Search Results (237)

Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances across the tillage–sowing–management–harvesting (TSMH) workflow, covering intelligent tillage, precision sowing, field management, and robotic harvesting. The literature shows that AI has significantly improved agricultural perception, prediction, and task-level decision-making. However, large-scale adoption remains constrained by data heterogeneity, limited cross-scene generalization, environmental uncertainty, and insufficient integration across operational stages. Future progress will depend on multimodal data fusion, lightweight and interpretable models, cloud-edge collaboration, and full-chain decision architectures. By framing current research within the TSMH pipeline, this review highlights both technical advances and the critical bottlenecks that must be addressed to move smart agriculture from stage-specific intelligence toward system-level autonomy. Representative studies indicate that AI models can improve soil-property prediction and reduce sowing miss-detection rates to below 3% under controlled or bench-top conditions. However, field deployment may be affected by environmental variability, including illumination changes, dust, vibration, occlusion, and hardware constraints. These limitations highlight the need for robust and edge-compatible architectures. Full article

This work examines the contribution of additive manufacturing as an enabling technology in the design and development of smart and sustainable construction systems, with particular emphasis on nature-based solutions. While the existing literature has devoted considerable attention to the material properties of additive manufacturing, much less emphasis has been placed on its role in design processes, prototyping, and decision-making in construction and urban systems. To address this gap, this study presents a comprehensive bibliometric analysis of the intersection between smart city frameworks and 3D printing technologies, utilizing a dataset of 103 peer-reviewed publications retrieved from the Scopus database. Using keyword co-occurrence analysis and network mapping through VOSviewer, this study identifies dominant thematic structures, core research hubs, and evolving trends within the field. Complementing this bibliometric analysis with qualitative synthesis, it also reveals a significant convergence of digital design, smart cities, and sustainability strategies. This work further highlights the contribution of additive manufacturing to design processes through rapid prototyping, customization, and the exploration of design alternatives. Rather than framing additive manufacturing as a replacement for conventional design practices, this study positions it as a complementary design capability that can enhance the design process, while also acknowledging important challenges related to scaling, regulation, and integration into construction workflows. This review concludes by outlining future research directions for strengthening the design-oriented integration of additive manufacturing within smart construction systems. Full article

Despite the operational phase being the most cost-intensive in a building’s lifecycle, Facility Management (FM) resource optimization continues to face challenges due to fragmented and low-structured data. Building Information Modeling (BIM) offers a centralized data environment, but interoperability gaps persist between design-oriented BIM models and operational Computerized Maintenance Management Systems (CMMSs). This paper presents a scalable, standards-based methodology for BIM-CMMS integration based on the extension of Industry Foundation Classes (IFCs) and the enrichment of FM data. The proposed Python-based application leverages the open-source IfcOpenShell library to inject custom, FM-specific Property Sets (Psets), including asset condition, criticality, and maintenance schedules, directly into IFC entities. The approach transforms standard IFC files into data-rich Asset Information Models (AIMs) without relying on proprietary middleware. The methodology was validated through two residential building case studies. IFC models were successfully checked through the buildingSMART validation service, providing full interoperability across multiple IFC-compatible platforms. Integration with OpenMAINT automatically generates a complete asset database, minimizing manual data entry and reducing inconsistencies. The results confirm the feasibility of a repeatable open-standard workflow. The future development is the definition of a functional/cognitive DT, with the scope of improving the lifecycle BIM model quality and enhancing the efficiency of facility operations. Full article

While integrating blockchain technology into Business Process Management (BPM) has gained attention, existing compilation-based approaches suffer from high redeployment costs and isolated network structures. This study proposes an FSM-based workflow interpreter engine utilizing the Inter-Blockchain Communication (IBC) protocol within the Cosmos ecosystem to overcome these limitations. The proposed system adopts an interpreter architecture that treats business logic as lightweight JSON specifications instead of hard-coding it into smart contracts. This separation allows for process updates through data modification rather than contract redeployment, significantly increasing operational flexibility. Furthermore, custom IBC packet structures were designed to enable seamless cross-chain process synchronization between independent application-specific blockchains. Experimental results demonstrate that the interpreter approach reduces process update costs by over 90% compared to conventional compilation methods. Additionally, gas consumption exhibited a linear growth pattern relative to task count and gateway complexity, ensuring cost predictability for large-scale business scenarios. Interoperability validation using a standard Procurement Order (PO) process showed successful cross-chain state transitions with a latency of approximately 1.45 s. This research provides a practical solution for building trust-based decentralized collaboration ecosystems by simultaneously achieving operational efficiency and interoperability in blockchain BPM. Full article

Digital Twins (DTs) are increasingly deployed in Industry 4.0 to enable real-time monitoring, analysis, and control, yet the transition from isolated DT instances to plant-wide ecosystems across cloud and edge infrastructures introduces fragmentation and coordination challenges among heterogeneous assets, data sources, and services. This paper addresses this gap by proposing a cloud-native Digital Twin Computing Layer (DTCL) that provides a unified control and orchestration plane for composing and operating DT applications in Smart Manufacturing. The DTCL is designed as a three-tier architecture comprising a developer-facing user interface, a Deploy Engine for automated deployment and lifecycle management, and a Service Catalog of reusable, independently deployable microservices. Standardized interaction is supported through semantic DT models and API- and message-based communication mechanisms. A governance workflow, based on service discovery and validation, is introduced to support non-redundant integration and controlled evolution of services. The approach is demonstrated through a Smart Manufacturing predictive maintenance case study and further extended with a Smart Mobility scenario for urban public transport planning, highlighting the flexibility of the DTCL across different application domains. Overall, the DTCL supports modular composition, interoperability, and lifecycle governance across heterogeneous Digital Twin applications, providing a scalable foundation for both industrial and urban data-driven scenarios. Full article

This work presents the design, development, and proof-of-concept validation of a smart greenhouse for romaine lettuce (Lactuca sativa var. longifolia) that integrates Internet of Things (IoT) sensing/actuation with an image-based crop state assessment pipeline. The proposed pipeline combines a lightweight AI image classifier with fractal texture descriptors (box-counting fractal dimension) to support the non-destructive monitoring of leaf condition and growth stage. The system also implements resilience-oriented resource strategies, including rainwater harvesting, graywater reuse, and a hybrid power supply (photovoltaic + grid backup). Water and energy indicators are reported as estimated values derived from the prototype operating profile and literature-based baseline values (i.e., contextual comparisons rather than a contemporaneous controlled trial). Using an expanded dataset (n = 1500 images) and an independent held-out test subset (n = 350), the image classifier achieved 97.1% accuracy, with detailed precision/recall/F1 metrics reported in the Results. Overall, the proposed architecture and evaluation workflow provide an accessible and reproducible pathway toward sustainable, low-cost smart greenhouses in resource-constrained settings. Full article

Two-dimensional (2D) barcodes are now embedded in payment platforms, authentication workflows, industrial traceability, smart packaging, and mobile information services. Their ubiquity has simultaneously increased the incentive for phishing, tampering, and malicious redirection, while recent RGB-colored barcode designs have introduced a second challenge: maintaining reliable payload recovery under non-ideal capture conditions. This study presents a unified framework for secure and recoverable RGB-colored 2D barcodes across QR Code, Data Matrix, Aztec, and PDF417 symbologies. The framework combines channel-separated RGB encoding, lightweight hybrid cryptographic protection, and pretrained vision-based validation to jointly improve confidentiality, authenticity, and operational trust. A recoverability-oriented evaluation protocol is introduced to quantify robustness under distance variation, angular distortion, illumination change, blur, and color shift. Experimental results show that compact schemes based on ChaCha20-Poly1305 and Ed25519 achieve the most favorable trade-off between security overhead and decoding reliability, while EfficientNet-B0 offers the best deployment balance among the evaluated vision backbones. Data Matrix and Aztec exhibit the strongest maximum reliable distance under the tested conditions. The results indicate that secure barcode design cannot be treated as a purely cryptographic or purely visual problem; instead, practical deployment benefits from a layered architecture in which cryptography, computer vision, and recoverability metrics are optimized together. Full article

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows. Full article

The integration of artificial intelligence with unobtrusive sensing technologies is transforming occupational health monitoring by enabling continuous, objective assessment of worker activities in real industrial environments. This study focuses on the accurate recognition of subtle motion actions within logistics workflows using multichannel optical motion-capture data. We investigate several deep learning architectures commonly employed for temporal motion analysis, including tCNN, Transformer, CNN–LSTM, and ConvLSTM. To enhance robustness and fairness across workers with varying movement styles, a subject-independent evaluation protocol is adopted, and a multiscale temporal learning strategy is explored to better capture fine-grained and low-saliency actions. Experimental results show that the proposed multiscale tCNN achieves the highest accuracy, obtaining per-class recall range between 73% and 83% and an overall accuracy of approximately 79%, consistently outperforming recurrent and attention-based architectures. These findings demonstrate the effectiveness of multiscale convolution-based temporal modeling for recognizing subtle motion actions and highlight the potential of combining optical motion capture with AI analytics to support unobtrusive, reliable occupational health monitoring in smart industry environments. Full article

Background: Radish seeds are our model on glucosinolates (GSLs), which is a class of secondary metabolites in medicinal plants of the Brassicaceae family. Multilayer perceptron (MLP) network is highly effective in the study of complex plants. This study came up with a smart plan through the Python language. Methods: First, we used the MLP network to pick out GSL precursor ions, running them through a deep learning filter. Next, we set up an automated screening system and looked at how standard chemicals break down. To speed things up, we created a scoring system that flagged promising compounds. After that, we built a tracer molecular network, basically connecting compounds according to how the plant makes them, which helped us label everything accurately. Finally, we brought in a math-based tool that pieces together different chemical parts to predict new GSL structures. Results: With this workflow, we annotated 195 glucosinolate-related compounds in radish seeds. That includes 86 regular GSLs, 34 malonyl products, 40 sinapoyl compounds, and 35 diglycosides. Among them, eight compounds were confirmed by comparison with authentic standards (retention time and MS/MS data), whereas the remaining compounds were tentatively annotated based on accurate mass measurements, diagnostic fragment ions, Tracer Molecular Nnetworking, and literature/database matching. In addition, 36 compounds were considered putatively novel derivatives pending further structural confirmation. Conclusions: This new approach reduces the time spent on determining chemicals in complicated samples. This can be done with other vegetables and medicinal herbs by researchers. It assists us in knowing the chemistry of plants in a deeper manner. Full article

Artificial intelligence (AI), cloud computing, robotics, automation, and remote sensing technologies are all contributing to digital transformation in forestry. Improving on low-accuracy Global Navigation Satellite Systems (GNSS) positioning affected by multipath error and interception under forest canopies is critical for integrating smart and digital technologies into equipment in forest operations. In an era where lidar-derived individual tree locations are now increasingly available in digital forest inventories, a possible alternative approach to positioning resources such as people or equipment accurately could be to match locally-measured tree positions and attributes in the forest with an existing global reference map based on prior remote sensing missions, effectively using the trees themselves as satellites to circumvent the need for GNSS-based positioning. We evaluated a lidar-based alternative to GNSS positioning using predicted tree positions from local terrestrial laser scanning (TLS) matched with a global stem map derived from prior airborne laser scanning (ALS), a methodology we refer to as TreePS. The horizontal error of the TreePS system was estimated using 154 permanent single-tree inventory plots on the University of Idaho Experimental Forest with two different workflows based on two common R packages (lidR v. 4.3.0, FORTLS v. 1.6.2) using either spatial coordinates or spatial plus stem DBH predicted using one or both segmentation routines and a custom matching algorithm. Mean TreePS error using lidR for below and above-canopy segmentation had mean error of 1.04 and 2.04 m with 93.5% and 91.6% of plots with viable match solutions on spatial and spatial plus DBH matching. The second workflow with both FORTLS (TLS point cloud) and lidR (ALS point cloud) had errors of 1.09 and 2.67 m but only 57.9% and 54.2% of plots with solutions using spatial and spatial plus DBH, respectively. There is room for improvement in the matching algorithm but the TreePS methodology and similar feature-matching solutions may be useful for below-canopy positioning of equipment, people or other resources under dense forests and other GNSS-degraded environments to help advance smart and digital forestry. Full article

Background/Objectives: The rapidly expanding landscape of digital technologies is transforming innovation processes across industries, and the food sector is increasingly encouraged to adopt novel tools that can enhance development workflows and support competitive positioning. In the context of Industry 4.0, it is particularly important to examine open innovation approaches that may increase the efficiency of engineers and researchers involved in the research and development of food supplements. Such approaches enable broader access to relevant scientific information, including new bioactive ingredient research and their physiological implications, potentially contributing to the development of better-informed and higher-quality products. Methods: In the present study, we evaluated the deep research capabilities of several popular large language models to assess their suitability for supporting the conceptual design of a blood glucose-optimizing food supplement intended for prediabetes management. The comparative analysis focused on the level of detail in the outputs generated by each model, the robustness of the conclusions drawn, and the capacity to produce formulation-oriented recommendations grounded in scientific literature and regulatory frameworks. Our evaluation was primarily qualitative and subjective, highlighting both the potential and limitations of these models. Moreover, the study outlines a forward-looking concept for product validation using wearable smart devices and medically certified wearable devices with continuous biometric monitoring, which could provide an innovative avenue for assessing supplement efficacy. Results: The findings indicate that large language models can support the collection, organization, and preliminary interpretation of complex scientific information. Conclusions: Nevertheless, expert input remains essential for accurate evaluation, scientific validation, and regulatory compliance, as these models cannot yet replace domain expertise or rigorous experimentation in food supplement development. Full article

Within the hospital sector, digitalization brings smarter, more resilient and more sustainable systems. Advancements in remote sensing technologies and building information modeling (BIM) are revolutionizing infrastructure design and construction. The aim of the study is to investigate the impact of digitalization on the spatial configuration of hospitals and its effects on operational efficiency and environmental sustainability, combining theoretical insights with an empirical survey of fourteen hospitals in Italy. The methodology adopted consisted of the following steps: (i) the conduct of a literature review; (ii) the analysis of international best practice; (iii) the definition of criteria to support the design of digital hospitals; (iv) the investigation on the Italian context through a survey; (v) data collection and analysis to support the formulation of strategies for smart hospital development. The findings highlight how the adoption of innovative solutions related to clinical and management sector can optimize hospital workflow, enhance management efficiency, and create safer and more functional and sustainable environments. However, the persistence of outdated infrastructures and the need for significant adaptation still represent major barriers: only 28.7% of hospitals have a fully centralized logistics hub, and just 7.1% have implemented a Digital Twin. In conclusion, this research provides a reference framework for designers, healthcare administrators, and policymakers, outlining strategies for the development of smart and sustainable hospitals. Full article

This study presents ZK-EHR, a decentralized access control framework designed to enable secure and privacy-preserving sharing of encrypted electronic health records across institutional boundaries. Unlike existing blockchain-based EHR access control systems that expose user identities on-chain or lack cryptographic privacy guarantees, ZK-EHR decouples authorization from identity disclosure by integrating zk-SNARK-based proofs with blockchain smart contracts to verify policy compliance without revealing user roles, affiliations, or credentials. The framework employs three differentiated actor roles—Patient (Data Owner), Doctor (Care Provider), and Researcher (Authorized Analyst)—with distinct policy-driven access workflows, a custom Groth16 zero-knowledge circuit for role-based constraint enforcement, and a modular architecture combining on-chain verification with off-chain encrypted storage via IPFS. Concrete design proposals for access revocation and replay attack prevention are introduced to address operational security requirements. The system was evaluated under multiple operational and adversarial scenarios. Experimental results indicate consistent on-chain verification latency (approximately 390 ms), reliable rejection of tampered submissions, and per-verification gas consumption of 216,631 gas. A comparative analysis against representative baseline systems demonstrates that ZK-EHR uniquely combines identity anonymity, on-chain cryptographic policy enforcement, and auditable encrypted record retrieval. These findings establish the feasibility of zk-SNARK-based access control for decentralized, verifiable, and privacy-aware EHR management. Full article

The mandatory introduction of Romania’s national e-invoicing system, ANAF e-Factura, in January 2024 has reshaped B2B document exchange in the retail sector, but suppliers still operate in parallel with two proprietary electronic data interchange (EDI) platforms, EDINET and DocProcess, which increases integration complexity. This paper presents the architecture, implementation, and evaluation of a custom Laravel-based B2B platform developed to automate commercial workflows across these three channels. The system supports XML purchase order ingestion and normalization, product identifier resolution, unified order persistence, platform-specific invoice XML generation, and ANAF SPV submission via SmartBill and Oblio REST APIs. A comparative analysis of real production XML documents showed full field-level overlap across 21 invoice data dimensions, with the main differences between systems related to entity identification schemes rather than business information content. During 2025, the platform processed 1247 EDI purchase orders and achieved an 87.30% fully automated processing rate, reaching 94.60% by year-end through progressive product catalog enrichment. The results indicate that ANAF e-Factura is technically capable of covering the core invoice exchange function currently duplicated by proprietary EDI platforms, while their coexistence continues to impose additional integration effort and slows SME digital transformation, particularly for small and medium-sized suppliers. Full article