Search Results (2,099)

Conventional defect inspection for warp-knitted lace relies on manual work and negative-sample-based training, resulting in low efficiency, frequent false detections and poor adaptability. This study presents a novel AI visual inspection system centered on positive-sample learning, which is built upon a five-layer 5G + Industrial Internet distributed architecture. Supported by modified looms, high-precision imaging devices and an optimized YOLOv5s model, the system accomplishes intelligent defect detection. A positive-sample self-learning paradigm and dual-model collaboration mechanism are proposed to reduce the demand for negative samples and cut labeling expenses. The integration of CBAM, FPN + PAN structure, self-supervised learning and hybrid loss further strengthens the recognition performance for subtle defects under complex patterns. Industrial tests show that the system reaches a grid-level classification accuracy of 95% and a frame-level detection rate over 98%, with a detection speed of 30 m/min. It reduces labor costs and product reject rates by 40% and 30% correspondingly while running stably in real production. This method breaks the constraints of traditional training modes, provides a scalable intelligent solution for the digital upgrading of the warp-knitted lace industry, and promotes the high-quality development of textile manufacturing. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

Smart labels are emerging as a key enabling technology for product traceability, environmental monitoring, and user interaction within Internet of Things (IoT) ecosystems. This work presents the design and experimental validation of a low-power smart label platform integrating Bluetooth Low Energy (BLE) communication, temperature sensing, and dynamic e-paper visualization based on the HY0020 System-on-Chip (SoC). This platform was implemented on a custom Printed Circuit Board (PCB) designed around a 1.02-inch monochrome e-paper display and incorporates a TXS0108E interface to support reliable display communication. The developed prototype enables wireless user interaction, dynamic QR code rendering, and ambient temperature monitoring while maintaining low average power consumption. Experimental evaluation included BLE communication testing, display operation validation, temperature monitoring assessment using the integrated HY0020 sensor, and energy consumption characterization. Experimental results confirmed reliable BLE connectivity, stable temperature monitoring performance under normal environmental conditions, and an estimated battery lifetime of approximately 54 days under the evaluated operating profile. The presented platform demonstrates the feasibility of integrating sensing, wireless communication, and electrophoretic display technology within a compact battery-powered smart label device. The proposed architecture provides a practical proof-of-concept foundation for future applications involving product traceability, digital information management, and Digital Product Passport (DPP)-oriented services. Full article

This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances the existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

The article presents the development of a multi-zone temperature control model in an Internet of Things (IoT) environment, designed for the cold chain of pharmaceutical products transported by sea. The model is based on the Elephant Herding Optimization (EHO) algorithm, which is used to regulate cooling modes in three independent temperature zones. The study is designed as a simulation-based proof-of-concept rather than as a full-scale experimental validation on an industrial refrigerated container. The proposed framework evaluates whether an EHO-based controller can generate spatially differentiated cooling decisions under synthetic but controlled disturbance scenarios. The variability of sensor readings reflects conditions typical of long-distance maritime transport. These include transitions across different climate zones, changes in solar exposure, and local differences in thermal load. The simulation results indicate that EHO maintains the temperature within the target range required for pharmaceutical cargo, i.e., 0–8 °C. The algorithm responds effectively to local disturbances and to asymmetry between zones. The proposed model provides a basis for further research on autonomous monitoring and control methods in IoT-based cold chain systems; however, validation using measurements from real refrigerated containers, physical heat-transfer modelling, refrigeration-unit response delays, and IoT communication disturbances remains necessary before operational deployment. Full article

Small- and medium-sized enterprises, especially in the agricultural food sector, struggle to implement end-to-end product traceability systems, such as enterprise resource planning (ERP), due to the high costs and complexity involved for businesses of this scale. As customer expectations and regulatory requirements place an increasing emphasis on traceability and transparency, the combined use of industrial Internet of things (IIoT) technologies and blockchain-based smart contracts offers a promising pathway to cost-effective automation of supply chain processes. This paper develops a conceptual, multi-layer architecture that integrates sensing, communication, integration and smart-contract layers to support affordable, automated and extensible traceability for agri-food supply chains. Building on information processing theory and transaction cost economics, the framework explains how such architecture can reduce information uncertainty, lower monitoring costs and strengthen the organisational trust in agri-food supply chains. The framework is empirically illustrated and tested through an implementation that links distributed sensing infrastructure with a blockchain-based smart contract in a real agricultural supply chain setting. The evaluation assesses operational performance, data integrity and cost-efficiency, demonstrating that the proposed architecture can serve as a viable alternative or most importantly complement to traditional ERP solutions for small- and medium-sized enterprises that seek end-to-end traceability, transparency and automation. Full article

The application of Internet of Things (IoT) technology to Digital Twin (DT) in smart manufacturing has opened significant opportunities for real-time monitoring, predictive maintenance, and closed-loop control; however, the inherent latency that exists in these architectures (the temporal gap between a physical event and its reflection in a digital model) remains one of the most significant and least systematically understood barriers to fulfill its full potential. This paper aims to propose a formal four-layer taxonomy of latency sources in IoT-based Digital Twin systems for smart manufacturing and to review the current approaches and tools that are available for their measurement. The PRISMA protocol has been used to perform a systematic literature review, where 58 primary survey studies published between 2020 and 2026 were extracted from IEEE Xplore, Elsevier Scopus, Google Scholar and arXiv, with all the studies being coded along six dimensions (architectural layer, application domain, latency metrics reported, evaluation methodology, quantitative impact, and enabling technologies). The proposed taxonomy presents 28 different types of latencies under four layers: (L1) network, (L2) compute, (L3) data, and (L4) end-to-end (E2E), whose magnitudes vary from 0.1 ms for local network propagation to tail latencies above 500 ms in production (P99). Three categories and three cross-layer interaction patterns are formalized here and are absent from prior partial taxonomies. Among the most promising results is the finding that several high-impact interventions require no infrastructure investment: a protocol migration from Modbus to WebSocket reduces telemetry latency by 32%, while Age of Information-aware synchronization and clock drift correction deliver substantial data layer gains through software updates alone, yet remain underutilized. The review identifies a systematic under-reporting of tail-latency percentiles across the corpus, the lack of a cross-protocol jitter benchmark, and a predominance of simulation-based evaluation over real-hardware measurement. The systematic review contributions of this paper (the formal four-layer taxonomy, the proportional metric audit across the 58 papers, and the formalization of three cross-layer interaction patterns) are derived from cross-corpus analysis. The investigation also identifies three open research directions (a standardized manufacturing IoT-DT benchmark, cross-layer joint optimization frameworks, and wireless TSN validation on real manufacturing testing grounds) that together form a well-organized and practical basis to advance both the science and the application of ultra-low-latency Digital Twin technology in the industrial field. Full article

Social media has become a significant factor in unhealthy consumption behaviors among adolescents, given the prevalent use of mobile phones and the internet. This study investigates the association between social media exposure and adolescents’ sugar-sweetened beverage (SSB) preferences, as well as their consumption behavior. This study included 1517 adolescents across Henan Province, China, in 2025. We employ a mixed logit model, a hurdle model, and an Ordinary Least Squares (OLS) model to assess the association of social media exposure with adolescents’ SSB preferences and consumption behavior. The findings indicate that social media exposure is positively associated with adolescents’ overall preference for SSB products. Specifically, it is associated with a higher preference for carbonated drinks and beverages containing sweeteners and a lower preference for juice. Furthermore, the association between social media exposure and SSB preferences differs between urban and rural adolescents. Rural adolescents exposed to social media tend to show a lower willingness to forgo SSB options, whereas urban adolescents exposed to social media tend to show less sensitivity to price attributes. Additionally, social media exposure is positively associated with both the selection and consumption of SSBs among adolescents, which in turn are linked to health concerns such as overweight and obesity. Full article

The increasing adoption of Plant Factories with Artificial Lighting (PFAL) has intensified the reliance on Internet of Things (IoT) technologies for real-time monitoring and control of environmental and operational variables. While IoT-based architectures enable precise resource management and productivity optimization, PFAL systems remain highly vulnerable to component failures, sensor malfunctions, communication faults, and energy disruptions, which may compromise crop integrity and system reliability. These risks are particularly critical in low-cost and small-scale PFAL implementations, where maintenance capacity and redundancy are often limited. Existing IoT-based PFAL monitoring systems typically address either hardware or software redundancy in isolation and rarely incorporate a dedicated maintenance-oriented fault detection layer validated under realistic multi-failure scenarios. This study addresses these challenges by proposing a low-cost redundant system architecture for PFAL applications that simultaneously integrates (1) hardware redundancy through multi-sensor configurations; (2) analytical redundancy based on residual generation and threshold-based fault isolation; and (3) a maintenance-oriented fault detection layer capable of identifying abnormal internal device conditions. Experimental validation was conducted using four hardware configurations—Arduino Nano with Ethernet, ESP32, STM32 with Wi-Fi, and STM32 with Ethernet—evaluated across five fault scenarios: dust accumulation, water exposure, high temperature, fire detection, and physical impact. The STM32 with Ethernet configuration consistently achieved the fastest fault detection response times across all tested scenarios. Future work will focus on the integration of machine learning-based predictive maintenance algorithms, multi-node PFAL network deployments, and long-term field validation. Full article

Internet memes increasingly move from social media into physical product packaging, yet little is known about how consumers respond when online cultural symbols become package design cues. Drawing on the Stimulus–Organism–Response framework, this study examines how meme-based packaging shapes purchase intention and sharing intention through perceived value, brand warmth, and cultural resonance. A between-subjects survey experiment was conducted with 305 Chinese adult consumers, who evaluated either a meme-based packaging stimulus or a no-explicit-meme conventional packaging control stimulus. Partial least squares structural equation modeling showed that purchase intention and sharing intention followed different dominant mechanisms. Perceived value was the strongest predictor of purchase intention, whereas cultural resonance was the strongest predictor of sharing intention. Visual attractiveness most strongly enhanced perceived value, while playfulness and expression–product fit contributed more clearly to brand warmth and cultural resonance. Mediation results further showed that brand warmth and cultural resonance consistently transmitted the effects of meme-packaging cues, whereas the value route was more selective. These findings show how online cultural symbols can continue to shape consumer evaluation and social transmission after entering physical product interfaces. Full article

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

In the current Industrial Internet of Things (IIoT) environment, data security for product lifecycle management is greatly challenged, particularly in scenarios involving vertical multi-level Bill of Materials (BOM) deep nesting and lifecycle dynamic evolution. The traditional case-bounding model, in large-scale deployment, easily leads to rule expansion and an increase in database I/O overhead, thus causing authorization lag, authority boundary ambiguity and other problems. To address these limitations, this paper proposes a Decoupled Hybrid Access Resolution (DHAR) framework. The framework separates static organizational roles from dynamic lifecycle constraints, and the complexity of authorization configuration is reconstructed from case-dependent growth into an object-instance-independent bounded structure; combined with the state-based pre-filtering mechanism and memory cache strategy, redundant recursive query is reduced. Experiments on increasing BOM depths show that, under a 20-layer topology, DHAR reduces average access latency from 285.8 ms to 1.3 ms. Under a 20-layer BOM with 1000 concurrent requests, DHAR maintains an average latency of 5.2 ms, while compressing the authorization rule set from millions to hundreds. These results indicate that, within the studied vertical multi-level BOM setting, DHAR improves response performance while preserving data consistency and strengthening protection against unauthorized modification. Full article

Enhancing mariculture’s green total factor productivity (GTFP) is essential to balance industrial growth with ecology, safeguard global food security, and meet UN Sustainable Development Goal 14 amid mounting marine stress. As a global leading mariculture producer, China provides a typical research sample. This study constructs a mariculture GTFP measurement index system, estimates GTFP in China’s coastal provinces via the global Super-SBM model, identifies root causes of efficiency loss, and explores influencing factors and spatial spillover effects using a spatial econometric model. The results show that the overall mariculture GTFP of China’s coastal provinces exhibits a fluctuating upward trend with significant regional heterogeneity, specifically presenting a distribution pattern of “the highest in the South China Sea Region, followed by the East China Sea Region, and the lowest in the Yellow Sea and Bohai Sea Region”. Meanwhile, mariculture GTFP shows significant positive spatial autocorrelation, with distinct High-High and Low-Low agglomeration characteristics. Excessive resource consumption and undesirable output discharge are the core drivers of efficiency loss. For direct effects, industrial scale, industrial structure, fishermen’s income, transportation accessibility, internet development, technology adoption, and environmental regulation significantly boost local GTFP, while fishery disasters exert a significant negative impact. For spatial spillovers, industrial scale, industrial structure, and internet development show significant positive effects, while fishermen’s income and urbanization present negative effects. Based on these findings, this study proposes targeted multi-stakeholder optimization paths, providing decision support for China’s mariculture green development and replicable experience for global coastal countries. Full article

The Architecture, Engineering, and Construction (AEC) industry has significant potential to improve productivity, quality, and sustainability of its projects through emerging digital technologies. Advances in technology and the complexity of what new graduates need to learn have resulted in persistent training gaps and have highlighted new needs to be addressed in education. One of the new needs is the level of learners’ awareness of new technologies and their adoption practices. This research examines how current education and training practices in the selected sample of the Australian AEC sector support or hinder the development of digital capabilities. The set of technologies considered in this study focuses on Artificial Intelligence (AI), Building Information Modelling (BIM), Digital Twins (DTs), Virtual and Augmented Reality (VR/AR), and the Internet of Things (IoT). A mixed-method design integrates a structured survey of industry professionals and students, along with semi-structured interviews of industry and academic stakeholders, to evaluate exposure, self-rated capability, training participation, organisational support, and perceptions of graduate preparedness. Findings show comparatively higher maturity in BIM, but limited capability in other technologies, inconsistent formal training, and barriers linked to time, cost, organisational priorities, and rapid technological change. Qualitative findings and interpretation of preparedness-related survey responses indicate that stakeholders place greater value on transferable, interdisciplinary digital competencies than on narrow tool-specific proficiency. The research delivers statistically robust findings and actionable recommendations that address the identified barriers and promote the development of a skilled workforce in the AEC industry. Full article