Integration Of Smart Technologies Research Articles

Innovative Transitions: Exploring Demand for Smart City Development in Novi Sad as a European Capital of Culture

This study investigates the factors influencing the acceptance and implementation of smart city solutions, with a particular focus on smart mobility and digital services in Novi Sad, one of the leading urban centers in Serbia. Employing a quantitative methodology, the research encompasses citizens’ perceptions of the benefits of smart technologies, their level of awareness regarding smart solutions, the degree of engagement in using digital services, and their interest in smart mobility. The results indicate that these factors are crucial for the successful integration of smart technologies. Notably, awareness of smart city initiatives and the perceived benefits, such as improved mobility, reduced traffic congestion, increased energy efficiency, and enhanced quality of life, are highlighted as key prerequisites for the adoption of these solutions. Novi Sad, as the European Capital of Culture in 2022, presents a unique opportunity for the implementation of these technologies. Our findings point to the need for strategic campaigns aimed at educating and raising public awareness. The practical implications of this study could contribute to shaping policies that encourage the development of smart cities, not only in Novi Sad but also in other urban areas across Serbia and the region. This study confirms the importance of citizen engagement and technological literacy in the transformation of urban environments through smart solutions, underscoring the potential of these technologies to improve everyday life and achieve sustainable urban development.

Smart Technology Integration: SHM and BIM for Preventing Building Collapses

Smart Technology Integration: SHM and BIM for Preventing Building Collapses

PERENCANAAN PEMBELAJARAN PAI BERBASIS HIGHER ORDER THINKING SKILLS (HOTS) DI ERA SOCIETY 5.0: STRATEGI DAN IMPLEMENTASI

The Society 5.0 era, which is characterized by the integration of smart technology in everyday life, demands significant changes in educational practices, including Islamic Religious Education (PAI). To answer this challenge, PAI needs to integrate Higher Order Thinking Skills (HOTS) into its curriculum, with the aim of producing students who not only understand religious teachings but also have critical, creative and solution thinking skills. Strategies such as a contextual learning approach that links Islamic teachings with social issues and the use of digital technology can be used to strengthen the learning process.Research shows that implementing HOTS has a positive impact on students' critical thinking and problem solving abilities. However, challenges remain, including limited teacher knowledge about the HOTS method and inadequate technological facilities. Therefore, continuous teacher training and support from technological facilities are needed to ensure that technology-based learning can run effectively

The interplay between smart technologies, business sustainability, and environmental sustainability: An empirical analysis of SMEs

AbstractThis study examines the interplay between smart technologies, business sustainability, and environmental sustainability within the context of small and medium‐sized enterprises (SMEs). By employing structural equation modeling (SEM), the study evaluates the direct and indirect effects of smart technology integration on business and environmental sustainability outcomes. The results indicate that while smart technologies do not directly influence environmental sustainability, they significantly enhance business sustainability, which in turn positively affects environmental sustainability. This implies a mediated relationship, in which the strategic implementation of smart technologies is crucial for achieving environmental goals. The implications extend to the necessity of a strategic, holistic approach to sustainability in SMEs, emphasizing the intricate roles played by smart technologies in this sector.

Safety 4.0: Harnessing computer vision for advanced industrial protection

In the pursuit of enhanced productivity, reduced costs, and minimized lead times, manufacturers are transitioning from traditional systems to autonomous systems. This shift, driven by the emergence of smart manufacturing and technological advancements such as robotics, collaborative robots (Cobots), automation, and digitalization, necessitates a parallel evolution in safety protocols—termed Safety 4.0—to mitigate the risks associated with such dynamic environments. The integration of smart technologies within manufacturing significantly transforms traditional workflows and intensifies the need for comprehensive safety training and guidelines. Innovations like smart personal protective equipment (PPE) and wearable sensors are pivotal in this transition, yet they often prove financially burdensome for manufacturers due to high costs and the scale of workforce deployment. Moreover, the effective use of these technologies requires continuous monitoring and data analysis, further straining resources. To address these challenges, this paper proposes the adoption of computer vision technology to enhance safety measures within manufacturing facilities, focusing on human and PPE detection. It details a holistic methodology encompassing data collection, preprocessing, training, and execution. The discussion extends to the implementation framework of this technology, emphasizing its role in enabling autonomous decision-making—a crucial step beyond mere detection. Furthermore, the paper explores the utilization of the accumulated data to develop immersive training modules employing Mixed Reality, thereby reinforcing safety protocols and fostering an environment of continuous learning and adaptation. This approach not only contributes to safeguarding personnel but also aligns with the financial and reputational interests of forward-thinking manufacturers.

Digitalization and Decarbonization of Sri Lanka's Electric Power System: A Way Forward for Sustainability

The global energy landscape is undergoing a transformative shift driven by the dual imperatives of digitalization and decarbonization, essential for creating sustainable and resilient power systems (Lichtenthaler, 2021, [1]). This transformation is particularly critical for developing nations like Sri Lanka, where the integration of smart technologies and renewable energy sources can significantly enhance system efficiency and contribute to a robust response to climate change challenges (Lichtenthaler, 2021, [1]). This paper examines the potential of leveraging advanced digital technologies—such as artificial intelligence, big data analytics, and the Internet of Things—to optimize energy management and facilitate a swift transition toward a low-emission economy in Sri Lanka, while addressing the socio-economic implications of this transition. Furthermore, this study highlights the importance of fostering a circular economy through these technological advancements, emphasizing that innovative solutions can not only improve energy efficiency but also contribute to broader sustainability goals, ensuring equitable access to resources and combating the adverse effects of climate change. Through this lens, the investigation underscores the necessity of a holistic approach that integrates digital transformation with sustainable practices, enabling the decoupling of economic activity from resource depletion and environmental degradation, thereby fostering a resilient energy future for Sri Lanka.

Applications of Machine Learning: Classifying Wheat Seeds through Computer Neural Networks

In the past few decades, the incompetent processes of traditional mechanical seed sorting have given rise to new, innovative methods, along with the integration of smart technology in agriculture. The present study created a multi-classification machine learning model that classifies wheat seeds based on given characteristics by giving highly accurate predictions for future samples based on given data. Due to various uses of different types of seeds, effective sorting is an integral preparatory stage for food processing. The dataset used in the study included seven features (directly measured characteristics of seeds), three labels (types of seeds), and around two hundred examples. The seven features include area, perimeter, compactness, kernel length, kernel width, asymmetry coefficient, and kernel groove. The first model was created through neural networks trained on all seven characteristics, reaching an overall accuracy of 0.94. Implementing a decision tree method, feature selection was applied to the data, which distinguished kernel groove, kernel width, and asymmetry coefficient as the three most important features. A new model was constructed with only the three selected features, which reduced the risk of overfitting, eliminated noise, and yielded a higher accuracy (0.96) than the previous model. Both multi-classification models used the softmax activation function and results were compared; feature selection noticeably reduced any inaccuracies, including categorical cross entropy loss. The main incentive of the study was to develop an effective model to sort wheat seeds (to replace traditional mechanical sorting) and provide reasonable recommendations for practical use through an analysis of feature selection.

Research on Urban Renewal Design in the Context of the Future Community Concept

As urbanization accelerates, traditional cities face numerous challenges such as aging infrastructure, limited community functions, and inadequate environmental sustainability. The concept of the "future community," as an innovative approach to urban development, emphasizes the application of smart technologies, enhanced community participation, eco-friendly designs, and diversified economic growth, providing a new direction for urban renewal. This study aims to explore the application of the future community concept in urban renewal design through a combination of literature review, case studies, and data analysis, systematically analyzing the core characteristics of future communities and their impact on urban renewal. The research selects typical urban renewal cases from both domestic and international contexts, constructs a relationship model between future communities and urban renewal, and proposes a series of design strategies such as participatory design, smart technology integration, sustainable green design, and mixed-use development. Comparative analysis reveals that the future community concept significantly improves social inclusiveness, technological application levels, and environmental sustainability, while also boosting economic vitality. The use of data tables further validates the effectiveness of these design strategies, providing empirical support for urban planning and design. The study concludes that while the future community concept offers important guidance for urban renewal, challenges such as technological, financial, and policy issues must still be overcome during implementation. Finally, the paper offers relevant policy recommendations and future research directions to provide theoretical and practical references for building more livable, intelligent, and sustainable urban communities.

COMPARATIVE ANALYSIS OF SMART ECONOMY DEVELOPMENT IN SELECTED COUNTRIES OF SOUTHEAST ASIA

Research subject. The development of the global economy indicates the formation of a new trend in the formation of the smart economy, which currently covers both developed and developing countries. According to the analysis, developed countries are leaders in economic development precisely because of the development of technologies that are the basis for the formation of the smart economy and the smart intellectualisation of economic activity. The smart economy in Asia is actively developing due to the intensive introduction of the latest technologies, innovative management approaches, and increased resource efficiency. Asian countries such as China, Japan, South Korea, Singapore, and India are leaders in the development of the smart economy. At the same time, Asian countries have been demonstrating one of the highest rates of economic development since the beginning of the XXI century, which is primarily due to the active technologisation of production and the spread of communication technologies in all areas of economic activity. Several technology centres are emerging in Asia, concentrating technology companies and creating a favourable environment for the development of the smart economy. Methodology. The article conducts a comparative analysis of the factors contributing to the development of the smart economy in individual Asian countries, identifies the most influential factors and the degree of their impact on each country. The purpose of this study is to identify the factors influencing the development of the smart economy and compare their impact in the economies of China, India and Singapore. To achieve this goal, the following research objectives are envisaged: to identify factors that have a significant impact on the development of the smart economy in different countries; to assess the impact of individual factors on the development of the smart economy in China, India and Singapore; to compare the impact of individual factors of the smart economy on the economies of these countries; and to assess the models of smart economy development in China, India and Singapore. Conclusion. The smart economy in Asia is characterised by unique features that reflect the cultural, technological and economic conditions of the region. The key features of the Asian smart economy include a high level of digitisation and technological integration, active development of smart cities, financial technologies (FinTech), a focus on automation and robotics, innovation hubs and support for start-ups, environmental sustainability and green energy, and the digitalisation of governance and public services. Asian countries are also distinguished by the integration of smart technologies into the everyday life of citizens and the support of innovation ecosystems at the state level.

NARCISSISM AND ENVY'S EFFECT ON TOURISTS SHARING SELFIE IN SOCIAL COMPARISON THEORY APPROACH

Tourism, as a social endeavor, has been significantly influenced by advancements in technology and communication tools, thereby facilitating interpersonal interactions as a social activity. The widespread integration of smart technologies and the continually evolving social media platforms have fundamentally altered the rapid dissemination of tourist experiences. Consequently, this research aims to examine the tendency to share selfies on social media platforms within the framework of social comparison theory. Data were collected from 533 participants through an online survey and were subjected to Exploratory Factor Analysis, Confirmatory Factor Analysis, and Path Analysis within the framework of Structural Equation Modeling. The analyses revealed that narcissism and envy positively impact tourists' tendencies to share selfies on social media platforms. Based on these findings, the study offers recommendations for developing effective strategies for tourism stakeholders.

Unveiling temporal and spatial research trends in precision agriculture: A BERTopic text mining approach

This study leverages the BERTopic algorithm to analyze the evolution of research within precision agriculture, identifying 37 distinct topics categorized into eight subfields: Data Analysis, IoT, UAVs, Soil and Water Management, Crop and Pest Management, Livestock, Sustainable Agriculture, and Technology Innovation. By employing BERTopic, based on a transformer architecture, this research enhances topic refinement and diversity, distinguishing it from traditional reviews. The findings highlight a significant shift towards IoT innovations, such as security and privacy, reflecting the integration of smart technologies with traditional agricultural practices. Notably, this study introduces a comprehensive popularity index that integrates trend intensity with topic proportion, providing nuanced insights into topic dynamics across countries and journals. The analysis shows that regions with robust research and development, such as the USA and Germany, are advancing in technologies like Machine Learning and IoT, while the diversity in research topics, assessed through information entropy, indicates a varied global research scope. These insights assist scholars and research institutions in selecting research directions and provide newcomers with an understanding of the field's dynamics.

Efektivitas Pelatihan Keselamatan Kerja di Konstruksi Dan Peran Manajemen dalam Meningkatkan Kepatuhan K3 ; Literatur Review

This study aims to evaluate the effectiveness of safety training programs in the construction sector and the role of management in enhancing compliance with Occupational Health and Safety (OHS) standards through a literature review. The research employs a literature study method, gathering data from 20 articles indexed in Sinta and Scopus between 2018 and 2023. Conducted over two months (June and July 2024), the study identifies the most effective training methods for improving OHS compliance. It also highlights the managerial role in ensuring that safety training is well adopted and implemented by workers on-site. The findings reveal that from 2018 to 2023, the implementation of OHS across various sectors has shown significant progress, particularly with the adoption of advanced technologies like Virtual Reality (VR) and the Internet of Things (IoT). However, gaps in understanding and applying OHS, especially in SMEs and construction, persist, with a need for increased awareness and comprehension. A more proactive managerial role and the integration of smart technologies are crucial for enhancing the effectiveness of OHS training and worker safety. Future research should explore the broader application of VR and IoT in various sectors and assess the long-term effectiveness of OHS training programs supported by these technologies. Further studies are needed to address the challenges in implementing OHS in SMEs and construction to achieve higher safety standards

Preparation and performance study of sodium alginate/bamboo fiber/gelatin ionic conductive self-healing hydrogel

This study has been successfully developed the Sodium alginate/Bamboo fiber /Gelatin（SA/BF/Gel）composite conductive hydrogel with adhesive and self-healing properties. Through in-depth research, the influence of Gel content on the tensile, adhesive, self-healing properties, and conductivity of the SA/BF/Gel composite conductive hydrogel was discussed. The sensing performance and sensing mechanism of the material were also investigated, along with a preliminary exploration of its potential applications. An attempt was made to apply the SA/BF/Gel composite conductive hydrogel to 3D printing technology, establishing a connection between the rheological properties of the hydrogel and its printing structure. The addition of Gel significantly improved the flexibility of the hydrogel, with a conductivity of up to 3.12 S/m at a Gel content of 1.5 %. When employed as a sensor, the material exhibited high sensitivity (GF = 2.21) and excellent cyclic stability, rendering it suitable for a wide range of applications in real-time monitoring of bending movements of fingers and wrists, as well as dynamic contact and variations in contact forces on the hydrogel surface. The SA/BF/Gel composite conductive hydrogel has the potential to be utilized in a multitude of applications, including the development of smart wearable devices, the monitoring of individual human beings, and the integration of human beings and machines. Furthermore, the research findings associated with this hydrogel will provide a strong foundation for the advancement of materials science and the integration of smart technologies.

Longitudinal Coadaptation of Older Adults With Wearables and Voice-Activated Virtual Assistants: Scoping Review.

The integration of smart technologies, including wearables and voice-activated devices, is increasingly recognized for enhancing the independence and well-being of older adults. However, the long-term dynamics of their use and the coadaptation process with older adults remain poorly understood. This scoping review explores how interactions between older adults and smart technologies evolve over time to improve both user experience and technology utility. This review synthesizes existing research on the coadaptation between older adults and smart technologies, focusing on longitudinal changes in use patterns, the effectiveness of technological adaptations, and the implications for future technology development and deployment to improve user experiences. Following the Joanna Briggs Institute Reviewer's Manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, this scoping review examined peer-reviewed papers from databases including Ovid MEDLINE, Ovid Embase, PEDro, Ovid PsycINFO, and EBSCO CINAHL from the year 2000 to August 28, 2023, and included forward and backward searches. The search was updated on March 1, 2024. Empirical studies were included if they involved (1) individuals aged 55 years or older living independently and (2) focused on interactions and adaptations between older adults and wearables and voice-activated virtual assistants in interventions for a minimum period of 8 weeks. Data extraction was informed by the selection and optimization with compensation framework and the sex- and gender-based analysis plus theoretical framework and used a directed content analysis approach. The search yielded 16,143 papers. Following title and abstract screening and a full-text review, 5 papers met the inclusion criteria. Study populations were mostly female participants and aged 73-83 years from the United States and engaged with voice-activated virtual assistants accessed through smart speakers and wearables. Users frequently used simple commands related to music and weather, integrating devices into daily routines. However, communication barriers often led to frustration due to devices' inability to recognize cues or provide personalized responses. The findings suggest that while older adults can integrate smart technologies into their lives, a lack of customization and user-friendly interfaces hinder long-term adoption and satisfaction. The studies highlight the need for technology to be further developed so they can better meet this demographic's evolving needs and call for research addressing small sample sizes and limited diversity. Our findings highlight a critical need for continued research into the dynamic and reciprocal relationship between smart technologies and older adults over time. Future studies should focus on more diverse populations and extend monitoring periods to provide deeper insights into the coadaptation process. Insights gained from this review are vital for informing the development of more intuitive, user-centric smart technology solutions to better support the aging population in maintaining independence and enhancing their quality of life. RR2-10.2196/51129.

Is smart city low-carbon? Evidence from China

Smart cities were originally conceived to address a myriad of urban challenges arising from rapid urbanization, including energy scarcity, congestion, and environmental degradation. The Chinese government has made substantial efforts to advance smart city initiatives. However, the extent to which the integration of smart technologies contributes to urban sustainability, especially within a high-carbon urbanization paradigm, poses a critical question in light of escalating extreme weather events and worsening global challenges. Urgency is underscored in prioritizing low-carbon strategies within smart city frameworks. This paper presents a Multicriteria Decision Making Network (MCDN) approach to assess and rank the low-carbon levels (LCL) of 36 pilot smart cities in China. Findings reveal that overall LCL among these cities remains relatively modest, with significant disparities attributed to varying economic, social, institutional, cultural, and environmental contexts. The study also delves into the nexus between urban intelligence and LCL, highlighting a discernible positive correlation between a city's smartness and its low-carbon profile. Moreover, empirical evidence suggests that advancements in smart technologies are conducive, albeit to varying degrees, to enhancing urban LCL. In light of these findings, recommendations are made to fortify economic and social advancement, bolster management practices, and foster multi-stakeholder collaboration to propel the coordinated development of smart and low-carbon initiatives in China.

Elements of Smart Leadership Approaches for Smart City Development

This article explores how smart leadership contributes to developing inclusive, equitable, and sustainable smart cities amidst the backdrop of rapid urbanization and technological advancement. Smart leadership, as articulated in this article, embodies a holistic and forward-looking approach that prioritizes the well-being and inclusion of residents as a primary concern. In line with this approach, technological innovation is harmoniously embodied alongside an unwavering commitment to social justice, sustainability, and long-term planning, enabling cities that are not only technologically advanced, but also more sustainable, equitable and enriched for all residents. Central to this visionary leadership is a profound recognition of equality and representation in city governance. The principles of inclusive leadership require a leadership structure that reflects the diversity of the population, ensuring that the voices of all communities are integral to decision-making processes. Smart leaders actively advocate for the problems and aspirations of each segment of society, promoting a sense of belonging and empowerment among residents, thereby strengthening the city’s commitment to the principles of social cohesion and equal opportunities. In addition, sound leadership recognizes the key challenge of the digital divide, which can exacerbate disparities in urban landscapes. This article highlights the fundamental importance of digital access in ensuring all residents’ full participation in modern society, economics, and governance. Smart leaders introduce policies and initiatives to bridge the digital divide and ensure equal access to digital tools and information for every resident, regardless of socioeconomic background. This proactive approach promotes digital inclusion and lays the foundation for seamless smart technology integration in the city, ultimately improving its residents’ overall quality of life. Through a systematic analysis of articles, this inquiry observes the metamorphosis of cities into centers of advanced technological implementation and economic dynamism. The article proposes the author’s Smart City Leadership Activity Model, which contains several key principles of smart leadership in the context of equitable, sensible, and strategic smart city growth, development, and innovation.

Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring.

Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodic blood pressure monitoring and prescribing antihypertensive drugs. Smart technology integration in healthcare offers promising results in optimizing the diagnosis and treatment of various conditions. We investigate its role in improving hypertension diagnosis and treatment effectiveness using machine learning algorithms for early and accurate detection. Intelligent models trained on diverse datasets (encompassing physiological parameters, lifestyle factors, and genetic information) to detect subtle hypertension risk patterns. Adaptive algorithms analyze patient-specific data, optimizing treatment plans based on medication responses and lifestyle habits. This personalized approach ensures effective, minimally invasive interventions tailored to each patient. Wearables and smart sensors provide real-time health insights for proactive treatment adjustments and early complication detection.

Enhancing Najran’s sustainable smart city development in the face of urbanization challenges in Saudi- Arabia

ABSTRACT This research offers valuable perspectives on the nexus between urbanization and the smart environment within the Najran city, Saudi Arabian context. Najran City, with its diverse districts and evolving urban landscape, is at the forefront of adopting advanced technologies and participatory governance models to create a resilient, environmentally conscious, and sustainable urban environment, likewise positioning itself as a model of smart urban development but is also fostering an innovation-driven community equipped to address the difficulties and exploit the prospects given by the 21st century. The study systematically analyzes the multifaceted factors of smart city variables in Saudi Arabia and their direct impact on urban sustainability through extensive quantitative investigations. This study reveals the positive correlations between smart city attributes, on the attainment of urban sustainability. In order words, the smart city’s attributes such as IoT (Internet of Things) devices, smart infrastructure, and data-driven solutions are linked to the creation of a resilient urban environment in the face of urbanization and sustainability challenges. On the other hand, the study noted that the integration of smart technologies might lead to unintended consequences in urban planning. For example, an overemphasis on technology may overshadow human-centric approaches, which might potentially affect the community well-being and social cohesion. Study’s insights provide policymakers, urban planners, and players with an in-depth knowledge of the pathways to harness smart city attributes for sustainable Najran urban development. The implications of this research extend beyond the borders of Saudi Arabia, serving as a valuable reference for regions worldwide grappling with similar urbanization and sustainability challenges in the 21st century. By harnessing the potential of smart cities, Saudi Arabia and other nations can pave the way for greener, more resilient urban futures.

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.

Systematic Review of Models Usedto Handle Class Imbalance in Anomaly Detection for Energy Consumption

The widespread integration of Smart technologies into energy consumption systems has brought about a transformative shift in monitoring and managing electricity usage. The imbalanced nature of anomaly data often results in suboptimal performance in detecting rare anomalies. This literature review analyzes models designed to address this challenge. The methodology involves a systematic literature review based on the five-step framework proposed by Khan, encompassing framing research questions, identifying relevant literature, assessing article quality, conducting a critical review, and interpreting results. The findings show that classical machine learning models like Support Vector Machines (SVM) and Random Forests (RF) are commonly used. In conclusion, classical machine learning models like SVM and RF struggle to recognize rare anomalies, while deep learning models, notably Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), show promise for automatically learning elaborate representations and improving performance while dealing with class imbalance.