Technological Transformation Research Articles

Penerapan Digital Marketing sebagai Upaya Peningkatan UMKM Keripik Pepaya di Desa Lubuk Enau

The ever-evolving technological transformation can produce innovations that attract public attention over time. Digital marketing is a form of effort that uses digital media, such as the internet, to promote and market a brand. However, the lack of understanding of village communities regarding sales in the market is a problem that needs to be understood. Lubuk Enau Village is a place for community service activities. This social service activity was carried out by KKN-Thematic students of the Faculty of Economics, Universitas Indo Global Mandiri, in the context of Sustainable Entrepreneurship Development Efforts in Lubuk Enau Village. This guidance is expected to make it easier for MSME economic actors to create a marketplace that aims to sell processed culinary products such as Instagram, Facebook, WhatsApp, and other e-commerce. Increasing online sales promotions aims to expand the marketing of papaya chips so that it can increase the economy of business actors.

Strategic priorities for digitalization of the mining sector of Kazakhstan

Global uncertainty has transformed the economy of Kazakhstan, which is striving to become a center of innovative development in Central Asia. According to the authors, one of the reasons for the raw material orientation of the country's economy, along with the predominance of energy-intensive types of production, is the technological backwardness of the mining sector. The article shows that the strategic goal of the Kazakhstan mining complex to integrate into the world economy involves a focus on innovative development. PEST analysis of the most important micro- and macroeconomic factors made it possible to assess the current situation of enterprises. It was revealed that one of the significant reasons for the innovative lag in the mining sector of Kazakhstan is the lack of a strategic vision of digital transformation and change management mechanisms among enterprises. The authors conclude that the continuous complication of mining and geological conditions and the growing demands of stakeholders require enterprises to build internal production processes, promote R&D, improve the organization of analytical work based on an engineering and economic approach to the selection of tools for the digital basis of production, and active interaction with external partners and startups. It is substantiated that for further progress an integrated approach to the creation of intra-production digital competencies is necessary; enterprises should focus on organizational and technological transformation, including changes in the organizational structure and transformation of the business model. The results of the study are of interest to industrial enterprises when they develop proactive innovative strategies focused on reorganizing operational activities and increasing investment to promote business.

B-025 Revolutionizing NGS-driven Laboratory Developed Tests: The impact of automated liquid handling on plate-based bead clean-up

Abstract Background Automated liquid handling systems are proving to be a transformative technology in the landscape of Laboratory Developed Tests (LDTs), especially those utilizing Next-Generation Sequencing (NGS). By enhancing accuracy, boosting efficiency, and offering scalable solutions, these systems enable labs to meet current and future challenges more effectively. The right system not only improves the quality of diagnostics but also positions labs to be agile and adaptive in a rapidly advancing healthcare environment. firefly® is a well-suited liquid handling solution for diagnostics labs developing LDTs due to its compact footprint, user-friendly software and exceptional versatility. Validated protocol templates that support NGS library preparation are readily available from the firefly cloud and can be adapted to meet specific lab needs. For example, automated bead-based clean-ups designed for 96- and 384- NGS applications are available on firefly. These remove DNA fragments of undesirable sizes, enzymes or primers rom the amplified DNA after a PCR reaction. Automating this process facilitates higher throughput and scaling. firefly has a unique combination of 6 positive displacement dispense heads and a 96/384-well air-displacement pipetting head, making it ideal to distribute the reagents required for bead clean-ups. Methods firefly protocols were developed and optimized to be compatible with a range of elution volumes and bead ratios. Protocol performance was tested using library DNA (∼300bp). The same input DNA was plated out and quantified using the QuantiFluor dsDNA system (Promega) before and after running the protocol. This data was used to assess any reduction in DNA concentration and uniformity across the plate. The protocol's ability to successfully modulate the fragment size distribution was demonstrated using a 50bp DNA ladder and different bead ratios. Data was obtained using a Fragment Analyzer (Agilent, DNF-474 HS NGS Fragment Kit). Well-to-well contamination was assessed by running no-template-controls (NTCs) in wells adjacent to library DNA, then analyzed by qPCR using a LightCycler 480 System (Roche, KAPA Library Quantification kit). Results 96 format has a setup time of 10 minutes, run time of 43 and %CV library concentration of <6%. 384 format has a setup time of 10 minutes, run time of 47 and %CV library concentration of <8%. Uniformity of concentration and fragment sizes was seen across the plates. Comparison of the library concentration before and after the clean-up showed a 5% reduction in DNA concentration after the 96-well plate clean-up and an 11% reduction in library concentration when using the 384-well plate clean-up. Fragment size analysis showed that fragment sizes can be successfully modulated on firefly by changing the bead ratio of the firefly run and no detectable well-to-well contamination was detected by qPCR. Conclusions We have demonstrated that automated bead clean-ups on firefly are uniform show uniformity across both 96- and 384-well plates, with no detectable well-to-well contamination, to deliver the consistent and robust results required for an LDT. Bead-based cleanups on firefly offer a fast, 100% walkaway solution. By running 96-samples at once on firefly, we see approximately a 6-fold increase in throughput than when performed manually by a single user.

Remote sensing image classification based on non-linear enhanced attention mechanism

Abstract In recent years, Transformer technology has gradually shown promising applications in the field of computer vision, becoming a research hotspot. However, traditional vision Transformers suffer from significant computational burdens. Although previous studies have attempted to alleviate this issue by reducing the computational load of attention mechanisms, their methods still require improvement. Additionally, past research often overlooked the non-linear representation of values within attention mechanisms, posing another challenge to address. Therefore, this paper proposes a novel attention mechanism aimed at reducing the computational burden of traditional attention mechanisms while enhancing their ability to express non-linearities in values. Furthermore, conventional feedforward neural networks typically output results directly after passing through an activation function, which may limit the model’s representational capacity due to the simplistic structure leading to singular feature information. To address this issue, this study introduces a more robust feature fusion convolutional feedforward network. Through these methods, the aim is to enhance the accuracy of Transformers in the field of remote-sensing image classification.

A STUDY ON THE APPLICATION OF AI IN PYTHON PROGRAMMING IN 21ST CENTURY

Artificial Intelligence (AI) has emerged as a transformative technology, reshaping industries and revolutionizing how we approach problem-solving. Python, with its simplicity and powerful libraries, has become the preferred programming language for AI development. This paper explores the application of AI in Python programming, highlighting its impact, the evolution of tools and libraries, practical applications, challenges, and future trends. By examining key areas such as machine learning, natural language processing, computer vision, and reinforcement learning, we aim to provide a comprehensive understanding of how AI, powered by Python, is driving innovation in the 21st century.

Generative AI: Shaping the Future While Disrupting the Present

Generative AI, a transformative technology in the world of artificial intelligence, is reshaping how we create and interact with digital content across various fields like art, business, and healthcare. This paper delves into the historical journey of generative AI, starting from early neural networks to recent developments like GPT-4 and diffusion-based models. By exploring pivotal technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer architectures, we offer a detailed analysis of how these models have revolutionized content generation. While these advancements open new doors for creativity and innovation, they also introduce significant challenges. Issues of bias, ethical concerns, and the environmental costs of AI particularly the growing water consumption for data centers are discussed at length. The paper further examines the dual impact of generative AI: its ability to enhance productivity while also causing disruptions in traditional industries and human interactions. As the use of AI scales, this research highlights the urgent need for sustainable and ethical approaches to its development and deployment. By examining both the potential and the pitfalls of generative AI, this study aims to provide a balanced outlook on the future of this influential technology.

Future-Proofing Your Organization: Key Principles for Leading Through the AI Transition

As artificial intelligence (AI) advances rapidly, organizations must develop meaningful long-term strategies to realize opportunities and mitigate risks, as academic research shows AI will significantly impact over 50% of jobs by 2030, requiring workforce planning, while industries like travel agencies have faced disruption from failing to adapt to digital technologies like AI. Effective AI strategies include establishing integrated 3-5 year technology roadmaps that articulate adoption timelines, skill development, and business transformations, identifying core capabilities to augment with AI, such as diagnostics for healthcare, while data and technology foundations are also critical for deploying AI projects at scale and continuous skills training is needed to fill emerging roles and offset potential job losses. The article discusses strategic considerations for industries like healthcare, where strategies focus on augmenting clinicians, standardizing data, and establishing governance, transportation organizations must plan for autonomous vehicle testing and adoption, and reskill mechanics, and financial institutions can develop robo-advisors and lending platforms while modernizing legacy systems. Therefore, organizational leaders need such integrated roadmaps and ongoing governance to leverage AI's productivity benefits while guiding responsible adoption and workforce evolution amid ongoing disruption through proactive strategic planning that will help direct, rather than undergo, technological transformation.

A Systematic Review of Organizational Resilience Through Digital Technology Adoption: Trends and Insights in a Decade

Numerous organizations have been compelled to adopt new ways of managing work and the environment because of the pandemic. Understanding the resilience dynamics during the adoption of digital technology is more important than ever considering challenges like terrorism, economic recessions, mass migration, cyberattacks, and a variety of other socio-political and economic trends. Reviewing publications that examine the connection between digital technologies and organizational resilience from 2014 to 2024 is the goal of this project. The PRISMA approach was used to conduct a systematic review. 47 relevant papers were found as a consequence of this, most of which came from the Scopus database. This implies that academics are engaged in the subject. The papers consistently cited the diffusion of innovation (DOI) theory and the technology-organization-environment (TOE) framework, with a bias towards quantitative research methods. Five major themes emerge from the analysis: digital transformation, organizational resilience, business performance, innovation, and adoption of digital technology. By providing new perspectives, the study closes gaps in the literature. It was believed that the Journal of Technological Forecasting and Social Change was a valuable source of information on studies on digital adoption. Based on the results, the report offers recommendations and points up directions for future research to increase organizational resilience by utilizing digital technology. Organizations may leverage digital technology to effectively overcome obstacles and prosper in a constantly changing landscape by implementing these suggested practices.

A seat at the energy table

Abstract Energy injustice persists due to the difficulty of meeting competing interests in a rapidly evolving system. Transformative energy technologies need to fit into existing structures while also improving upon those systems. To aid in complex decisions, this paper provides a simple checklist of four necessary and often missed concepts for energy justice. Energy choices should be Supported, Environmental, Affordable, and Tolerable to provide everyone a SEAT at the energy table, in other words to promote energy justice. For the purposes of this framework, supported indicates that supply chains and infrastructure to meaningfully participate are available, environmental considerations cover climate change and health-affecting pollutants as well as disposal affecting soil and water, affordability rather straight-forwardly requires equal economic access to the energy transition, and tolerable requires personal and political buy-in. This structure is intended to aid anyone that makes decisions, i.e. not only highly specialized academics. Many technologies that address one or two of these concepts are touted as solutions to injustice, but without additional structure, they may have disbenefits in other areas. By using a simple structure to illuminate this consistent difficulty, future research and policy will be better able to promote true justice instead of tangentially addressing it in ways that fail to capture the complexities of the system. The tension between benefits and unintended consequences is explored for several energy technologies at varying stages of implementation, including electric vehicles, cooking, time-of-use pricing, and air conditioning. Existing policy mechanisms that have tried to address only some barriers have not necessarily resulted in an equitable transition. This indicates that equity needs to be considered explicitly, and that all four chair legs need to be evaluated. This method highlights the principles most easily considered in non-specialist decisions and maps them to concepts that are easy to grasp.

GPT Technology and Systemic Transformations in Issuance and Drafting of Fatwas in Islamic Finance

GPT Technology and Systemic Transformations in Issuance and Drafting of Fatwas in Islamic Finance

Systemic Perspective on Companies and the Digital Transformation: A Prototypical Methodological Approach as a Solution to Complex Business Problems

Due to changing market conditions, customer needs and environmental influences, organisations are facing increased pressure to transform in today’s world. The consequences of the COVID-19 pandemic have made many organisations aware of the importance of digital transformation in order to remain successful even in times of disruptive change. However, the pandemic has also shown how connected and complex the world is. The aim of this work is to answer the question of what a possible systematic method for dealing with complex business problems might look like. Dealing with complexity is a critical factor for any organisation and determines how resilient it can position itself in the face of dynamic environmental influences. To answer the research question, a prototype was first developed based on Niklas Luhmann’s systems theory, from which a systematic method was derived. The prototypes were then further developed and validated in qualitative expert interviews. A systems theory approach was chosen because it claims to construct a holistic picture of the company as a social system and thus to capture the interactions of all system components. Qualitative interviews based on a semi-standardised approach were used to test the prototypes for comprehensibility, applicability, and validity. German experts in the fields of system technology, change management and digital transformation were interviewed.

Digital transformation technologies for conveyor belts predictive maintenance: a review

The availability of condition-monitoring data has increased due to internet of things (IoT) technologies, providing information on various parameters like vibration, temperature, current, and voltage. Cloud computing and big data facilitate the prevention of failures and estimation of remaining useful life through advanced mathematical models and artificial intelligence (AI) techniques. These enable prompt and suitable maintenance actions. This article conducts a systematic review of digital transformation technologies, including cloud computing, edge computing, AI, machine learning (ML), and TinyML, in predictive maintenance for conveyor belt systems. This article reviews how these digital transformation technologies improve predictive maintenance strategies for conveyor belts. The systematic review summarizes the results and challenges of various methodologies used in conveyor belt systems and suggests areas for further research. This paper aimed to serve as a useful resource for researchers, practitioners, and industry professionals seeking insights into current predictive maintenance technologies for conveyor belt systems. The takeaways of the review are expected to ignite discussion on efficient and proactive maintenance strategies and promote the development of innovative solutions for ensuring the reliability and longevity of conveyor belt systems in the digital era.

Design of a turbo-based deep semantic autoencoder for marine Internet of Things

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.

Support vector machine based on the quadratic unconstrained binary optimization model

Abstract Support vector machine (SVM) is a powerful supervised machine learning model that is often used in binary classification algorithms. As Moore’s Law approaches its theoretical limits and the demand for machine learning to handle large-scale, high-dimensional data analysis intensifies, the necessity of adopting non-traditional computational approaches becomes evident. Quantum computing, in particular, emerges as a vital solution for the effective training of SVM models, providing capabilities beyond those of classical computing systems. To solve the above problems, a QUBO (quadratic unconstrained binary optimization) model is proposed to transform the SVM machine learning model into a quadratic unconstrained binary optimization problem so that they can be effectively trained on the D-Wave platform using adiabatic quantum computer. The results show that the QUBO model can transform the SVM model into a simple quadratic programming problem, which makes it suitable for adiabatic quantum computer processing. When processing large-scale and high-dimensional data, this transformation shows a natural advantage and significantly improves computational efficiency. The application potential of this transformation technology is huge in the medical field.

MSRR-T2I: Multi-Scale Reverse Residual for Text-to-Image

Abstract Text-generated image is a very challenging task in the artificial intelligence and machine learning. To address the problems of low quality and no-diversity in images generated by conventional text-generated approaches, a multi-scale reverse residual text-generated image network (MSRR-T2I) is proposed in this paper. Firstly, the multi-scale residuals inverse structure is embedded into the fine image generation module to resolve the issue of loss of feature information when original residuals are reduced. Then, the information content of the feature map is extended, and the features of different resolutions are captured by multi-scale convolution. Finally, channel transformation technology is introduced to achieve cross-group information fusion and enhance image diversity. The test results of common CUB and COCO data sets indicate that compared with the original model ATTNGAN, the IS of MSRR-T2I increases by 8.7% and 8.4%, and the FID increases by 24.8% and 17.0%, respectively. Therefore, the proposed method is confirmed to be effective.

Three-dimensional bioprinted materials in alginate-hyaluronic acid complex based hydrogel based bio-ink as absorbents for heavy metal ions removal

Major environmental and health effects arise from the presence of heavy metals in water, including lead (Pb²⁺), mercury (Hg²⁺), chromium (Cr⁶⁺), cadmium (Cd²⁺), and arsenic (As³⁺), which contribute significantly to water pollution and pose severe risks to ecosystems and human health. This study demonstrates the fabrication of 3D printed self-regenerative functional living materials, a fungi, by using mixture of sodium alginate and hyaluronic acid hydrogel as absorbents to remove the heavy metals from the environment. Bioprinting has emerged as a transformative technology for fabricating complex biological constructs. The bioprinting experiments, conducted with optimized parameters, revealed the viscoelastic behavior of the hydrogel, suitable for 3D bioprinting applications. MicroCT analysis indicated that the hydrogel's porosity and structural properties support fungal growth in a controlled three-dimensional setting. Microscopic analyses, including phase contrast microscopy, FESEM, and HRTEM, provided insights into the cellular morphology of the bioprinted constructs. Cell viability was assessed using flow cytometry with FDA and PI staining, showing a significant population of live cells, affirming the biocompatibility of the hydrogel. Additionally, the study explored the hydrogel's potential for heavy metal removal from water samples. Multi-element standard solutions containing Copper(Cu²⁺), Cadmium(Cd²⁺), Nickel(Ni²⁺), Cobalt(Co²⁺), and Ferrous(Fe³⁺) ions at different concentrations were incubated with hydrogel patches embedded with Aspergillus flavus. Atomic absorption spectrophotometry analysis showed rapid initial removal rates of metal ions, with maximum removal efficiency achieved around the 12th hour. Comparisons with a control hydrogel without the fungal strain demonstrated significantly enhanced removal efficiency due to the presence of Aspergillus flavus. This study highlights the dual functionality of sodium alginate with hyaluronic acid -based hydrogels for bioprinting fungi and for environmental remediation of heavy metals, offering promising applications in environmental cleanup.

BEYOND THE HORIZON: FUTURE TECHNOLOGIES TRANSFORMING MARITIME NAVIGATION

The shipping industry is constantly evolving to meet today's demands and challenges. This article explores how innovations and trends in shipping are influencing the efficiency, sustainability and competitiveness of this vital sector of the global economy. Analysing technological transformations and emerging practices, the paper reveals how new technologies and cross-company collaborations are shaping the future of the maritime industry. The example of the BYD Explorer fleet, the first Ro-Ro vessel specialised in transporting electric cars, underlines the adaptability and innovation in response to the current needs of the automotive industry and sustainability requirements. At the same time, the collaboration between MOL and Bearing, which has led to the development of advanced solutions based on artificial intelligence, demonstrates the positive impact of innovation in optimising maritime operations.

Embracing AI in Change Management

Abstract: This article discusses how organizations can leverage artificial intelligence (AI) to support rather than hinder change management efforts amid technological transformation. It first establishes a research foundation on common sources of resistance to change, such as job insecurity, lack of understanding, and lack of involvement. The article then presents examples of how AI can augment traditional change management approaches to systematically address each resistance factor. Specifically, AI enhances two-way communication through personalized outreach and question answering. It also enables immersive, dynamic training solutions for ubiquitous, experiential skill development. AI fosters wider participation and feedback while monitoring employee well-being and performance. By addressing causes of resistance at each stage, AI strengthens critical aspects of communication, education, participation, and support to build understanding of changes rather than apathy. The article argues leaders who view AI as an ally in change management will empower their workforce through disruption.

Contemporary discourse on neocolonialism: an analytical review of research

The article provides an overview of contemporary research on neocolonialism. The work examines the history of the term "neocolonialism", as well as the main features of neocolonialism in the 21st century, including: the transformation of the nature, forms and methods of colonial influence, and changes in its scale. The main reasons for the interest in the problems of neocolonial studies are outlined, including a general change in research optics, the emergence of new forms of colonial dependence, etc. The prerequisites for the emergence of neocolonial studies in sociological science are indicated. The analysis of foreign studies demonstrates, on the one hand, the preservation of interest in traditional topics related to the colonial legacy in African countries, since they are still in the most vulnerable position compared to other former colonies, and on the other hand, an exit to a broader topic related to cultural neocolonialism in the context of globalisation, as well as the transformation of technology into an instrument of colonial influence. Modern domestic studies of neocolonialism have their own specifics, expressed, firstly, in the dominance of the economic interpretation of neocolonialism, secondly, they are often devoted to the study of the relationship between neocolonialism and globalisation and capitalism, thirdly, in the works of domestic authors special attention is paid to the position of Russia in the system of global neocolonialism. Based on the analysis of scientific discourse, the authors developed a typology of neocolonialism forms, that divides all forms into two large groups - traditional and new. Traditional forms include, first of all, economic and political neocolonialism. New forms include cultural and educational neocolonialism. The intensification of the digitalisation process and the increase in environmental risks at the beginning of the 21st century allow us to say that such forms of neocolonialism as environmental and digital are becoming increasingly important. Particular attention in the article is paid to environmental neocolonialism as a new, special form. The reasons for the growing interest in it and its impact on the political and economic spheres are also considered. The thesis of turning the green agenda into a new form of ideology, with the help of which the G7 countries implement policies that are beneficial to them, is discussed. The authors conclude that it is necessary to identify specific interests and values behind modern environmental initiatives.

Use of Building Information Modeling (BIM) to Improve Construction Management in the USA

The construction industry in the United States faces ongoing challenges related to cost overruns, project delays, and inefficiencies in project management. Building Information Modeling (BIM) has emerged as a transformative technology with the potential to address these issues by enhancing the precision, collaboration, and decision-making processes in construction management. This paper provides a comprehensive review of the application of BIM in the U.S. construction sector, with a focus on its impact on improving construction management practices. The review explores the specific ways in which BIM contributes to project success, including its ability to streamline workflows, reduce errors, and facilitate real-time collaboration among stakeholders. Through an in-depth analysis of current literature, this paper highlights the specialized functions of BIM that are particularly beneficial for construction management in the U.S., such as clash detection, 4D scheduling, and cost estimation. The paper also examines case studies that demonstrate the successful integration of BIM in large-scale projects across the country, showcasing tangible improvements in project outcomes, including reduced project timelines and enhanced cost efficiency. Additionally, the paper delves into the challenges associated with BIM adoption in the U.S., including the need for skilled personnel, the integration of BIM with existing project management systems, and the legal and contractual implications of BIM-based collaboration. These challenges are discussed alongside strategies for overcoming them, emphasizing the importance of training, standardization, and cross-disciplinary collaboration. The findings of this review suggest that the widespread adoption of BIM in the U.S. construction industry holds significant promise for enhancing project management efficiency, improving stakeholder communication, and ultimately contributing to more successful project outcomes. The paper concludes by proposing best practices for BIM implementation in construction management, aiming to guide industry professionals in leveraging this technology to its fullest potential.