Search Results (774)

This article presents a systematic review of Internet of Things technology applications across the entire lifecycle of prefabricated buildings. By combining bibliometric analysis with qualitative research methods, seven key research topics in this field are identified and analyzed: integrated information management platforms, position tracking, quality checking and management, project management and cost control, carbon emissions monitoring, indoor environment monitoring, and data security and information encryption. The current research status and challenges pertaining to each of these topics are critically assessed with emphasis on the main challenges in terms of automation level and accuracy, system integration and data interoperability, and deployment economy and robustness. The findings reveal that current IoT applications in prefabricated buildings are mainly focused on data collection, data visualization, and status monitoring, and future research should further strengthen the integration of IoT with AI, big data, and other technologies to promote predictive analysis, intelligent optimization, and autonomous decision-making. Three key topics are subsequently discussed from a management perspective: collaborative carbon information flow management, human-centered health and safety management, and finally, smart operation, maintenance, and disassembly driven by a circular economy, and directions are proposed for their future integration and innovation with emerging technologies. This study provides directional recommendations and references for researchers and practitioners in related fields. The review further suggests that the future development of IoT-enabled prefabricated buildings requires not only technological breakthroughs but also the collaborative evolution of digital technologies, construction practices, and industrial systems, supported by effective management mechanisms, industry collaboration, and practical implementation strategies. Full article

The Internet of Plants (IoP) represents the convergence of Artificial Intelligence (AI), Big Data analytics, and the Internet of Things (IoT) within protected agricultural systems. This study presents an IoP platform designed to collect, process, and analyze real-time environmental data using specialized IoT sensors capable of monitoring critical variables, including carbon dioxide concentration (CO₂), pH, air temperature, and relative humidity in hydroponic production systems. The proposed framework integrates advanced machine-learning algorithms, including Random Forest Regressor and Long Short-Term Memory (LSTM) neural networks, to process large volumes of environmental data and support crop management. In addition, the platform incorporates Vapor Pressure Deficit (VPD) and Growing Degree Days (GDD) analyses to provide crop-specific recommendations and support informed decision-making. This platform establishes a benchmark for smart agriculture in Mexico’s Laja–Bajío region, facilitating informed decision-making and maximizing the sustainability of food systems. Experimental validation was conducted under both controlled and semi-controlled environments using Swiss chard (Beta vulgaris subsp. cicla L.) and lettuce (Lactuca sativa L.) cultivated in hydroponic systems. These environments represented contrasting climatic conditions, allowing evaluation of platform stability and forecasting performance under varying thermal regimes. The Random Forest Regressor model, trained using growth chamber data consisting of 19,836 valid observations, reproduced the deterministic VPD relationship with a coefficient of determination (R²) of 0.90 and a root mean square error (RMSE) of 0.08 kPa, confirming internal consistency and identifying temperature as the dominant contributing variable rather than predicting an independent outcome. The dynamic alarm system, integrated with crop phenological stages, demonstrated greater effectiveness than conventional static-threshold approaches by generating alerts according to crop developmental requirements. Furthermore, the web-based visualization platform enabled users to interpret environmental conditions through intuitive graphical representations, facilitating decision-making without requiring specialized technical expertise. The results demonstrate the feasibility of the IoP platform as a comprehensive environmental management tool for protected agricultural systems. The proposed framework provides a scalable solution for precision agriculture applications in the Laja–Bajío region of Mexico and in other regions with similar production systems. Full article

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

Whether the marketization of data as a production factor can be redirected toward environmental ends is a central question for the governance of the digital economy. This study investigates whether and how the pilot policy for big data trading platforms improves corporate green development performance (CGDP). Using A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2024, this paper treats the pilot policy for big data trading platforms as a quasi-natural experiment and applies a staggered difference-in-differences (DID) design to estimate its effect on CGDP, together with the transmission channels and boundary conditions that govern it. Because the rollout is staggered, we complement the two-way fixed-effects benchmark with the heterogeneity-robust estimators of Callaway and Sant’Anna, Sun and Abraham, and the Goodman–Bacon decomposition, and cluster standard errors at the city level. The policy raises CGDP by 0.076, about 6.1% of the sample mean. The estimate remains robust to an event-study/parallel-trend test, placebo tests, propensity score matching (PSM), the Oster selection-on-unobservables bound, alternative and broader green outcome measures—including a significant reduction in chemical oxygen-demand emissions—controls for concurrent digital and innovation policies, exclusion of the 2020 pandemic year, and industry fixed effects. Mechanism evidence shows that the effect operates through stronger green dual innovation, upgraded human capital, and heightened scrutiny from media outlets and securities analysts. The impact is stronger for firms whose executives exhibit greater green awareness and whose internal control is of higher quality, and in more competitive industries and regions with stricter environmental regulation. By showing that a market for data can be redirected toward environmental ends, this study links data-factor marketization to corporate green transition and provides policy evidence for aligning digital economy reform with sustainable development. Full article

The rapid growth of smart cities has highlighted the need for robust, flexible, and cost-effective platforms that can support the integration and management of Internet of Things (IoT) devices, data streams, and analytics pipelines. However, recent studies have revealed a significant gap in the availability of open-source IoT platforms tailored for smart cities, creating a barrier for researchers and developers seeking to experiment, innovate, and validate connected urban applications. This article proposes a lightweight yet comprehensive open-source smart city platform architecture to address this gap. By integrating essential components such as real-time data ingestion, big data storage, messaging protocols, and data analytics, the platform offers a solid foundation for developing and testing smart city solutions. The architecture focuses on modularity, interoperability, and scalability, ensuring that the system can grow alongside the demands of urban environments. We aim to foster an open, collaborative ecosystem where researchers and developers can freely access, modify, and enhance the platform, driving forward the next generation of smart city innovations. The proposed solution serves as a bridge between theoretical research and practical deployment, reducing barriers to entry and accelerating the development of smarter, more sustainable urban systems. Full article

The metalworking industry faces growing regulatory pressure to quantify and verify its product carbon footprint (PCF), driven by frameworks such as the European Union’s Carbon Border Adjustment Mechanism (CBAM) and emissions trading schemes (ETS). Traditional static accounting methods, based on generic emission factors and annual averages, are insufficient given the dynamic, multi-stakeholder nature of the sector’s supply chains. This study presents a systematic review conducted under the PRISMA 2020 protocol to identify, classify and critically evaluate the digital tools and technologies used to calculate, manage and verify PCF in this sector. From 495 records screened, 53 thematically relevant studies were analyzed and 5 sector-specific cases examined in depth. The results indicate that the Internet of Things (IoT) and smart sensor networks constitute the primary data-capture layer, while Machine Learning, Big Data and Digital Twins are the predominant processing technologies. Blockchain and verifiable digital credentials emerge as governance mechanisms that ensure the transparency, auditability and immutability of emissions inventories, enabling compliance through Digital Product Passports (DPPs). We conclude that digital decarbonization requires interoperable architectures integrating real-time capture, distributed traceability and common semantic standards; viability in small and medium-sized enterprises (SMEs) and interoperability across heterogeneous platforms remain the main research gaps. Full article

To address the challenges of data security governance under new conditions and align with technological trends in data security, this paper proposes a graph algorithm-based method for identifying key personnel with critical permissions in the practical applications of large state-owned enterprises. This method can uncover potential permission influence factors within the system and evaluate the weight of influence from different perspectives, providing highly interpretable identification results. To tackle the issue of detecting anomalous user and entity behaviors in data security governance, a user and entity behavior anomaly detection method based on Generative Adversarial Networks (GAN) is introduced. Experimental results show that the proposed method achieves higher precision, recall, and F1-score averages compared to baseline models; specifically, an average F1-score of 0.75 versus 0.72 for LSTM-based TadGAN, 0.62 for ARIMA, and 0.65 for a commercial UEBA baseline across the three evaluation datasets. A data security platform was designed and developed, which plays a significant role in reducing data security risks, assisting enterprise compliance, and promoting data development and utilization. This platform has been applied in various centralized data management projects and meets the big data processing requirements in secure environments, demonstrating strong application and promotional value. Full article

The omics sciences represent a revolution for clinical studies, offering integrative approaches to analyzing biological data with unprecedented depth. From the discovery of the double-helix structure of DNA to the CRISPR-Cas9 gene editing tool, passing through the evolution of sequencing platforms and the exponential advance of computing power and in silico tools, omics has progressed in its role of leading innovative solutions for old challenges in health sciences. In this review, we describe different omics, the history of their techniques and technologies, data analysis and up-to-date visualization tools used for clinical data in research and health systems. We also discuss how omics are currently being applied in diagnosis, precision and personalized medicine. For the future, omics vow to underpin the majority of decisions made by health professionals, allowing individualized treatments based on Big Data and personal biological information. Full article

Smart-city governance increasingly relies on data infrastructures to connect public agencies, digital platforms, and urban services; however, complex emergencies continue to expose fragmentation in information sharing, administrative responsibilities, and cross-boundary coordination. A key unresolved question is whether data-driven policy experimentation can strengthen the institutional foundations of emergency collaboration. In this study, we examine China’s National Big Data Comprehensive Pilot Zones as a systemic governance intervention and apply a multi-period difference-in-differences model to estimate the effect of pilot-zone construction on the policy-text-based institutionalization of emergency collaboration, using provincial panel data for 30 Chinese provinces from 2010 to 2022. The results show that pilot-zone construction significantly strengthens institutionalized emergency collaboration in provincial policy systems. Mechanism tests provide evidence consistent with two complementary pathways: emergency-related technological innovation, measured by granted patents screened through IPC/CPC classifications and title–abstract keywords, supports task-specific capacities such as risk sensing, early warning, emergency communication, command support, and resource allocation; digital government strengthens administrative interoperability, data sharing, platform-based coordination, and standardized interdepartmental procedures. Heterogeneity analyses show stronger effects in eastern, middle-income, and severely aging regions, suggesting that policy effectiveness depends on implementation capacity, absorptive capacity, and emergency-service demand. This study contributes to systems governance and smart-city research by showing how data-driven policy experimentation can shape the formal institutionalization of emergency collaboration. The findings should be interpreted as evidence of institutionalized policy attention and formal collaborative arrangements rather than direct evidence of field-level emergency response performance. Full article

The article critically examines the emergence of technofeudalism within the European Union’s legal framework, drawing on the theoretical contributions of Yanis Varoufakis, Alfred C. Yen, and Katrina Geddes. We argue that the EU’s historically market-oriented regulatory architecture contributed to conditions that facilitated the rise of dominant technology companies exercising quasi-governance functions over digital environments, extracting value from users while evading meaningful democratic accountability. Our analysis distinguishes between two categories of enabling legislation: structural rules, which govern corporate status, taxation, and market consolidation; and action-oriented rules, which regulate platform behavior, algorithmic governance, consumer relations, and data protection. We demonstrate how fragmented national tax regimes, ineffective merger control, under-regulated algorithms, asymmetric consumer protections, unclear liability frameworks for online content, exploitable private international law mechanisms, and inadequately enforced data protection standards collectively reinforce Big Tech’s dominance. While recent regulatory interventions such as the Digital Services Act and Digital Markets Act represent important steps, they remain embedded in a market-oriented paradigm that insufficiently addresses the broader social, cultural, and democratic implications of platform power. The article concludes by calling for a more coherent, democratically grounded approach to digital regulation—one that moves beyond fragmented, reactive policymaking toward a comprehensive framework capable of strengthening democratic accountability and public oversight within the digital sphere. Full article

The massive amount of publicly available data has necessitated an increase in public and organizational awareness of the potential risks of leaking private data, whether intentionally or unintentionally. The damage caused by leaking these data depends on their degree of sensitivity. Disclosing a person’s or an organization’s private data via different social media platforms might threaten people’s lives or the organization’s reputation or finances. Handling big data, especially unstructured data, is challenging. Consequentially, many solutions have been proposed to detect sensitive data in structured containers. However, detecting sensitive data in unstructured containers is still challenging, especially with context-dependent and high-performance measurement results. In this study, experiments on certain machine learning models and two transformers—DistilRoberta and ALBERT—were conducted to detect unstructured, textual, context-dependent sensitive data. The results show that DistilRoberta demonstrated higher accuracy and recall, and was faster and lighter than ALBERT. Full article

Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), and remote sensing technologies have transformed agricultural monitoring, precision farming, and climate-resilient decision-making. However, the widespread adoption of AI-driven agricultural systems remains constrained by the black-box nature of advanced predictive models, particularly deep neural networks. Explainable Artificial Intelligence (XAI) has emerged as a critical solution for improving transparency, interpretability, accountability, and trust in AI-based agricultural remote sensing systems. This review provides a comprehensive synthesis of the recent developments in XAI applications within smart agriculture, with emphasis on interpretable remote sensing analytics and sustainable decision-making. The review discusses the evolution of AI in agriculture, major remote sensing platforms, explainability frameworks, and the integration of XAI with satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and geospatial big data. Key agricultural applications, including crop classification, yield prediction, disease detection, soil property assessment, irrigation management, carbon monitoring, and climate adaptation, are critically evaluated. Furthermore, the review compares intrinsic and post hoc explainability methods such as attention mechanisms, saliency maps, and counterfactual explanations. The interpretation of model outputs and reported results from recent studies is discussed to demonstrate how XAI improves model reliability and stakeholder confidence. Challenges related to data heterogeneity, scalability, uncertainty, ethics, fairness, and computational complexity are also analyzed. Finally, future perspectives are presented regarding hybrid explainable frameworks, physics-informed AI, edge computing, digital twins, and trustworthy autonomous agricultural systems. The review emphasizes the central role of XAI in enabling transparent and sustainable agricultural intelligence under rapidly changing climatic and environmental conditions. Full article

The evolution of digital technologies is reshaping parliaments worldwide, driving fundamental changes in their operations. Parliaments, being traditionally conservative institutions, typically lean toward “mature” emerging or disruptive technologies through cautious, incremental digital transformation attempts, resulting in complex digital parliamentary environments for their users, processes, systems, and tools. The paper employs an integrative literature review as its methodological tool, examining the concept of the “digital parliament” and the technologies that enable it. Using a PRISMA-informed methodology as a guide, we conducted an integrative review covering the period 2006–2025, and in this context, we retrieved 535 publications, screened 260, thoroughly examined 57, and analyzed and synthesized 34 studies addressing digital parliamentary technologies, digital platforms, and cooperative workspaces. We found that while specific parliamentary technology (ParlTech) applications—including big data analytics, artificial intelligence (AI), and hybrid parliamentary tools—are reaching institutional maturity, the concept of a digital parliament remains fragmented, lacking a unified definitional and operational framework. Key research gaps have been identified concerning user classification, the digitization of parliamentary functions, operations, and processes, as well as the institutionalization of cooperation platforms. Based on these findings, we propose strategic directions toward establishing a responsible, inclusive, and evidence-based digital parliament. This research contributes as a guideline for parliamentary organizations seeking to create, retain, and disseminate public value through the responsible adoption of emerging digital technologies. Full article

Google Earth Engine (GEE) has become a central platform for planetary-scale geospatial analysis, but its rapid evolution in the last few years is not yet reflected in the existing review literature. Earlier reviews mainly describe the platform’s architecture and its initial application domains, whereas a structured bibliometric and thematic overview of the post-2022 phase of GEE is still lacking. In this more recent phase, the platform has introduced foundation models, satellite embeddings, and native links to cloud databases. Drawing on a structured bibliometric analysis of 5591 Scopus and Web of Science indexed documents published between 2011 and 2025, the results reveal sustained long-term growth, with annual publications increasing from 3 records in 2011 to 1371 records in 2025, corresponding to a compound annual growth rate (CAGR) of 54.88%, indicating a shift from exploratory testing of the platform to more operational use. Logistic growth modelling ($R^2=0.991$) suggests that GEE research is transitioning from rapid expansion towards a scientific maturity phase, where the platform increasingly functions as a normalized analytical infrastructure embedded within broader cloud-native geospatial ecosystems. The full 2011–2025 corpus is used to establish long-term bibliometric trajectories, whereas the thematic synthesis focuses on the post-2022 transition towards Geospatial Artificial Intelligence(GeoAI), satellite embeddings, and cloud-database interoperability. The review examines how new satellite embedding datasets and BigQuery integrations help close the gap between raster-centric Earth observation (EO) workflows and tabular data science. We summarise methodological changes from traditional pixel-based classifiers to multimodal fusion approaches that combine Synthetic Aperture Radar (SAR), Global Ecosystem Dynamics Investigation (GEDI), and optical sensors, and we discuss how GEE’s highly integrated ecosystem influences reproducibility and the risk of vendor lock-in. Finally, we propose a roadmap for the ongoing transition of GEE towards GeoAI, offering researchers and policymakers a transparent and reproducible framework for deploying the platform in high-impact environmental governance. Full article

The Marmara region of Türkiye, situated along the North Anatolian Fault Zone (NAFZ), constitutes one of the most seismically active and densely monitored zones globally. Given the region’s high vulnerability and the catastrophic impacts of historical events—notably the 1999 İzmit and 2023 Kahramanmara¸s sequences—there is a critical need for advanced seismic hazard risk assessment (SHRA) methods that move beyond static models. This review examines the paradigm shift from traditional geophysics to big data seismology, characterized by the “Five Vs”: volume, velocity, variety, veracity, and value. Critically, we distinguish between two fundamentally different problems: Earthquake Early Warning (EEW), which operates on sub-second timescales after rupture initiation, and probabilistic earthquake forecasting, which operates on timescales of years to decades. The study discusses how cloud-native platforms such as Azure Databricks, combined with data pipelines using Apache Kafka (version 3.5.1) and Apache Spark (version 4.1.2), enable the real-time processing of petabyte-scale seismic sensor streams. Key technological tools, including Physics-Informed Neural Networks (PINNs) and deep learning models such as PhaseNet, are analyzed for their demonstrated ability to enhance EEW systems through sub-second phase picking and automated event detection. Seismic tomography is also undergoing AI-enabled transformation, yielding higher-resolution subsurface imaging. We present statistical validation metrics and uncertainty quantification methods essential for credible hazard assessment. By addressing computational bottlenecks through hybrid computing architectures and edge computing, this framework aims to improve the warning lead time for Istanbul’s critical infrastructure. This work provides a structured roadmap for bridging the gap between traditional seismic data analysis and operational predictive analytics in the Marmara region. Full article