Search Results (13,881)

Digital twins (DTs) based on agent-based artificial intelligence (Agentic AI) provide a transformative framework for streamlining nondestructive testing (NDT) of 3D-printed rehabilitation equipment. This study applies a conceptual research methodology based on the integration and analysis of recent advances in Agentic AI, digital twin architectures, additive manufacturing, NDT technologies, and intelligent rehabilitation systems to establish a framework for autonomous quality monitoring and lifecycle management of 3D-printed medical devices. By creating intelligent virtual replicas of physical devices, these systems enable continuous monitoring of structural integrity, functional performance, and degradation mechanisms throughout the product lifecycle. Unlike conventional AI-based DTs, Agentic AI-driven DTs can autonomously perceive, reason, plan, and execute corrective actions based on real-time sensor data, NDT results, manufacturing information, and historical knowledge. The main conclusion of this work is that Agentic AI-enhanced DTs have the potential to transform NDT from a passive inspection approach into an intelligent, predictive, and autonomous decision-support system for rehabilitation equipment. Advanced machine learning and autonomous decision-making algorithms enable the identification of early signs of material degradation, manufacturing defects, fatigue accumulation, and performance anomalies, supporting predictive maintenance and proactive quality assurance. Integrating Agentic AI DTs with additive manufacturing processes enables real-time optimization of printing parameters, adaptive process control, and continuous refinement of inspection strategies without production interruption or destructive sampling, thereby supporting Industry 4.0 and smart manufacturing principles. The main innovation of this research lies in proposing an autonomous closed-loop framework that combines Agentic AI, DTs, additive manufacturing, and NDT into a unified system capable of continuous learning, reasoning, and operational optimization. Compared with existing studies that mainly focus on AI-assisted defect detection or static digital twin models, this approach introduces autonomous agents capable of coordinating sensing, simulation, diagnosis, prediction, and corrective actions across the entire lifecycle of 3D-printed rehabilitation devices. The proposed concept extends current digital twin applications by incorporating virtual stress testing, autonomous simulation, patient-specific customization, and adaptive device management, reducing dependence on physical prototypes, minimizing material waste, and accelerating design validation. By combining autonomous reasoning with predictive analytics, Agentic AI-based DTs represent a next-generation solution for intelligent, adaptive, and sustainable nondestructive testing, advancing both additive manufacturing technologies and personalized rehabilitation engineering. Full article

Modern power systems are evolving rapidly with growing distributed generation and high penetration of renewables and electric vehicles. Renewable sources, being inherently intermittent and weather dependent, may lead to rapid power fluctuations that challenge grid stability and power quality. Multilevel inverters (MLIs) have become a key enabling technology in modern power grids due to the escalating need for high-power, high-voltage, and high-quality energy conversion. Research on the control of MLIs in modern grid scenarios has significant scope due to the rising penetration of renewables, distributed generation and smart grid technologies. Hence sophisticated control strategies and topology selection of MLIs are required to enhance efficiency and to ensure optimal performance. Emerging areas like AI-based adaptive control find relevance in enabling stable, flexible and sustainable future power systems. In this comprehensive review, the state-of-the-art MLI classification, AI-driven control techniques, and their emerging technological applications are investigated. There is limited exploration of AI-based adaptive control for self-tuning operation and coordinated control of multiple MLIs in microgrids, active filtering and harmonic compensation in MLI-based modern power systems, and vehicle-to-grid (V2G) and bidirectional battery inverter control optimization integrated with MLI control. Full article

Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are among the key enabling technologies driving intelligent logistics and industrial automation. Despite their widespread adoption and rapid technological evolution, the literature often addresses AGV and AMR systems in a fragmented manner, lacking a structured methodological perspective that highlights their architectural foundations, levels of autonomy, and technological maturity. This paper presents a methodological survey of AGV and AMR technologies, focusing on system-level architectures and core functional components rather than isolated algorithms. The survey systematically analyzes key technological dimensions, including sensing and perception, localization and positioning strategies, navigation and path-planning approaches, communication infrastructures, and multi-robot coordination mechanisms. A clear distinction is drawn between classical AGV systems, which rely on fixed infrastructure and predefined routes, and AMR systems, which exhibit adaptive, perception-driven, and self-configuring behaviors enabled by artificial intelligence techniques. Rather than proposing new algorithms, this paper organizes existing approaches into a coherent framework that highlights technological transitions from infrastructure-dependent guidance to autonomous, data-driven navigation. Recent trends such as cloud–edge integration, learning-based navigation, scalable fleet management architectures, and cooperative multi-robot systems are reviewed and discussed from a methodological standpoint, emphasizing their role in increasing flexibility, robustness, and operational efficiency in industrial and logistics environments. The survey also addresses cross-cutting challenges, including system transparency, safety and certification, interoperability, and sustainability. Finally, this paper outlines research directions aligned with the principles of Industry 5.0, highlighting the need for human-centered, resilient, and scalable AMR and AGV systems capable of safe and explainable operation in complex industrial contexts. Full article

University–industry research collaboration is an important pathway through which firms integrate external scientific knowledge, strengthen innovation capabilities, and develop sustainability-oriented technological solutions. Public procurement, as a demand-side policy instrument, may provide market demand, resource expectations, and policy signals that encourage firms to cooperate with universities and research institutes. However, whether public procurement is associated with firms’ university–industry research collaboration remains insufficiently examined. Based on Chinese A-share listed firms from 2013 to 2024, this study matches public procurement contract data with listed firms and their subsidiaries and constructs firm-year measures of public procurement. University–industry research collaboration is measured by co-applied patents between firms and universities or research institutes. The baseline two-way fixed-effects results show a positive and statistically significant association between public procurement scale and university–industry research collaboration, although the estimated magnitude is economically small. Additional count-data and selection-adjusted analyses provide partial support for this relationship, but the results are not fully consistent across all specifications. The mechanism analysis provides suggestive evidence that R&D investment scale may serve as a channel through which public procurement is related to collaborative research activities. Further analysis shows that financing constraints positively moderate this relationship, and ownership heterogeneity indicates that the association is stronger among state-owned enterprises. These findings suggest that public procurement should not be understood as a uniformly effective driver of university–industry collaboration. Rather, its role appears to depend on firms’ financial conditions and institutional characteristics. This study contributes to research on demand-side innovation policy, university–industry collaboration, and sustainable innovation systems by highlighting the conditional nature of the procurement–collaboration relationship. Full article

Chinese home-cooked dishes (CHCDs) link agricultural production and daily consumption, but their resource dependence and sustainability remain poorly quantified. We standardized 341 recipes from China’s 34 provincial-level administrative regions, conducted a production-to-consumption analysis using emergy (solar-equivalent available energy directly and indirectly required for a product or service), and evaluated six optimization scenarios. Mean emergy input was 5.93 × 10¹² sej·dish⁻¹, with nonrenewable purchased inputs accounting for 77.99%. Ingredients, oil and sauces, and cooking contributed 69.17%, 21.69%, and 9.14%, respectively. Plant-based production relied on labor and chemical inputs; animal-based production relied on feed. Ruminant meats had relatively high nonrenewable inputs and environmental loads; vegetables had lower values. Across CHCDs, the emergy sustainability index (ESI), transformity, emergy consumption intensity, and food processing intensity averaged 0.26, 1.02 × 10⁶ sej·J⁻¹, 1.45 × 10¹² sej·serving⁻¹, and 4.64 × 10¹¹ sej·serving⁻¹, respectively. Sustainability declined from plant-based through mixed to animal-based dishes. Shandong cuisine ranked highest and Zhejiang cuisine lowest in combined sustainability and efficiency. Combining crop–livestock recycling, mechanization and energy-efficiency improvements, technological advancement and scaling up, moderate oil and sauce reduction, and cooking-efficiency improvements was projected to increase ESI by 19.34% relative to baseline. These results support dish-level emergy accounting for improving agri-food sustainability. Full article

Hybrid cyber–physical systems (CPS) integrate cloud services used in enterprise environments with operational technology (OT), which controls physical processes. However, most threat models applied to such systems implicitly assume that an adversary necessarily causes any loss of process availability. This perspective is reflected in both the STRIDE model and the MITRE ATT&CK for ICS knowledge base, which primarily focus on adversarial activities. As a result, they do not explicitly account for a scenario that is becoming increasingly relevant in hybrid architectures: the intentional shutdown of a physical process by the organization defending the system. The loss of availability resulting from the activation of protective mechanisms is not, in itself, a new problem. The concept of a spurious trip has been recognized in safety engineering for decades and is addressed in standards such as IEC 61511. Related dependencies are also considered within the STPA-Sec methodology. Therefore, the objective of this work is not to introduce a new type of threat, but rather to demonstrate that this phenomenon is not adequately represented in widely used threat-modeling taxonomies that are primarily attacker-centric. In addition, a specific trust boundary within the hybrid architecture at which this problem manifests itself is identified. This makes it possible to incorporate the phenomenon into the risk analysis of systems in which a compromise of the IT layer alone can ultimately lead to the shutdown of physical processes. The proposed threat model is based on trust-boundary analysis. The reference hybrid architecture was divided into seven trust boundaries, and each STRIDE category was subsequently mapped to the corresponding MITRE ATT&CK techniques for ICS, based on the trust boundary crossed by a given attack scenario. The resulting threat vectors were then ranked using the fundamental metrics defined in CVSS v4.0. The attack vector was derived from the trust boundary crossed by each scenario and, where applicable, was correlated with published CVE vulnerability assessments. The model was validated against four widely documented industrial cybersecurity incidents: Stuxnet, Triton, Industroyer, and Colonial Pipeline. Full article

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) comprise a biologically and clinically heterogeneous spectrum of tumors with substantial inter- and intratumoral variability. Despite advances in molecular classification, imaging, and treatment, accurate diagnosis, prognostication, and disease monitoring remain challenging. Established biomarkers, including chromogranin A (CgA) and 5-hydroxyindoleacetic acid (5-HIAA), provide limited and context-dependent information, highlighting the need for more biologically informative approaches. Recent advances in liquid biopsy, transcriptomic and genomic profiling, spatial technologies, molecular imaging, radiomics, and artificial intelligence are expanding the ability to characterize tumor biology and evolution. However, biological association and analytical validity do not necessarily translate into clinical utility. This narrative review critically evaluates established and emerging biomarkers according to the biological information they provide, with particular emphasis on molecular heterogeneity, tumor evolution, and the distinction between validated and investigational applications. We further discuss how complementary molecular, circulating, spatial, imaging, and clinical information may be integrated through multimodal and artificial intelligence-based approaches to advance tumor characterization and support precision oncology in GEP-NENs. Full article

This systematic review examines how artificial intelligence (AI) technologies relevant to human-factor risk reduction can be integrated into international and Spanish maritime safety frameworks. The formal PRISMA corpus comprises 23 core sources (13 peer-reviewed studies and 10 regulatory, institutional or technical documents), while a separate supplementary search provides recent independent technical and regulatory evidence up to 31 August 2026. The analysis covers computer vision, thermal and near-infrared sensing, multimodal fusion, behavioral and fatigue analysis, and onboard edge processing, with particular attention to precision, recall, false alarms, latency, computational requirements and operational robustness. The evidence shows that high detection performance can be achieved in specific maritime datasets, but the reported values depend strongly on the task, sensor, dataset and hardware and cannot be treated as a universal accuracy threshold. A system architecture is therefore proposed in which heterogeneous sensors feed synchronized edge processing, event verification, alarm management, VDR-compatible event logging, and human confirmation with defined fail-safe behavior. On the regulatory side, the study proposes staged adaptations of SOLAS, the ISM Code, STCW, MLC and Spanish inspection frameworks. The 2026 IMO MASS Code, considered as supplementary regulatory evidence, provides a relevant precedent for goal-based approval, risk assessment and progressive operational experience. Fixed tonnage and implementation-date thresholds are consequently treated as illustrative parameters rather than validated requirements; any mandatory carriage provision should be supported by formal safety assessment, type approval and operational evidence. The resulting framework links electronics implementation with a short-, medium- and long-term regulatory roadmap for safer merchant-ship operations. Full article

Deep soil cementation is widely used for strengthening weak soils; however, the effectiveness of injection mortars largely depends on their penetration ability and interaction with in situ soil conditions. This study aims to develop and validate a methodology for preparing equivalent loamy soil for large-scale tray model tests intended for subsequent investigations of deep soil cementation and injection technologies. The study focuses on reproducing natural soil density, water content and stress conditions representative of engineering–geological conditions of Astana. The model soil was prepared by reproducing natural density and moisture parameters including a target natural bulk density of 1.85 g/cm³ and an average measured natural density of 1.88 g/cm³, with a water content of 13.3%, while overburden pressure was simulated using a rigidly fixed cover. Laboratory tests determined consistency limits (plastic limit 14.42%, liquid limit 19.75%), indicating a semi-solid to solid soil state. A strong correlation (R = 0.99) between added water and achieved water content was established, with an optimal water volume of 18,900 cm³ required to achieve target conditions. Compaction tests showed that the required density was achieved with 20–50 roller passes depending on layer depth, resulting in final densities of 1.85–1.88 g/cm³. The results confirm that the proposed modeling approach reliably reproduces in situ conditions and can be effectively used to assess injection mortar performance in weak soil stabilization. Full article

This perspective examines the interactions among microplastics (MPs), emerging contaminants (ECs), climate change, and extreme hydrological events. Droughts, water scarcity, heatwaves, intense rainfall, and floods can alter the occurrence, mobilisation, transport, fate, and risks of MPs and associated ECs across water, soil, and groundwater systems. The analysis focuses on the context-dependent potential of MPs to act as vectors of ECs, soil–water interactions, and the potential influence of MPs on greenhouse gas (GHG) emissions. When these stressors co-occur, their combined effects may be additive, synergistic, or antagonistic. Accordingly, the “perfect storm” framing is used here to describe the potential for mutually reinforcing and cascading risks rather than to imply that synergy occurs universally. An integrated perspective therefore helps anticipate worst-case scenarios and move beyond the fragmented assessment of individual stressors. This paper discusses sustainable mitigation and adaptation strategies, including advanced wastewater treatment, water reuse, climate-resilient water management, infrastructure adapted to increasing hydrological variability, climate-smart agriculture, and safer alternatives to conventional plastics and chemicals. Effective implementation combines technological innovation with monitoring, governance, policy action, and public awareness. By framing the microplastics–contaminants–water–soil–climate nexus as an interconnected sustainability challenge, this work aims to support environmental resilience and progress toward the Sustainable Development Goals. Full article

Closed-loop insulin delivery, or the artificial pancreas, has evolved from an ambitious engineering concept into one of the most consequential advances in diabetes technology. By integrating continuous glucose monitoring, insulin pumps, and control algorithms into a single feedback system, these platforms aim to shift diabetes care from repeated manual correction toward more anticipatory and adaptive glucose regulation. The field has progressed from early proof-of-concept systems to contemporary hybrid closed-loop platforms, reflecting a deeper shift in diabetes management itself: from treating glucose excursions after they occur to trying to blunt them in real time. Its clinical relevance is greatest in type 1 diabetes, where the burden of self-management is high and the consequences of glycemic instability are immediate. This review introduces the major technologies and evidence shaping the field, with emphasis on the control strategies that drive system behavior, and asks which currently available closed-loop systems offer the best balance of glycemic benefit, usability, and translational readiness for routine diabetes care. Across randomized trials and real-world studies, automated insulin delivery has consistently improved time in range, reduced hypoglycemia, and enhanced patient experience, particularly in pediatric populations. At the same time, important limitations remain: sensor lag, the physiologic constraints of subcutaneous insulin, device complexity, cost, and unequal access limit full autonomy and widespread adoption. Looking ahead, the next phase of progress will likely depend on more adaptive artificial intelligence, improved meal detection, multimodal wearable data, and multi-hormone systems that move the field closer to truly physiologic glucose control. Full article

This article presents a comparative study of the geometric accuracy of spur gears manufactured by additive manufacturing (3D printing) and by injection molding. Gears were produced from two engineering plastics—POM (polyoxymethylene) for injection molding and carbon fiber-reinforced polyamide for 3D printing. The study evaluates three key parameters: deviation from the involute profile, single-pitch deviation, and surface roughness (Ra). Gears with identical geometric parameters (module 3 mm) were produced using both methods, allowing direct comparison between the resulting products under identical design conditions. Profile measurements were performed using a coordinate measuring machine (CMM) according to ISO 1328-1:2013. The surface roughness of gear teeth is a key parameter that determines operability, efficiency, and wear resistance. Smoother surfaces reduce friction and wear, leading to lower operational losses, higher efficiency, and longer service life. High roughness may lead to local stress points, increased noise levels, and vibrations during operation, which are especially critical for high-speed and heavily loaded gears. The obtained data serve as a basis for an objective evaluation of the applicability of additive manufacturing and injection molding for producing gears with different requirements for geometric accuracy, as well as for formulating guidelines for selecting an appropriate manufacturing technology depending on the specific application. Full article

The requirement of energy efficiency in designs of sustainable aquaponics has brought about the incorporation of passive solar systems and earth–air heat exchangers (EAHE). In this study, an evaluation of the physiological effects that arise from the implementation of the use of underground thermal stabilization by means of heat exchangers and the limitations of this technology will be carried out. The greatest strength of this technology is the “thermal buffer” that is created in the physiology of the fish; this technology makes the fish resilient to changes in environmental temperatures, thus reducing energy usage. However, the weakness is that there are ecological risks involved, such as moisture build-up due to low air exchange and biofilm formation in pipes, which occur when this technology is used without biological controls. This review highlights the critical balance between energy efficiency and biological safety while analyzing the impacts of these technological advantages on growth performance, metabolic regulation, and physiological stress responses in fish. This review demonstrates that, beyond improving heating and cooling efficiency, successful implementation of EAHE-assisted passive solar aquaponics depends on balancing engineering performance with biological requirements, including thermal regulation, gas exchange, humidity control, and biosecurity. Full article

Genetic transformation is a core technology for wheat molecular breeding. Transformation efficiency and genotype compatibility determine the pace of targeted genetic improvement. Mature embryos are readily accessible but are associated with low transformation efficiency and strong genotype dependence. Using mature wheat embryos as explants, we screened eight wheat varieties representing three ecological types to develop genotype-adapted tissue culture systems. Treatments A5 (4 mg/L 2,4-D + 2 mg/L Dicamba), A7 (4 mg/L 2,4-D + 6 mg/L Dicamba), and A8 (4 mg/L 2,4-D + 8 mg/L Dicamba) were optimal for callus induction in winter, semi-winter, and spring wheat, respectively. WF3 (a 1/2 MS-based medium) was broadly applicable to callus differentiation, whereas IBA produced the highest rooting efficiency in regenerated plantlets. The optimal transformation conditions comprised ultrasound treatment (2.5 min) followed by vacuum infiltration (5 min), supplementation of the infection solution with PEG₆₀₀₀ (250 mg/L) and Tween20 (0.01%), and an OD₆₀₀ of 0.6–0.8. Transformation efficiencies using mature embryos of the semi-winter wheat varieties YX288 and ZM578 were 2.5% and 1.8%, respectively. Using this transformation platform, the dwarfing genes Rht12 in ZM578 and Rht8 in YX288 were targeted by CRISPR/Cas9-mediated editing. Multiple single-base substitutions at the target sites were detected in ZM578 rht12 mutants, which exhibited significantly reduced height, increased tiller number, and a compact plant architecture. However, no successfully edited rht8 lines were obtained in YX288. These findings indicate that this study established a reliable mature-embryo transformation system for wheat and successfully validated gene editing in the semi-winter variety ZM578. The system provides a technical platform for the functional analysis of key genes and molecular breeding in bread wheat. Full article

In the present circumstances, it is exceedingly hard for people to monitor their blood sugar levels on a regular basis. Checking the blood glucose levels of diabetic individuals is often an essential part of managing diabetes. Now, this means repeatedly pricking your finger and bleeding. Non-invasive (NI) detection methods are anticipated to have several benefits, including the elimination of discomfort, avoidance of sharp items and biohazardous chemicals, the possibility of more frequent testing, and, therefore, better regulation of glucose levels. Infrared technology has become one of the most important technologies for the development of the NI self-monitoring of blood glucose (NI-SMBG). One good thing about this approach is that it does not need any chemicals and can employ fiber optic parts. So, only insulators come into direct contact with the skin. Also, the spectrometer may be made without any moving parts, which makes it strong. For this method to be effective, the spectral signature of glucose must be uniquely identifiable from all other chemical constituents in the human body, and this glucose-specific data must be obtained with a sufficiently high signal-to-noise ratio to facilitate reliable differentiation between glucose-dependent signals and those generated by other matrix components. In this paper, we have addressed the issue of infrared signature analysis for blood glucose, which has been done in the infrared region of the electromagnetic spectrum. Firstly, the analysis is carried out for the glucose molecule only. Later, looking at the presence of numerous other analyses in whole blood, tissues, skin, etc., for in vivo measurement of blood glucose, a set of wavelengths is identified on which in vivo measurements can be done with minimal interference from other body fluid analyses. Absorption of spectroscopic information collected on these wavelengths, along with a suitable calibration model, can be a step ahead for in vivo NI glucose measurement. The main innovative features of the present study are non-invasive glucose sensing, Patient-friendly and continuous monitoring opportunity, Progress towards wearable and real-time diagnostics, Clinical and Social Relevance and Contribution to Research. The paper investigates a non-invasive infrared-based methodology for glucose estimation, focusing on the spectral response characteristics of glucose in biological tissue. While the complexity of tissue spectroscopy involves potential interference from other biomolecules, the present work emphasizes the feasibility of glucose detection without invasive blood extraction, rather than conducting a dedicated interference-analysis study. Full article