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Search Results (279)

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Keywords = Smart, Connected Products

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20 pages, 1279 KiB  
Article
A Framework for Quantifying Hyperloop’s Socio-Economic Impact in Smart Cities Using GDP Modeling
by Aleksejs Vesjolijs, Yulia Stukalina and Olga Zervina
Economies 2025, 13(8), 228; https://doi.org/10.3390/economies13080228 - 6 Aug 2025
Abstract
Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires [...] Read more.
Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Full article
(This article belongs to the Section International, Regional, and Transportation Economics)
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38 pages, 5046 KiB  
Review
Photonics on a Budget: Low-Cost Polymer Sensors for a Smarter World
by Muhammad A. Butt
Micromachines 2025, 16(7), 813; https://doi.org/10.3390/mi16070813 - 15 Jul 2025
Viewed by 581
Abstract
Polymer-based photonic sensors are emerging as cost-effective, scalable alternatives to conventional silicon and glass photonic platforms, offering unique advantages in flexibility, functionality, and manufacturability. This review provides a comprehensive assessment of recent advances in polymer photonic sensing technologies, focusing on material systems, fabrication [...] Read more.
Polymer-based photonic sensors are emerging as cost-effective, scalable alternatives to conventional silicon and glass photonic platforms, offering unique advantages in flexibility, functionality, and manufacturability. This review provides a comprehensive assessment of recent advances in polymer photonic sensing technologies, focusing on material systems, fabrication techniques, device architectures, and application domains. Key polymer materials, including PMMA, SU-8, polyimides, COC, and PDMS, are evaluated for their optical properties, processability, and suitability for integration into sensing platforms. High-throughput fabrication methods such as nanoimprint lithography, soft lithography, roll-to-roll processing, and additive manufacturing are examined for their role in enabling large-area, low-cost device production. Various photonic structures, including planar waveguides, Bragg gratings, photonic crystal slabs, microresonators, and interferometric configurations, are discussed concerning their sensing mechanisms and performance metrics. Practical applications are highlighted in environmental monitoring, biomedical diagnostics, and structural health monitoring. Challenges such as environmental stability, integration with electronic systems, and reproducibility in mass production are critically analyzed. This review also explores future opportunities in hybrid material systems, printable photonics, and wearable sensor arrays. Collectively, these developments position polymer photonic sensors as promising platforms for widespread deployment in smart, connected sensing environments. Full article
(This article belongs to the Section A:Physics)
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72 pages, 22031 KiB  
Article
AI-Enabled Sustainable Manufacturing: Intelligent Package Integrity Monitoring for Waste Reduction in Supply Chains
by Mohammad Shahin, Ali Hosseinzadeh and F. Frank Chen
Electronics 2025, 14(14), 2824; https://doi.org/10.3390/electronics14142824 - 14 Jul 2025
Viewed by 368
Abstract
Despite advances in automation, the global manufacturing sector continues to rely heavily on manual package inspection, creating bottlenecks in production and increasing labor demands. Although disruptive technologies such as big data analytics, smart sensors, and machine learning have revolutionized industrial connectivity and strategic [...] Read more.
Despite advances in automation, the global manufacturing sector continues to rely heavily on manual package inspection, creating bottlenecks in production and increasing labor demands. Although disruptive technologies such as big data analytics, smart sensors, and machine learning have revolutionized industrial connectivity and strategic decision-making, real-time quality control (QC) on conveyor lines remains predominantly analog. This study proposes an intelligent package integrity monitoring system that integrates waste reduction strategies with both narrow and Generative AI approaches. Narrow AI models were deployed to detect package damage at full line speed, aiming to minimize manual intervention and reduce waste. Using a synthetically generated dataset of 200 paired top-and-side package images, we developed and evaluated 10 distinct detection pipelines combining various algorithms, image enhancements, model architectures, and data processing strategies. Several pipeline variants demonstrated high accuracy, precision, and recall, particularly those utilizing a YOLO v8 segmentation model. Notably, targeted preprocessing increased top-view MobileNetV2 accuracy from chance to 67.5%, advanced feature extractors with full enhancements achieved 77.5%, and a segmentation-based ensemble with feature extraction and binary classification reached 92.5% accuracy. These results underscore the feasibility of deploying AI-driven, real-time QC systems for sustainable and efficient manufacturing operations. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Intelligent Manufacturing)
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16 pages, 934 KiB  
Proceeding Paper
Unlocking the Role of Food Processing in Nutrition-Smart and Nutrition-Sensitive Agriculture in West Africa: Challenges, Opportunities, and a Framework for Deployment
by G. Esaïe Kpadonou, Caroline Makamto Sobgui, Rebeca Edoh, Kyky Komla Ganyo, Sedo Eudes L. Anihouvi and Niéyidouba Lamien
Proceedings 2025, 118(1), 17; https://doi.org/10.3390/proceedings2025118017 - 11 Jul 2025
Cited by 1 | Viewed by 364
Abstract
West Africa’s agri-food systems face a triple burden of malnutrition, climate vulnerability, and structural inefficiencies that compromise nutrition and public health. Despite increased attention to food security, agricultural strategies often prioritize yield over dietary quality. This paper explores the critical role of food [...] Read more.
West Africa’s agri-food systems face a triple burden of malnutrition, climate vulnerability, and structural inefficiencies that compromise nutrition and public health. Despite increased attention to food security, agricultural strategies often prioritize yield over dietary quality. This paper explores the critical role of food processing in advancing Nutrition-Sensitive Agriculture (NSA) and Nutrition-Smart Agriculture (NSmartAg) across West Africa. Drawing on a systems lens, it positions food processing not as a peripheral activity, but as a catalytic mechanism that connects nutrient-dense production with improved consumption outcomes. Food processing can reduce post-harvest losses, preserve micronutrients, extend food availability, and foster inclusive value chains particularly for women and youth. Yet, persistent challenges remain, including institutional fragmentation, infrastructure gaps, and limited financial and technical capacity. This paper proposes a conceptual framework linking food processing to NSA and NSmartAg objectives and outlines operational entry points for implementation. By integrating processing into agricultural policies, investment, education, and monitoring systems, stakeholders and policymakers can reimagine agriculture as a platform for resilience and nutritional equity. Strategic recommendations emphasize multisectoral collaboration, localized solutions, and evidence-informed interventions to drive the transformation toward sustainable, nutrition-oriented food systems. Full article
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20 pages, 2517 KiB  
Article
Transformation of Shipbuilding into Smart and Green: A Methodology Proposal
by Zoran Kunkera, Nataša Tošanović and Neven Hadžić
Eng 2025, 6(7), 148; https://doi.org/10.3390/eng6070148 - 1 Jul 2025
Viewed by 302
Abstract
Since the beginning of the last decade, digital technological achievements have ushered the economies of developed countries into the fourth industrial revolution, transforming industries into smart ones, referred to as “Industry 4.0”, enabling them to innovate as a prerequisite for sustainable development and [...] Read more.
Since the beginning of the last decade, digital technological achievements have ushered the economies of developed countries into the fourth industrial revolution, transforming industries into smart ones, referred to as “Industry 4.0”, enabling them to innovate as a prerequisite for sustainable development and economic growth. At the same time, the European Union’s institutions are adopting strategies and programs to transform the European industry into a climate-neutral one, aiming to achieve this by 2050. The authors, participating in the introduction of Lean tools and digital technologies into one of the European shipyards using the “CULIS” (Connect Universal Lean Improvement System) methodology, recognize the high potential of its contribution to the European Commission’s guidelines for transitioning the economy to a sustainable one, and for this purpose, they present it in this paper. Namely, the methodology in question not only theoretically results in a “quick” implementation of tools and doctrines—with an approximately 36-month total duration of the process—but also encompasses as many as three transformations: Lean, digital, and green; an analysis of a methodology with such characteristics significantly adds to the originality of this study. The current stage of the observed shipyard’s “triple” transformation process already results in significant improvements—e.g., an increase in productivity by around 21% or a reduction in sales process costs by 38%. However, given its ongoing pilot phase, (further) analyses of improvements in (European) shipbuilding competitiveness and profitability that can be achieved through digital Lean management of projects’ realization process are implied. Full article
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24 pages, 5056 KiB  
Article
Lattice-Hopping: A Novel Map-Representation-Based Path Planning Algorithm for a High-Density Storage System
by Shuhan Zhang, Yaqing Song, Ziyu Chen, Guo Chen, Yongxin Cao, Zhe Gao and Xiaonong Xu
Appl. Sci. 2025, 15(12), 6764; https://doi.org/10.3390/app15126764 - 16 Jun 2025
Viewed by 319
Abstract
Optimal path planning algorithms offer substantial benefits in high-density storage (HDS) systems in modern smart manufacturing. However, traditional algorithms may encounter significant optimization challenges due to intricate architectural configurations and traffic constraints of the HDS system. This paper addresses these issues by introducing [...] Read more.
Optimal path planning algorithms offer substantial benefits in high-density storage (HDS) systems in modern smart manufacturing. However, traditional algorithms may encounter significant optimization challenges due to intricate architectural configurations and traffic constraints of the HDS system. This paper addresses these issues by introducing a two-step novel path planning method: (1) the mesh-tree grid map topological representation and the (2) Lattice-Hopping (LH) algorithm. The proposed method first converts the layout of an HDS system into a mesh-tree grid hierarchical structure by capturing and simplifying the spatial and geometrical information as well as the traffic constraints of the HDS system. Then, the LH algorithm is proposed to find optimal shipping path by leveraging the global connectivity of main tracks (main track priority) and the ‘jumping’ mechanism of sub-tracks. The main track priority and the ‘jumping’ mechanism work together to save computational complexity and enhance the feasibility and optimality of the proposed method. Numerical and case studies are performed to demonstrate the superiorities of our method to properly modified benchmark algorithms. Algorithm scalability, robustness, and operational feasibility for industrial production in modern smart manufacturing are also displayed and emphasized. Full article
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8 pages, 2202 KiB  
Conference Report
The 11th International Congress on Biocatalysis (biocat2024), Hamburg, Germany, 25–29 August 2024
by Victoria Bueschler, Paul Bubenheim, Barbara Klippel, Ana Malvis Romero, Daniel Ohde, Anna-Lena Heins, Johannes Gescher, Franziska Rohweder and Andreas Liese
Catalysts 2025, 15(6), 574; https://doi.org/10.3390/catal15060574 - 10 Jun 2025
Viewed by 662
Abstract
The “11th International Congress on Biocatalysis (biocat2024)” was part of a biennial series that unites the fields of biology and chemistry, attracting researchers from the life sciences, engineering, and computer science. This international forum provides an opportunity for scientists worldwide to connect, seek [...] Read more.
The “11th International Congress on Biocatalysis (biocat2024)” was part of a biennial series that unites the fields of biology and chemistry, attracting researchers from the life sciences, engineering, and computer science. This international forum provides an opportunity for scientists worldwide to connect, seek collaboration for future projects, and gain insights into contemporary topics and innovative techniques. Biocat covers a range of compelling subjects and recent advancements in biocatalysis, including enzyme discovery, evolution, and applications. This congress focused on six key topics: AI and computational methods, structure–function analysis and enzyme engineering, enzymatic and whole-cell biotransformations, reaction cascades (electro-, chemo-, and photoenzymatic synergies), bioprocess engineering and the design of smart reactors, and facing climate change through sustainability and a circular bioeconomy. In 2024, we welcomed 344 expert delegates alongside 21 internal attendees, including 154 women and 1 non-binary participant, bringing the total number of participants to an impressive 365. Established researchers and emerging scientists from academia and industry delivered a total of 119 presentations, comprising 59 standard lectures, 60 lightning talks, and 195 posters. Six industry exhibitors showcased their latest products and services, providing an excellent opportunity to strengthen the connection between science and industry. Furthermore, the biocat award, recognized as one of the most prestigious honors in biotechnology, was presented for the eleventh time in the categories of “Science in Academia”, “Lifetime Achievement,” and “Industry”. Full article
(This article belongs to the Section Biocatalysis)
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25 pages, 4931 KiB  
Article
Real-Time Maintenance Optimization with Industrial Internet of Things
by Tamás Bányai and Ágota Bányai
Appl. Sci. 2025, 15(10), 5640; https://doi.org/10.3390/app15105640 - 18 May 2025
Cited by 1 | Viewed by 751
Abstract
Efficient maintenance management is critical to ensuring the reliability and productivity of industrial systems. This article explores how the Industrial Internet of Things (IIoT) enables real-time maintenance optimization through data-driven decision-making. IIoT technologies, such as connected smart sensors and predictive analytics, provide continuous [...] Read more.
Efficient maintenance management is critical to ensuring the reliability and productivity of industrial systems. This article explores how the Industrial Internet of Things (IIoT) enables real-time maintenance optimization through data-driven decision-making. IIoT technologies, such as connected smart sensors and predictive analytics, provide continuous monitoring of equipment performance and state. Within the frame of this article, a novel mathematical model is proposed to support the real-time optimization of maintenance operations in production systems. The model makes this possible by using real-time state information to optimize maintenance operations, minimize maintenance costs, and maximize the efficiency of the production system. The results highlight the potential of IIoT to transform conventional maintenance strategies into dynamic, adaptive systems. This research contributes to advancing smart maintenance solutions for modern industrial applications. Full article
(This article belongs to the Special Issue Applications of Industrial Internet of Things (IIoT) Platforms)
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19 pages, 6390 KiB  
Article
AI-Based Smart Monitoring Framework for Livestock Farms
by Moonsun Shin, Seonmin Hwang and Byungcheol Kim
Appl. Sci. 2025, 15(10), 5638; https://doi.org/10.3390/app15105638 - 18 May 2025
Viewed by 1285
Abstract
Smart farms refer to spaces and technologies that utilize networks and automation to monitor and manage the environment and livestock without the constraints of time and space. As various devices installed on farms are connected to a network and automated, farm conditions can [...] Read more.
Smart farms refer to spaces and technologies that utilize networks and automation to monitor and manage the environment and livestock without the constraints of time and space. As various devices installed on farms are connected to a network and automated, farm conditions can be observed remotely anytime and anywhere via smartphones or computers. These smart farms have evolved into smart livestock farming, which involves collecting, analyzing, and sharing data across the entire process from livestock production and growth to post-shipment distribution and consumption. This data-driven approach aids decision-making and creates new value. However, in the process of evolving smart farm technology into smart livestock farming, challenges remain in the essential requirements of data collection and intelligence. Many livestock farms face difficulties in applying intelligent technologies. In this paper, we propose an intelligent monitoring system framework for smart livestock farms using artificial intelligence technology and implement deep learning-based intelligent monitoring. To detect cattle lesions and inactive individuals within the barn, we apply the RT-DETR method instead of the traditional YOLO model. YOLOv5 and YOLOv8 are representative models in the YOLO series, both of which utilize Non-Maximum Suppression (NMS). NMS is a postprocessing technique used to eliminate redundant bounding boxes by calculating the Intersection over Union (IoU) between all predicted boxes. However, this process can be computationally intensive and may negatively impact both speed and accuracy in object detection tasks. In contrast, RT-DETR (Real-Time Detection Transformer) is a Transformer-based real-time object detection model that does not require NMS and achieves higher accuracy compared to the YOLO models. Given environments where large-scale datasets can be obtained via CCTV, Transformer-based detection methods like RT-DETR are expected to outperform traditional YOLO approaches in terms of detection performance. This approach reduces computational costs and optimizes query initialization, making it more suitable for the real-time detection of cattle maintenance behaviors and related abnormal behavior detection. Comparative analysis with the existing YOLO technique verifies RT-DETR and confirms that RT-DETR shows higher performance than YOLOv8. This research contributes to resolving the low accuracy and high redundancy of traditional YOLO models in behavior recognition, increasing the efficiency of livestock management, and improving productivity by applying deep learning to the smart monitoring of livestock farms. Full article
(This article belongs to the Special Issue Future Information & Communication Engineering 2024)
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25 pages, 2855 KiB  
Article
A Needs-Based Design Method for Product–Service Systems to Enhance Social Sustainability
by Hidenori Murata and Hideki Kobayashi
Sustainability 2025, 17(8), 3619; https://doi.org/10.3390/su17083619 - 17 Apr 2025
Viewed by 563
Abstract
This study proposes a design method for the evaluation and redesign of product–service systems (PSSs) from the perspective of social sustainability, one that applies Max-Neef’s framework of fundamental human needs. The proposed method systematically connects PSS functions and requirements—identified through service blueprints and [...] Read more.
This study proposes a design method for the evaluation and redesign of product–service systems (PSSs) from the perspective of social sustainability, one that applies Max-Neef’s framework of fundamental human needs. The proposed method systematically connects PSS functions and requirements—identified through service blueprints and value graphs—to “satisfiers” and “barriers” extracted via needs-based workshops. This connection enables the identification of functions that either contribute to or hinder the fulfillment of fundamental human needs and guide the generation of redesign proposals aimed at sufficiency-oriented outcomes. A case study involving a smart-cart system in Osaka, Japan, was conducted to demonstrate the applicability of the method. Through an online workshop, satisfiers and barriers related to both physical and online shopping experiences were identified. The analysis revealed that existing functions such as promotional information and automated checkout processes negatively impacted needs such as understanding and affection due to information overload and reduced human interaction. In response, redesign concepts were developed, including filtering options for information, product background storytelling, and optional slower checkout lanes with human assistants. The redesigned functions contribute to the fulfillment of fundamental human needs, indicating that the proposed method can enhance social sustainability in PSS design. This study offers a novel framework that extends beyond traditional customer requirement-based approaches by explicitly incorporating human needs into function-level redesign. Full article
(This article belongs to the Special Issue Smart Product-Service Design for Sustainability)
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24 pages, 8329 KiB  
Article
Leveraging Deep Learning and Internet of Things for Dynamic Construction Site Risk Management
by Li-Wei Lung, Yu-Ren Wang and Yung-Sung Chen
Buildings 2025, 15(8), 1325; https://doi.org/10.3390/buildings15081325 - 17 Apr 2025
Cited by 2 | Viewed by 1161
Abstract
The construction industry faces persistent occupational health and safety challenges, with numerous risks arising from construction sites’ complex and dynamic nature. Accidents frequently result from inadequate safety distances and poorly managed work-er–machine interactions, highlighting the need for advanced safety management solutions. This study [...] Read more.
The construction industry faces persistent occupational health and safety challenges, with numerous risks arising from construction sites’ complex and dynamic nature. Accidents frequently result from inadequate safety distances and poorly managed work-er–machine interactions, highlighting the need for advanced safety management solutions. This study develops and validates an innovative hazard warning system that leverages deep learning-based image recognition (YOLOv7) and Internet of Things (IoT) modules to enhance construction site safety. The system achieves a mean average precision (mAP) of 0.922 and an F1 score of 0.88 at a 0.595 confidence threshold, detecting hazards in under 1 s. Integrating IoT-enabled smart wearable devices provides real-time monitoring, delivering instant hazard alerts and personalized safety warnings, even in areas with limited network connectivity. The system employs the DIKW knowledge management framework to extract, transform, and load (ETL) high-quality labeled data and optimize worker and machinery recognition. Robust feature extraction is performed using convolutional neural networks (CNNs) and a fully connected approach for neural network training. Key innovations, such as perspective projection coordinate transformation (PPCT) and the security assessment block module (SABM), further enhance hazard detection and warning generation accuracy and reliability. Validated through extensive on-site experiments, the system demonstrates significant advancements in real-time hazard detection, improving site safety, reducing accident rates, and increasing productivity. The integration of IoT enhances scalability and adaptability, laying the groundwork for future advancements in construction automation and safety management. Full article
(This article belongs to the Special Issue Data Analytics Applications for Architecture and Construction)
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18 pages, 958 KiB  
Article
Industrial-Grade Edge Computing Device for Smart Furniture Products
by Damir Nozica, Damir Blazevic and Tomislav Keser
Appl. Sci. 2025, 15(8), 4355; https://doi.org/10.3390/app15084355 - 15 Apr 2025
Cited by 1 | Viewed by 779
Abstract
As Internet of Things (IoT), communication convergence, and distributed computing are maturing, many new solutions are invented or derived every day, bringing new products and services to society. This paper elaborates on an industrial-grade information technology (IT) component upgrade to an existing ESP32 [...] Read more.
As Internet of Things (IoT), communication convergence, and distributed computing are maturing, many new solutions are invented or derived every day, bringing new products and services to society. This paper elaborates on an industrial-grade information technology (IT) component upgrade to an existing ESP32 IoT device and the addition of new data communication layers to its capabilities. The aim of this research is to explore and propose new IT products to upgrade the first-generation smart furniture product. The proposed upgrades postulate a technological leap ahead in terms of (1) connectivity; (2) connection security; (3) product life-cycle management; and (4) integration (including sustainable product development). A distinct difference from the existing solution is presented in the aforementioned categories. The research points to a possible product that accommodates requirements and a niche of products that can be used, while eliminating other form factors. The process itself proves a point in the favor of the chosen IT product, providing confirmation of the hypotheses. Even though software and application security are not focal points of the discussion in this paper, these elements are generally presented as their presence is necessary in the modern convergence of communication methods. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)
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35 pages, 3981 KiB  
Review
Challenges and Solution Directions for the Integration of Smart Information Systems in the Agri-Food Sector
by Emmanuel Ahoa, Ayalew Kassahun, Cor Verdouw and Bedir Tekinerdogan
Sensors 2025, 25(8), 2362; https://doi.org/10.3390/s25082362 - 8 Apr 2025
Cited by 1 | Viewed by 1408
Abstract
Traditional farming has evolved from standalone computing systems to smart farming, driven by advancements in digitalization. This has led to the proliferation of diverse information systems (IS), such as IoT and sensor systems, decision support systems, and farm management information systems (FMISs). These [...] Read more.
Traditional farming has evolved from standalone computing systems to smart farming, driven by advancements in digitalization. This has led to the proliferation of diverse information systems (IS), such as IoT and sensor systems, decision support systems, and farm management information systems (FMISs). These systems often operate in isolation, limiting their overall impact. The integration of IS into connected smart systems is widely addressed as a key driver to tackle these issues. However, it is a complex, multi-faceted issue that is not easily achievable. Previous studies have offered valuable insights, but they often focus on specific cases, such as individual IS and certain integration aspects, lacking a comprehensive overview of various integration dimensions. This systematic review of 74 scientific papers on IS integration addresses this gap by providing an overview of the digital technologies involved, integration levels and types, barriers hindering integration, and available approaches to overcoming these challenges. The findings indicate that integration primarily relies on a point-to-point approach, followed by cloud-based integration. Enterprise service bus, hub-and-spoke, and semantic web approaches are mentioned less frequently but are gaining interest. The study identifies and discusses 27 integration challenges into three main areas: organizational, technological, and data governance-related challenges. Technologies such as blockchain, data spaces, AI, edge computing and microservices, and service-oriented architecture methods are addressed as solutions for data governance and interoperability issues. The insights from the study can help enhance interoperability, leading to data-driven smart farming that increases food production, mitigates climate change, and optimizes resource usage. Full article
(This article belongs to the Special Issue Leveraging IoT Technologies for the Future Smart Agriculture)
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21 pages, 7550 KiB  
Article
ECOTIRE: A New Concept of a Smart and Sustainable Tire Based on a Removable Tread
by Daniel Garcia-Pozuelo, Farshad Afshari, Ramon Gutierrez-Moizant and Miguel A. Martínez
Appl. Sci. 2025, 15(7), 3675; https://doi.org/10.3390/app15073675 - 27 Mar 2025
Cited by 1 | Viewed by 623
Abstract
This paper introduces a new concept of a smart and sustainable tire based on a removable tread band: ECOTIRE. Current tires, though crucial for road information and vehicle control, such as braking, traction, and turning, remain disconnected from Advanced Driver Assistance Systems (ADAS). [...] Read more.
This paper introduces a new concept of a smart and sustainable tire based on a removable tread band: ECOTIRE. Current tires, though crucial for road information and vehicle control, such as braking, traction, and turning, remain disconnected from Advanced Driver Assistance Systems (ADAS). Additionally, their production, use, and recycling pose significant environmental challenges, requiring sustainable materials and lifecycle improvements. The ECOTIRE concept makes it possible to separate the part of the tire subject to wear and apply new materials with reduced environmental impact. At the same time, the service life of the casing is extended, facilitating the introduction of sensors that improve vehicle safety. This study explores the purely mechanical connection between the casing and tread, demonstrating the feasibility of this innovative tire structure while eliminating the need for rubber matrix-based materials for a proper bond between the two components. Experimental tests using a rubber sample to simulate the tire–road contact patch validate the effectiveness of the mechanical link under varying normal loads. Grip test results, measuring longitudinal and lateral forces, show promising performance. This advancement in tire technology marks a first step toward sustainability, tire performance, and smart integration, ultimately reducing environmental impact. Full article
(This article belongs to the Section Transportation and Future Mobility)
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34 pages, 14344 KiB  
Article
FedBirdAg: A Low-Energy Federated Learning Platform for Bird Detection with Wireless Smart Cameras in Agriculture 4.0
by Samy Benhoussa, Gil De Sousa and Jean-Pierre Chanet
AI 2025, 6(4), 63; https://doi.org/10.3390/ai6040063 - 21 Mar 2025
Viewed by 988
Abstract
Birds can cause substantial damage to crops, directly affecting farmers’ productivity and profitability. As a result, detecting bird presence in crop fields is crucial for effective crop management. Traditional agricultural practices have used various tools and techniques to deter pest birds, while digital [...] Read more.
Birds can cause substantial damage to crops, directly affecting farmers’ productivity and profitability. As a result, detecting bird presence in crop fields is crucial for effective crop management. Traditional agricultural practices have used various tools and techniques to deter pest birds, while digital agriculture has advanced these efforts through Internet of Things (IoT) and artificial intelligence (AI) technologies. With recent advancements in hardware and processing chips, connected devices can now utilize deep convolutional neural networks (CNNs) for on-field image classification. However, training these models can be energy-intensive, especially when large amounts of data, such as images, need to be transmitted for centralized model training. Federated learning (FL) offers a solution by enabling local training on edge devices, reducing data transmission costs and energy demands while also preserving data privacy and achieving shared model knowledge across connected devices. This paper proposes a low-energy federated learning framework for a compact smart camera network designed to perform simple image classification for bird detection in crop fields. The results demonstrate that this decentralized approach achieves performance comparable to a centrally trained model while consuming at least 8 times less energy. Further efficiency improvements, with a minimal tradeoff in performance reduction, are explored through early stopping. Full article
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
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