Search Results (2,061)

The Internet of Things (IoT) and quantum computing revolutionized the era of conventional and classical computing into a new paradigm of Quantum-IoT where qubits and entanglement make IoT more interactive, powerful, and secure. They facilitate numerous tasks by increasing productivity and efficiency, paving the path for a smarter and more connected future. In this article, we propose a novel authentication scheme, “Securing the Internet of Things, Lightweight Mutual Authentication Based on Quantum Key Distribution (LMA-QIoT)”. LMA-QIoT enables mutual authentication using various parameters including quantum key distribution, symmetric keys and timestamps, as well as additional quantum random numbers. All these parameters play a crucial role in thwarting man-in-the-middle, backtracking and nonce reuse attacks. The evaluation of LMA-QIoT demonstrates that quantum key distribution and quantum numbers enhance system performance by reducing CPU usage by 25% and memory requirements 30% compared to an IoT edge-based system and without a server, respectively. In the reconfiguration ratio, the efficiency metric grows exponentially and remains constant on the initial line in edge-server-based systems. In comparison, LMA-QIoT confirms a much reduced overall computational complexity by 16.64%, with the lowest computational cost of $O\left(n^2\right)$. At 1024 Bytes, the original data length and increased data length (normalized) sizes stay constant with $2logn\left(klogn\right)$. Comparing the total overhead, LMA-QIoT demonstrates a reduction of 33 ms, which corresponds to approximately 16.63% less than the baseline mechanisms. Full article

Recently, poultry production has increased worldwide to address the increasing demand of affordable animal-sourced protein. To meet this requirement, poultry production operations have become more concentrated, introducing management challenges related to disease control, productivity, and animal welfare. However, manual flock monitoring and management have become impractical in such cases, creating a need for automatic data-driven management approaches. In this context, the Internet of Things (IoT) has emerged as a potential technological solution for continuous flock monitoring, data sharing, and decision-making. Despite this, its adoption in poultry production is limited compared with its widespread use in crop production, transportation, and manufacturing industrial sectors. Furthermore, advanced analytical techniques such as artificial intelligence (AI), applied to data gathered by IoT-enabled devices, have shown promising results by generating actionable information. Existing literature suggests that the integration of IoT and AI can address the major challenges associated with modern large-scale poultry production systems. While most applications remain at the research scale, such technologies have the potential for improving flock monitoring, enhancing productivity, and ensuring proper animal welfare. This narrative review examines the current state of IoT and AI based technologies, together or in part identifies the limitations, research gaps, and opportunities for future development. Full article

Background: In the Philippines, studies have shown that availability and access to healthcare varies widely. Although the shortage of psychiatrists in the country has been recognized for many years, no published study to date has described their distribution across the regions. This study aimed to describe the distribution of psychiatrists in the country using publicly available data on the Internet. Methods: This was a cross-sectional study, analyzing publicly available data from the Philippine Psychiatric Association (PPA) web directory, the Philippine Health Insurance Corporation (PhilHealth) web database of accredited psychiatrists, and the Philippine Statistics Authority. Information on location of practice, sex, PPA membership, PhilHealth accreditation, regional gross domestic product (GDP), and regional population were collated. Results: Information on 409 psychiatrists was available online, with 68% being female and 53% holding PhilHealth accreditation. There were a total of 417 declared locations of practice, with six psychiatrists practicing in more than one location. The National Capital Region accounted for 53.5% of the declared practice locations, while no psychiatrist declared practicing in the Bangsamoro region. Conclusions: This study highlights the maldistribution of psychiatrists across the Philippines. Policies to incentivize and encourage practice in low-access regions and investment in technology, such as telemedicine, may help reduce the access gap. Full article

This paper utilizes panel data of Chinese A-share listed manufacturing firms from 2006 to 2022 and measures regional digital infrastructure by the number of internet broadband access ports per capita. It systematically examines the moderating role of digital infrastructure in the relationship between labor protection policies and firms’ labor productivity. The findings are as follows: (1) Digital infrastructure exhibits a positive moderating effect on the relationship between the Labor Contract Law and firms’ labor productivity. This conclusion remains generally robust across multiple robustness tests and endogeneity treatments, and the direction of the results remains consistent after applying an instrumental variable approach to alleviate endogeneity concerns. (2) The digital transformation channel exhibits a negative relationship, indicating that compliance pressure associated with the institutional reform generates a short-term “crowding-out effect” on firms’ digital investment; the human capital channel shows a positive relationship, indicating that digital infrastructure strengthens the institutional effect by improving the level of urban human capital. (3) The moderating effect is particularly pronounced in cities with strong digital industry foundations, abundant fiscal resources, and firms that have not received government digital subsidies. These results provide empirical support for optimizing the supporting environment of labor protection policies, accelerating digital infrastructure development, and enhancing enterprise adaptability to institutional changes. Full article

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks. Full article

Autonomous supply chains (ASC) are the next generation of digitally empowered logistics and operations systems that can make adaptive, data-driven, and intelligent decisions. Innovations in artificial intelligence (AI), digital twins (DT), and predictive analytics (PA) are transforming traditional supply chains into integrated and interactive networks to detect disruptions, simulate the future, and automatically modify operational decisions. This paper reviews the ASC mechanism and summarizes the increasing literature on the technologies and analytical capabilities available to support intelligent supply chain decision systems. A structured literature review was conducted using Scopus, Web of Science, and Google Scholar, resulting in 52 relevant studies after screening and eligibility assessment. The paper discusses the recent advances in AI-based forecasting, simulation environments using digital twins, data integration using the Internet of Things (IoT), and predictive analytics. These technologies can help an organization gain real-time visibility of the supply chain networks. They improve the precision of demand forecasting, optimize inventory and production planning, and dynamically coordinate logistics operations. Digital twins allow the development of virtual models of supply chain ecosystems, which could be used to test scenarios, analyze risks, and plan strategies. These capabilities combined can be used to create predictive and self-adaptive supply networks capable of being responsive to uncertainty and market volatility. Besides examining the technological foundations, the paper also tracks key challenges related to the move towards autonomous supply chains, such as data governance, system interoperability, cybersecurity risks, algorithm transparency, and the necessity of successful human-AI collaboration in decision-making. The synthesis leads to a multi-layered framework that integrates data acquisition, analytics, simulation, and execution for autonomous decision-making in supply chains. Future research directions in relation to resilient supply networks, intelligent automation, and adaptive supply chain ecosystems are also provided in the study. Through integrating existing information on the new forms of intelligent technology and how it can be incorporated into the supply chain systems, this review contributes to the literature on next-generation supply chains. It will also offer information to both researchers and practitioners aiming at designing autonomous as well as data-driven supply networks. Full article

This study explores the drivers of economic growth in the BRICS economies—Brazil, Russia, India, China, and South Africa—over the period 1994–2024, focusing on the roles of digital infrastructure and financial development. Using a balanced panel, we examine how internet connectivity and access to credit shape growth, both independently and in combination, while accounting for gross fixed capital formation, urbanization, and government expenditure. Given the macro-panel structure, which exhibits heteroskedasticity, serial correlation, and cross-sectional dependence, we employ robust estimation techniques, including Driscoll–Kraay standard errors (DKSE), Feasible Generalized Least Squares (FGLS), and Panel-Corrected Standard Errors (PCSE). To capture potential heterogeneity across different growth scenarios, we further apply the Method of Moments Quantile Regression (MMQR) as a robustness check. Our findings show that both internet connectivity and financial development consistently promote economic growth across all main specifications. Importantly, the interaction between these two factors is also significant, indicating that the benefits of digital infrastructure are stronger in countries with deeper financial systems, and vice versa. Among the control variables, capital accumulation and government spending positively contribute to growth, while urbanization exhibits a negative association, reflecting the structural challenges of rapid urban expansion. MMQR results confirm that these relationships hold across low-, medium-, and high-growth periods, highlighting their broad relevance. These findings highlight the synergistic role of technological and financial development and underscore the importance of integrated policies to sustain long-term, inclusive growth in the BRICS economies. This study suggests that policymakers should adopt integrated strategies that enhance digital connectivity, deepen financial development, and support productive public investment to sustain inclusive and resilient economic growth. Full article

Using panel data from 278 Chinese prefecture-level cities during 2011–2019, this study employs two-way fixed effects and instrumental variable (2SLS) models to investigate how the distinct dimensions of digital financial inclusion (DFI)—coverage breadth, usage depth, and digitisation level—affect urban economic growth. The results reveal substantial heterogeneity across these DFI dimensions. The expansion of coverage breadth significantly and robustly promotes city-level economic growth. In contrast, greater usage depth exerts a negative effect, possibly due to regulatory lags in internet credit and insurance that intensify financial risks. The digitisation level shows a positive but statistically insignificant impact, indicating that digital infrastructure has not yet been fully transformed into growth-enhancing productivity. Furthermore, the regional heterogeneity analysis reveals a stark divergence: DFI acts as a crucial growth engine in the financially underserved central and western regions, whereas excessive financialisation has exerted a crowding-out effect in eastern cities. These findings suggest that policy efforts should prioritise broadening DFI coverage while strengthening the regulation of usage-related activities, thereby balancing financial innovation with systemic stability. Full article

Over the past decade, Building Information Modelling (BIM) has become increasingly adopted across the Architecture, Engineering, Construction, and Operation (AECO) industry. As its use in practice has expanded, BIM has also received growing scholarly attention. Existing research has largely concentrated on specific applications of BIM, such as construction management, sustainable building design, infrastructure development, and facility management. However, comparatively limited attention has been given to examining BIM implementation from a global perspective. This study addresses this gap by applying a scientometric approach to analyse global BIM implementation research published between 2004 and 2023. The analysis is conducted using co-authorship, co-word, and co-citation analysis to map the structure and development of the research field. A total of 1349 published articles were obtained from the Scopus database for the analysis. The study identifies the most productive and influential contributors to BIM implementation research, including leading researchers, research institutions, countries, subject areas, and academic journals. In addition, the analysis highlights several key thematic domains within global BIM research. These include topics related to Industry Foundation Classes (IFC), Internet of Things (IoT), Geographic Information Systems (GIS), Historic Building Information Modelling (HBIM), and Digital Twin technologies, which appear as prominent keywords within the BIM implementation literature. Beyond mapping these trends, this paper integrates dispersed scientometric evidence into a coherent global perspective, revealing how BIM implementation research has evolved, matured, and diversified across regions and disciplines. It also establishes a structured knowledge base that can serve as a benchmark for future comparative studies, performance assessments, and policy development initiatives in the digital construction domain. These findings provide valuable insights for researchers, practitioners, and policymakers by illustrating landscape of BIM-related research and highlighting potential directions for future investigation. Full article

Message Queuing Telemetry Transport (MQTT) is a lightweight publish–subscribe protocol widely deployed in Internet of Things (IoT) systems. Although MQTT defines authentication and authorization mechanisms, their enforcement accuracy, configuration sensitivity, and operational cost under controlled misconfiguration conditions remain insufficiently quantified. This study experimentally quantifies authentication enforcement behavior and Access Control List (ACL) misconfiguration impact within a standards-compliant MQTT deployment under controlled laboratory conditions. Rather than benchmarking a specific software product, the work measures protocol-defined security behavior—including authentication success rate, false acceptance rate (FAR), false rejection rate (FRR), privilege-boundary preservation, authentication latency, and broker CPU utilization—across systematically constructed operational and failure scenarios. Username/password and mutual TLS authentication were evaluated under valid and stress-induced connection conditions, alongside structured ACL policies incorporating wildcard over-permission. Across repeated trials, username/password authentication achieved higher observed connection reliability (≈0.95), while TLS-based authentication provided stronger cryptographic identity assurance at the cost of increased authentication latency (≈42.6 ms vs. 14.8 ms) and higher CPU utilization (≈23.7% vs. 9.4%). No false acceptances were observed within 100 unauthorized trials per configuration, corresponding to a 95% confidence upper bound of <3% for FAR under a binomial model. Under controlled ACL misconfiguration, 22 of 100 evaluated authorization operations accessed topics beyond the originally intended least-privilege scope, yielding a reproducible privilege expansion rate of 0.22. This expansion resulted from wildcard policy semantics rather than an enforcement malfunction. The results provide controlled empirical quantification of reliability–security trade-offs and configuration-driven privilege-boundary behavior within a standards-compliant MQTT deployment. While the findings reflect enforcement behavior as realized in the evaluated implementation and laboratory environment, the proposed measurement framework establishes reproducible criteria for assessing MQTT security enforcement accuracy under controlled conditions. Full article

Unmanned Aerial Vehicles (UAVs) were originally designed for military and surveillance applications but are now significant in smart agriculture, wireless communication, and product delivery. In contrast to an Internet Service Provider (ISP), which typically relies on fixed base stations, which can fail in the event of a disaster, UAVs offer more stable alternatives. Because IoT devices, sensors, and ground users have limited processing power and battery life, there is a need for energy-efficient solutions. Meanwhile, users still expect high data rates. UAV-based wireless networks can meet these needs, even in harsh or disaster-hit areas. Current research focuses on improving energy efficiency and data transmission by optimizing UAV flight paths and scheduling. In this work, we tackle these issues by formulating a mixed-integer non-convex optimization problem that jointly considers device scheduling and UAV trajectory. We further decompose it into the following two parts: energy-efficient scheduling among ground users ($P_2$) and the trajectory optimization of UAVs ($P_3$). To address these issues, we develop a linear programming relaxation approach, a Quadratically Constrained Quadratic Programming (QCQP)-based Successive Convex Approximation (SCA) scheme, and the Block Coordinate Descent (BCD) algorithm. Experimental results demonstrate that our approach outperforms the state of the art in both power consumption and transmission rate. Full article

This study explored pre-service teachers’ views on the use of generative artificial intelligence (Gen AI) in the production of misinformation, addressing the potential challenges posed by deepfakes generated by these online resources. A quantitative approach was used; 133 pre-service teachers participated in the study, all of them were enrolled in primary education degree programmes in the Region of Murcia, Spain. The results indicated a clear awareness of the risks posed by these digital tools in the generation of deepfakes. Respondents became aware of the potential threats this may pose on the internet, which can be further exacerbated when disseminated in educational environments. Recognising the relevance of pre-service teachers’ concerns can help educators and educational administrations take steps to limit Gen AI in accordance with ethical parameters and thus reduce the spread of misinformation. In social science teaching and learning, further research is needed to equip students with the essential skills to distinguish between accurate and inaccurate information. For all these reasons, it seems essential to improve research in media literacy education for the application of identification skills used in assessment processes. These improvements can take the form of evidence-based approaches, such as AI literacy programmes or media literacy modules, to facilitate student learning and ensure better quality education. Full article

The low-carbon transformation of the manufacturing industry is a key path to balance climate goals and industrial competitiveness. This systematic review critically analyzes 145 studies from 2012 to 2025 to explore the low-carbon transformation. Findings show that low-carbon city pilots reduce manufacturing carbon intensity via fiscal and tech expenditures; industrial internet and additive manufacturing reshape low-carbon production, with digital and green process innovations driving emission reduction. Yet, bottlenecks exist: SMEs face digital adaptation and green financing constraints; excessive digitalization causes energy rebound; high-carbon industries’ deep decarbonization is hindered by unproven large-scale economic feasibility of low-carbon tech, alongside policy-technological disconnection, and green finance structural contradictions. This study proposes core solutions: dynamic policy adjustment mechanisms, multi-dimensional SME support systems, and technology–economy coupling evaluation models. It establishes research coordinates for academia, designs policy tools for decision-makers, and provides a technological framework for industrial deep decarbonization, offering global references for balancing climate goals and manufacturing competitiveness. Full article

The rapid growth of the Internet of Things (IoT) is fundamentally altering industrial and economic landscapes by embedding smart, connected devices into everyday operations. Despite these benefits, significant concerns regarding data protection and user privacy continue to obstruct the widespread use of these technologies, particularly with the looming threat of quantum computing. Implementing post-quantum cryptographic (PQC) solutions is vital for addressing these risks, yet the limited resources found in IoT edge devices present major deployment challenges. Lattice-based cryptography has become a leading solution to these problems, largely because it depends on efficient polynomial multiplication. Enhancing the execution of this mathematical operation is crucial for improving the overall performance of PQC protocols. In this work, we introduce a hybrid serial–parallel systolic architecture specifically engineered for polynomial multiplication within the Binary Ring Learning With Errors (BRLWE) scheme. Designed for the security processors used in IoT hardware, this architecture significantly increases processing speeds while minimizing the use of hardware resources and reducing energy consumption. Such improvements are critical for establishing a secure IoT infrastructure that is resilient against quantum-era attacks and capable of supporting industrial expansion. Moreover, this research aligns with global Sustainable Development Goals (SDGs) 8 and 9 by building trust in innovative systems and fostering a more secure, sustainable, and productive digital economy. Full article

Small and medium-sized enterprises (SMEs) are expected to contribute to sustainable manufacturing, yet they often lack the resources and capabilities needed to adopt advanced automation in a structured and scalable manner. While lean robotics have been widely studied, there is still limited empirical evidence on how their integration can be systematically designed to improve sustainability-oriented performance in SME contexts. This paper examines how a scalable lean robotics system can be conceived and implemented to enhance productivity and resource efficiency in an SME packaging process. We develop a lean robotics design approach that jointly considers lean principles, collaborative industrial robotics, and Industrial Internet of Things (IIoT) monitoring. The approach is applied in a real-world case study of a “Fold Station” robotic cell, where stone paper sheets are destacked, glued, and formed into cylindrical plant protectors. Key performance indicators related to cycle time, material utilization, process stability, and manual workload are measured before and after implementation. The results show a three- to four-fold reduction in preparation time per unit, more efficient use of stone paper and adhesive, and a decrease in repetitive manual handling, thereby contributing to both economic and environmental sustainability. TRIZ (Teoriya Resheniya Izobretatelskikh Zadach, Theory of Inventive Problem Solving) is used to structure the resolution of design contradictions that arise when embedding lean principles into the robotic system and to support its scalable adaptation to different production scenarios. This study advances the understanding of lean robotics for sustainable SME production and derives practical guidelines for designing scalable, resource-efficient robotic cells. Full article