Hasil untuk "Technological innovations. Automation"

Lu Xu, Yulin Zhang, Mengjiao Liu et al.

The construction industry faces persistent challenges, including labor shortages and safety hazards, while traditional construction methods are increasingly strained by the complexity and sustainability demands of modern projects. The integration of robotics shows significant potential for mitigating labor shortages and enhancing safety on construction sites. The current adoption of robotics technologies is driven by both the maturity of robotics technology and the potential for cost reduction compared with manual labor. This review synthesizes recent advancements and trends in construction robotics through a bibliometric analysis of 212 publications indexed in Web of Science from 2002 to 2024. Key findings indicate a 320% increase in research output from 2015 to 2022, with dominant clusters focusing on autonomous navigation, human–robot collaboration, and sustainability-driven automation. Geographically, China and the United States lead in number of publications, with 67 and 65 articles, respectively; however, cross-border collaborations remain sparse, constituting fewer than 5% of co-authored papers. Keyword co-occurrence analysis reveals evolving priorities, including artificial intelligence (AI)-driven adaptive control, modular prefabrication, and the ethical implications of automation. Despite technological advancements, critical gaps remain in terms of interoperability, workforce retraining, and regulatory frameworks. This study emphasizes the need for interdisciplinary integration, standardized protocols, and policy alignment to bridge the divide between academic innovation and industry adoption, ultimately facilitating the global transition toward Construction 4.0.

B. D. Wichtmann, Daniel Paech, Oleg S. Pianykh et al.

Radiology has evolved from the pioneering days of X-ray imaging to a field rich in advanced technologies on the cusp of a transformative future driven by artificial intelligence (AI). As imaging workloads grow in volume and complexity, and economic as well as environmental pressures intensify, visionary leadership is needed to navigate the unprecedented challenges and opportunities ahead. Leveraging its strengths in automation, accuracy and objectivity, AI will profoundly impact all aspects of radiology practice—from workflow management, to imaging, diagnostics, reporting and data-driven analytics—freeing radiologists to focus on value-driven tasks that improve patient care. However, successful AI integration requires strong leadership and robust governance structures to oversee algorithm evaluation, deployment, and ongoing maintenance, steering the transition from static to continuous learning systems. The vision of a “diagnostic cockpit” that integrates multidimensional data for quantitative precision diagnoses depends on visionary leadership that fosters innovation and interdisciplinary collaboration. Through administrative automation, precision medicine, and predictive analytics, AI can enhance operational efficiency, reduce administrative burden, and optimize resource allocation, leading to substantial cost reductions. Leaders need to understand not only the technical aspects but also the complex human, administrative, and organizational challenges of AI’s implementation. Establishing sound governance and organizational frameworks will be essential to ensure ethical compliance and appropriate oversight of AI algorithms. As radiology advances toward this AI-driven future, leaders must cultivate an environment where technology enhances rather than replaces human skills, upholding an unwavering commitment to human-centered care. Their vision will define radiology’s pioneering role in AI-enabled healthcare transformation. QuestionArtificial intelligence (AI) will transform radiology, improving workflow efficiency, reducing administrative burden, and optimizing resource allocation to meet imaging workloads’ increasing complexity and volume. FindingsStrong leadership and governance ensure ethical deployment of AI, steering the transition from static to continuous learning systems while fostering interdisciplinary innovation and collaboration. Clinical relevanceVisionary leaders must harness AI to enhance, rather than replace, the role of professionals in radiology, advancing human-centered care while pioneering healthcare transformation.

R. Martins, J. V. Borsatto, Camila Will et al.

Miniaturized solid-based approaches have added an eco-friendly dimension to analytical procedures, establishing themselves as promising strategies for a wide range of applications. Among these, microextraction by packed sorbent (MEPS) stands out due to its ability to facilitate efficient sample interaction with a densely packed sorb ent phase within the microextraction system. MEPS offers several advantages, including preconcentration capabilities and the use of minimal sample and solvent volumes, making it an appealing choice for modern analytical workflows. Since the extraction efficiency is largely dictated by the sorbent phase, recent advancements in sorbent design have garnered considerable attention in the field of sample preparation. Innovations in sorbent phases have not only enhanced the MEPS efficiency but also enabled the development of semi- and fully automated systems, paving the way for high-throughput methodologies. These advancements have elevated MEPS beyond traditional offline miniaturized sample preparation methods, offering new opportunities for streamlined and scalable analyses. Therefore, this study provides a comprehensive overview of novel sorbent phases used in MEPS, with a particular focus on both bio-based and synthetic materials. Furthermore, it explores the semi- and fully automated aspects of MEPS, highlighting current trends, technological advancements, and future directions in this rapidly evolving field.

Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong et al.

Aim/objective This review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. It highlights current applications, challenges, ethical considerations, and the potential future developments of ML in nursing. Background With the advent of ML in healthcare, the nursing profession stands on the cusp of a transformative era. Despite the technological advancements, discussions on the utilization of ML in nursing, which are crucial for advancing the profession, are lacking. This review seeks to fill this gap by examining the balance between technological innovation and the human-centric nature of nursing. Design This narrative review employs a detailed search strategy across several databases, including PubMed, Embase, MEDLINE, Scopus, and Web of Science. It focuses on articles that were published from January 2019 to December 2023. Moreover, this review aims to illustrate the current use, challenges, and future potential of ML applications in nursing. Methods Inclusion criteria targeted articles that focus on ML application in nursing, challenges, ethical considerations, and future directions. Exclusion criteria omitted opinion pieces and nonrelevant studies. Articles were categorized into themes, such as patient care, nursing education, operational efficiency, ethical considerations, and future potential, thus facilitating a structured analysis. Results Findings demonstrate that ML has significantly enhanced patient monitoring, predictive analytics, and preventive care. For example, the COMPOSER deep learning model for early sepsis prediction was associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality and a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance. In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. Furthermore, ML contributes to operational efficiency through automated staffing optimization and administrative task automation, thus reducing nurse workload and enhancing patient care outcomes. However, key challenges include ethical considerations, such as data privacy, algorithmic bias, and patient autonomy, which necessitate ongoing research and regulatory oversight. Conclusions ML in nursing offers transformative potential across patient care, education, and operational efficiency, which is balanced by significant challenges and ethical considerations. Future directions include expanding clinical and community applications, integrating emerging technologies, and enhancing nursing education. Continuous research, ethical oversight, and interdisciplinary collaboration are essential for harnessing ML's full potential in nursing to ensure that its advancements improve patient outcomes and support nursing professionals without compromising core nursing values.

Kadar Nurjaman

The rapid advancement of technology has brought significant disruption to traditional Human Resource Management (HRM) practices, reshaping how organizations manage their workforce. This article provides a comprehensive literature review on the role of machine learning (ML) algorithms in strategic decision-making within HRM. By analyzing key functions such as recruitment, performance evaluation, and talent management, this review highlights the transformative potential of ML in enhancing HR processes. The various ML algorithms, including decision trees, neural networks, and support vector machines, identifying their strengths and limitations in addressing HR challenges. The paper discusses ethical concerns related to bias, data privacy, and the implications of automation on employee well-being. The findings suggest that while ML offers significant opportunities for innovation in HRM, careful consideration of ethical and strategic factors is crucial for its successful implementation. Future research directions and practical recommendations are also proposed to support the integration of ML in HR decision-making.

L. Hnatyshyn, O. Prokopyshyn, O. Maletska et al.

The study aimed to analyse the impact of digital innovations on accounting and the efficiency of the financial processes of enterprises. The methodology included an analysis of the financial performance of Kernel, one of the leaders in Ukraine’s agricultural sector. The key financial indicators of the company were compared before and after the introduction of digital innovations, such as the automation of accounting processes, the use of optical character recognition and the introduction of electronic consignment notes. The study analysed how modern accounting automation technologies, machine learning, data analytics, forecasting algorithms, artificial intelligence (AI) for anomaly detection, cloud technologies and blockchain have changed approaches to accounting, reducing costs, and increasing the accuracy and transparency of financial reporting. The study results demonstrated that the introduction of digital tools allowed Kernel to significantly improve its key economic indicators. Revenues grew from USD 2168.9 million in 2017 to USD 3581 million in 2024 (+49% compared to 2018). The company’s earnings before interest, taxes, depreciation and amortisation increased from USD 319.2 million to USD 381 million (+71%), and net profit in 2024 was USD 168 million. The use of electronic consignment notes ensured efficient management of logistics processes, minimised risks and improved product transportation management. The use of digital technologies has helped to increase the efficiency of operations, reduce costs and improve economic performance. The study established that automation of accounting operations through the introduction of digital platforms, such as the system of electronic consignment notes and integration of cloud technologies, has reduced the time for processing financial data, reduced the probability of errors and increased the accuracy of financial reporting. Recommendations for Ukrainian enterprises included the introduction of accounting process automation, which included the use of accounting software, including QuickBooks, as well as the integration of blockchain to increase the security and transparency of financial transactions. The study confirmed that digital innovations in accounting were not only a technological necessity, but also an important factor in the economic growth of enterprises, which reduced costs, improved management decisions, optimised budget control and increased the competitiveness of companies in the market

Francisco Gustavo Bautista Carrillo, Daniel Arias-Aranda

This study explores how the sequence and timing of Industry 4.0 technology adoption affect sustainable innovation in manufacturing firms. Using longitudinal data from the State Society of Industrial Participations, we track the adoption patterns of eight technologies, including industrial IoT, cloud computing, RFID, machine learning, robotics, additive manufacturing, autonomous robots, and generative AI. Sequence analysis reveals five distinct adoption profiles: data-centric foundations, automation pioneers, holistic integrators, cautious adopters, and product-centric innovators. Our results show that these adoption pathways differentially impact sustainability outcomes such as circular material innovation, energy transition, operational eco-efficiency, and emissions reduction. Mediation analysis indicates that data orchestration capabilities significantly enhance resource productivity in holistic integrators, generative design competencies accelerate biomaterial innovation in product-centric innovators, and cyber-physical integration reduces lifecycle emissions in automation pioneers. By highlighting how temporal complementarities among technologies shape sustainability performance, this research advances dynamic capabilities theory and emphasizes the path-dependent nature of sustainable innovation. The findings provide practical guidance for firms to align digital transformation with sustainability objectives and offer policymakers insights into designing timely support mechanisms for industrial transitions. This work bridges innovation timing with ecological modernization, contributing a new understanding of capability development for sustainable value creation.

Faisal Rahman, Amitabh Chandan

Introduction; The transition from Industry 4.0 to Industry 5.0 marks a significant evolution in the field of manufacturing and automation. Industry 4.0 introduced the integration of digital technologies, data analytics, and connectivity to optimize industrial processes and enable smart factories. However, Industry 5.0 takes this transformation to a new level by emphasizing the collaboration and interaction between humans and machines. Objective; While this industrial revolution holds immense potential for industrial growth, it also presents numerous challenges such as technical integration, human resource management, supply chain complexities, and data security concerns. However, the advent of Industry 5.0 promises to address these challenges head-on. Industry 5.0 introduces innovative technologies like predictive maintenance, hyper customization, cyber-physical cognitive systems, and collaborative robots. By prioritizing a human-centric approach, Industry 5.0 successfully overcomes the hurdles encountered in Industry 4.0, paving the way for a more efficient and collaborative future in the industry. Method; This paper investigates the evolution from Industry 4.0 to Industry 5.0, focusing on the distinctive features and advancements that characterize these industrial transformations. It examines the underlying principles, cutting-edge technologies, and their broader implications, emphasizing both the opportunities and challenges they present to industries and society. Result; It addresses the socio-economic ramifications of these advancements, including their potential to reshape manufacturing processes, improve efficiency, and foster sustainability. Conclusion; The paper provides valuable insights into the trajectory of industrial innovation. It highlights how these developments promise a transformative impact on the future of manufacturing, aligning technological progress with ethical considerations, social responsibility, and sustainable practices

Aleksandra Kuzior, Mariya Sira

Organizations implement intelligent automation across diverse operational contexts but often lack comprehensive frameworks for strategic decision-making and cross-domain integration. The existing literature frequently examines isolated applications with limited implementation guidelines addressing environmental interdependencies. This study conducts a systematic review of 69 publications (2019–2024) using thematic analysis to examine automation patterns across six environmental domains: social, economic, educational, scientific, technological, and ecological. The analysis identifies three implementation patterns: efficiency-focused domains (economic, technological) emphasizing operational optimization; capability-focused domains (social, educational) prioritizing human augmentation; innovation-focused domains (scientific, ecological) developing transformative applications. Cross-domain analysis reveals integration opportunities and sustainability considerations. The study proposes a strategic decision-making framework incorporating environmental assessment tools, quality enhancement mechanisms, and planning capabilities. This framework supports organizations in selecting domain-appropriate strategies while addressing sustainable transformation objectives. The research provides systematic environmental categorization of intelligent automation applications and offers implementation guidelines for practitioners pursuing coordinated digital transformation across organizational contexts.

Z. Ma, Kristina Lygnerud

District heating is critical for low-carbon urban energy systems, yet most networks remain centralized in both heat generation and data ownership, fossil-dependent, and poorly integrated with digital, customer-centric, and market-responsive solutions. While artificial intelligence (AI), the Internet of Things (IoT), and automation offer transformative opportunities, their adoption raises complex challenges related to business models, regulation, and consumer trust. This paper addresses the absence of a comprehensive synthesis linking technological innovation, business-model evolution, and institutional adaptation in the digital transformation of district heating. Using the PRISMA-ScR methodology, this review systematically analyzed 69 peer-reviewed studies published between 2006 and 2024 across four thematic domains: digital technologies and automation, business-model innovation, customer engagement and value creation, and challenges and implementation barriers. The results reveal that research overwhelmingly emphasizes technical optimization, such as AI-driven forecasting and IoT-based fault detection, whereas economic scalability, regulatory readiness, and user participation remain underexplored. Studies on business-model innovation highlight emerging approaches such as dynamic pricing, co-ownership, and sector coupling, yet few evaluate financial or policy feasibility. Evidence on customer engagement shows increasing attention to real-time data platforms and prosumer participation, but also persistent barriers related to privacy, digital literacy, and equity. The review develops a schematic conceptual framework illustrating the interactions among technology, business, and governance layers, demonstrating that successful digitalization depends on alignment between innovation capacity, market design, and institutional flexibility.

Shaila Kedla, Rohit J. Nair, N. Asha

The banking industry is experiencing a swift transformation fueled by technological advancements, including artificial intelligence (AI), blockchain, and automation, which are redefining financial services. The rise of the metaverse offers banks new avenues to boost customer engagement, provide immersive financial experiences, and create innovative digital products. This paper delves into the effects of technological innovation on banking, focusing on how the metaverse can be integrated into banking business models. It looks at the advantages of virtual banking branches, decentralized finance (DeFi), and tailored financial services, while also tackling significant challenges like cybersecurity threats, regulatory issues, and obstacles to consumer adoption. By analyzing existing literature and industry trends, this study underscores the metaverse's potential to transform banking, while stressing the importance of strong security measures and regulatory frameworks. The findings indicate that banks need to embrace a hybrid strategy that balances innovation with compliance and risk management to effectively navigate the changing digital landscape.

Jun Cui

The rapid advancement of the digital media industry has placed increasing emphasis on technological innovation as a key driver of competitiveness and growth. As artificial intelligence (AI) becomes more deeply integrated into digital media enterprises, human-AI interaction is emerging as a crucial factor influencing innovation processes. This study explores the role of human-AI interaction in driving technological innovation within digital media firms. Using a qualitative interview approach, data were collected from industry professionals, including AI engineers, product managers, and innovation strategists, across various digital media enterprises. The findings reveal that human-AI interaction enhances technological innovation in three key areas: data processing, creative content generation, and decision support. Specifically, AI-powered tools enable faster and more efficient data analysis, facilitate the development of novel media content, and assist decision-making processes through predictive analytics and automation. Additionally, this research contributes to the existing literature by addressing the gap in understanding how human-AI collaboration influences innovation outcomes in digital media enterprises. The study provides valuable insights for industry practitioners seeking to optimize AI-driven innovation strategies and offers a foundation for further academic inquiry into the evolving dynamics of human-AI synergy in creative industries.

Miko Mei Irwanto, Olyvia Rosalia, Firayani Firayani et al.

This study aims to analyze the impact of technological innovation on the productivity of the manufacturing industry. Technological innovation in this research includes the use of the Internet of Things (IoT), artificial intelligence (AI), and automation in production processes. The approach used is quantitative, with a linear regression analysis method to examine the relationship between the independent and dependent variables. This study involves respondents from various manufacturing sectors to gain a comprehensive understanding of the impact of technology on operational efficiency. The research findings indicate that technological innovation has a significant influence on productivity improvement, with each technological component contributing differently to production efficiency. The implications of these findings highlight the importance of digital transformation in the manufacturing industry to enhance competitiveness and operational effectiveness. Additionally, this study recommends workforce training and supportive policies for technology adoption to maximize the benefits of innovation in the industrial sector. This study also opens opportunities for further research by considering other factors such as organizational culture and supply chain integration in supporting technology implementation in the manufacturing industry.

Imtiaz U. Ahamed, Afsana Azmari

A crucial aspect of this research is determining the effectiveness of the tool developed for this study. This tool is built upon the understanding that technology continually evolves and significantly impacts higher education. It is believed that technology plays a vital role in how students learn in college today. This belief is supported by the numerous advancements that have been made in integrating technology into higher education across the United States. Additionally, technological advancements were incremental, but today’s rapid pace of innovation marks a departure from traditional patterns. Innovation is achieved by going through multiple failures, and our goal is to learn from history and provide a layout plan with interpretation that will serve as a disciplinary roadmap for the future of higher education in terms of technological advancements. The prospect of educational technology has undergone a remarkable transformation. We have moved beyond computer rooms with limited mainframes and face-to-face classes. The pandemic prompted a reassessment, especially in higher education, where online courses were previously overlooked (Handel et al., 2023).

Brendan Mackey, Sonia Hugh, Tatiana Shestakova et al.

Abstract Deforestation and forest degradation are of continued and growing concern for biodiversity loss, carbon emissions, and a host of ecosystem services for local and global communities. Current remote sensing-based products of forest condition offer valuable information, but typically require extensive training data and represent snapshots in time. Here we provide complementary analyses that address some of these limitations by quantifying forest stability, a key component of ecosystem integrity, of wet tropical forests in the Amazon Basin over a 20-year period using an unsupervised classification method and identify areas of primary and secondary forest. Canopy stability was explored using a time series of remotely sensed MODIS data for the period 2003–2019 at a 500 m pixel resolution. We built on previous work to develop a pixel-based Canopy Stability Index based on the slope and coefficient of variation in fPAR (the fraction of photosynthetically active radiation intercepted by sunlit vegetation canopy) and SIWSI (the shortwave infrared water stress index), which collectively provide information on biophysical processes, canopy structure, and water stress. We examined temporal trends in canopy responses to environmental factors, natural disturbances and land use impacts and compared our results with the MapBiomas forest condition product. Analyses were focused on the Brazilian Amazon but extended to the entire Amazon Basin. The findings revealed a high level of agreement between the Canopy Stability Index and forest categories classified by MapBiomas. However, notable mismatches exist, particularly in ecoregions which contain non-forest ecosystems (e.g., Guianan Highlands Moist Forests, Gurupa varzea, and Pantepui forests and shrublands). Disturbances such as fires are correlated with high levels of canopy instability. The time series analysis revealed prior land use impacts and the occurrence of otherwise unrecognized degraded forest. High resolution modelled data on forest structure can be usefully complemented in tropical wet forests by the kinds of time series analyses presented here, which can assist in tracking changes in forest condition and responses to disturbances.

José P. P. G. Lopes de Almeida, Vinícius G. Machado

This article addresses the multifaceted challenges inherent in the development of the novel REEFS (Renewable Electric Energy From Sea) wave energy converter (WEC). Building on the submerged pressure differential principle, it frames similar WECs before focusing on REEFS that combines renewable energy generation with coastal protection, functioning as an artificial reef. The review follows chronological criteria, encompassing experimental proof-of-concept, small-scale laboratory modeling, simplified and advanced computational fluid dynamics (CFD) simulations, and the design of a forthcoming real-sea model deployment. Key milestones include the validation of a passive variable porosity system, demonstration of wave-to-wire energy conversion, and quantification of wave attenuation for coastal defense. Additionally, the study introduces a second patent-protected REEFS configuration, isolating internal components from seawater via an elastic enveloping membrane. Challenges related to scaling, numerical modeling, and funding are thoroughly examined. The results highlight the importance of the proof-of-concept as the keystone of the development process, underscore the relevance of mixed laboratory-computational approaches and emphasize the need for a balanced equilibrium between intellectual property safeguard and scientific publishing. The REEFS development trajectory offers interesting insights for researchers and developers navigating the complex innovation seas of emerging wave energy technologies.

This study explores the contrasting approaches to robotics integration in manufacturing processes employed by Bangladesh and the United States. By examining three distinct case studies – garment manufacturing, high-tech manufacturing, and agriculture – the analysis reveals significant variations in adoption rates, technological focus, and strategic implementation. These disparities stem from each nation's unique economic landscape, industrial structure, and labour market dynamics. Bangladesh, characterised by low-wage labour in garment manufacturing, cautiously incorporates automation technologies like Sewbots to enhance long-term efficiency and product quality. Conversely, the U.S. garment industry strategically utilises robots for specialised tasks in niche markets, seeking a competitive advantage through precision and design innovation. In high-tech manufacturing, the U.S. leverages readily available commercial solutions and a skilled workforce for extensive robotics adoption, ensuring precision and consistency in complex processes. Bangladesh acknowledges these benefits but faces challenges related to cost and skills gaps, necessitating capability building and strategic partnerships for future integration. Both countries recognise the high potential of robotics in agriculture; however, the U.S. combats labour shortages and enhances efficiency through advanced solutions like farming robots and UAVs. Bangladesh explores automation to increase yield and reduce manual labour dependence. This comparative analysis contributes to understanding global manufacturing and supply chain optimisation by highlighting the diverse factors influencing robotics adoption across nations. The research suggests a future where the U.S. refines and expands advanced robotics use. Bangladesh strategically invests in and develops its workforce, potentially forming partnerships to integrate robotics into high-tech manufacturing and agriculture. As robotics and automation technologies evolve, both countries stand to benefit by embracing these advancements while navigating the challenges to ensure a smooth transition toward a more automated and efficient future for their manufacturing sectors.

Peter Ifechukwude Egbumokei, Ikiomoworio Nicholas Dienagha, Wags Numoipiri Digitemie et al.

The integration of automation in the oil, gas, and renewable energy industries has revolutionized operational efficiency and worker safety. This paper examines the dual role of automation in mitigating workplace hazards while addressing the associated risks and challenges. The objectives include evaluating how automated systems reduce human exposure to high-risk tasks, identifying potential risks introduced by automation, and proposing strategies to balance safety with technological advancement. Key findings highlight that automation significantly reduces accidents in high-hazard environments, such as offshore rigs and wind turbine maintenance, by replacing manual tasks with remote-controlled or autonomous systems. However, the increased reliance on automated technologies presents new challenges, including cybersecurity vulnerabilities, system failures, and the need for specialized training. Furthermore, the shift toward renewable energy introduces unique safety considerations, such as managing risks in battery storage and photovoltaic system maintenance. The paper concludes that while automation offers substantial benefits in enhancing worker safety, a holistic approach is essential to address its challenges. Strategies such as robust risk assessments, adaptive safety protocols, and comprehensive worker training programs are crucial to mitigate the unintended consequences of automation. By fostering collaboration between industry stakeholders and regulatory bodies, the oil, gas, and renewable energy sectors can achieve a sustainable balance between leveraging automation and safeguarding worker well-being. This research underscores the need for ongoing innovation and vigilance to ensure that automation remains a tool for enhancing, rather than compromising, workplace safety.

William Gouvêa Buratto, Rafael Ninno Muniz, A. Nied et al.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

S. Tanveer, Mohammad Imran Ahmad, T. Khan

Indoor air pollution (IAP) is a prevalent issue, and in the absence of any concrete and stringent guidelines, particularly in developing countries the concern becomes graver. Technological strategies like the use of the Internet of Things (IoT) and cloud computing have been explored for real‐time monitoring and these interventions may be investigated to improve indoor air quality (IAQ) and human health. This review article explores the prospects of IoT and smart environments for the improvement of indoor living conditions through automation. Some specific interference like sensors, air pollution simulations and modeling, the concept of smart ventilation, and fuzzy logic controllers (FLC) have been elaborated with pieces of evidence taken from previously published studies in leading indexing databases. Furthermore, some automated health risk assessment tools like the Human exposure model (HEM), Integrated Fuzzy‐stochastic modeling (IFSM), and proximity and interpolation models have also been described. The findings suggested that IoT‐based gadgets require ambient intelligence capabilities for ambient assisted living (AAL). The studies showed that innovations in technology like sensors and modeling techniques may yield crucial information on pollution exposure enabling long‐term and sustainable predictions. However, efficient AAL systems may also face challenges in designing interfaces, usability, and accessibility. Although IoT can aid in mimicking real‐world scenarios, nevertheless its‐micro spatial scale application requires thorough investigation for reliable information extraction.