industry 4.0 Technologies in Chilean Mining: A Comparative Analysis Between Sectors

Adoption of Industry 4.0 Technologies in Chilean Mining: A Comparative Analysis Between Sectors

PENERAPAN TEKNOLOGI INDUSTRI 4.0 DALAM LEAN WAREHOUSING: LITERATURE REVIEW

Warehouse plays a crucial role in the supply chain by linking material flows between suppliers and customers, and also matching demand with product availability. Operational disruptions in warehouse can lead to decreased customer satisfaction, necessitating effective and efficient warehouse operations. Lean principles, including lean warehousing, are often used to achieve this. Additionally, increasing supply chain complexity makes companies vulnerable to costly errors, leading to broader adoption of industry 4.0 technologies. Various studies have begun integrating industry 4.0 technologies into lean warehousing practices. However, literature reviews on integrating industry 4.0 with lean principles tend to focus on manufacturing area. This study aims to review researches on the application of industry 4.0 technologies in lean warehousing through a literature review. The findings of the literature review indicate that autonomous robots, simulation, horizontal and vertical system integration, and the industrial internet of things are industry 4.0 pillars that are widely applied alongside lean warehousing methodologies and tools. Most studies analyze overall warehouse processes, while others focus on specific processes within the warehouse. Overall, the use of industry 4.0 technologies in lean warehousing leads to significant positive impacts, including increased efficiency and productivity. Further studies are needed to understand how lean warehousing can serve as a foundation for implementing industry 4.0 technologies and to address the issues and challenges in their concurrent application.

An Emerging Solid‐State Electrolyte Reactor to Drive the Future of Electrochemical Synthesis

AbstractElectrochemical reactors, powered by renewable electricity, have garnered widespread attention for chemical synthesis due to their low energy consumption and pollution‐free features. However, the inherent design flaw of traditional electrochemical reactors has persistently hindered the advancement of electrochemical synthesis, as they result in low product concentrations, low purity, and continuous production issues. As a novel electrochemical reactor, the porous solid‐state electrolyte (PSE) reactor is elaborately designed to overcome the limitation by enabling the direct and continuous synthesis of pure products, possessing a modular and scalable structure with high efficiency, safety, and long stability. In this work, first, the distinctive design of the PSE reactor, highlighting its structural features, core components, and variable configurations, is introduced. Furthermore, the configuration‐relevant applications in electrosynthesis, such as formic acid, acetic acid, and hydrogen peroxide (H2O2) production, are summarized. Integrated applications are also discussed, along with potential domains for improvements and optimization. Finally, the future developmental directions of the PSE devices are thoroughly explored. By addressing its unique design attributes, showcasing its capabilities, and envisioning prospective refinements and diverse applications, the aim is to boost the progression of this transformative technology toward widespread commercialization and industrial adoption, thereby revolutionizing sustainable electrochemical synthesis.

Barriers Hindering Green Building Materials Adoption in the Nigerian Construction Industry

Barriers Hindering Green Building Materials Adoption in the Nigerian Construction Industry

A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.

Trehalulose: Exploring its benefits, biosynthesis, and enhanced production techniques

The increasing concern over sugar-related health issues has sparked research interest in seeking alternatives to sucrose. Trehalulose, a beneficial structural isomer of sucrose, is a non-cariogenic sugar with a low glycemic and insulinemic index. Besides its potential as a sugar substitute, trehalulose exhibits high antioxidant properties, making it attractive for various industrial applications. Despite its numerous advantages and potential application in various sectors, the industrial adoption of trehalulose has yet to be established due to lack of studies on its characteristics and practical uses. This review aims to provide a comprehensive overview of the properties of trehalulose, emphasizing its health benefits. The industrial prospects of trehalulose as sweetener and reducing agent, particularly in food and beverages pharmaceutical, and cosmeceutical sectors, are explored. Additionally, the review delves into the sources of trehalulose and the diverse organisms capable of producing trehalulose. The biosynthesis of this sugar primarily involves an enzyme-mediated process. Thus, these enzymes' properties, mechanisms, and the heterologous expression of genes associated with trehalulose production are explored. The strategies discussed in this review can be improved and applied to establish trehalulose bio-factories for efficient synthesis of trehalulose in the future. With further research and development, trehalulose holds promise as a valuable component across various industries.

Job roles for digital twinning building construction processes. Introducing the digital twin manager position

Digital Twins (DT) holds the potential to enhance management and monitoring in the construction sector by means of an interconnected information environment. Although DT theoretical frameworks have been proposed and numerous literature reviews conducted, real-world demo cases showing requirements and challenges are yet limited. These existing frameworks lack of grounded verification on real scenarios to identify essential roles and skills for effective knowledge transfer. This paper contributes to bridge these gaps by harvesting the knowledge of twinning a real-world office building. Key takeaways include a general mind map and two complementary workflows for DT generation and operation for building construction processes. The information pipeline from data collection to decision-making is described and required skills and roles are identified along this data journey. An identification of the emerging role of DT Managers is presented, a vital yet underexplored position in current construction literature. The presented practical roadmap paves the way for increasing maturity on DTs and for promoting their industry adoption.