Search Results (2,116)

Acoustic emission (AE) measurements have many uses to evaluate the integrity of materials. AE is often used to detect leakage in pipelines. It has also been used to monitor changes in strength properties of fiber-reinforced concrete. In the oil and gas industry, AE is predominantly used to study fracture initiation and propagation. In particular, characterization of samples is key for evaluating subsurface formations for successful underground storage. Research has been performed to understand the behavior of AE in uniaxial compression and single-stage triaxial compression tests. However, the validity of this method has not been documented in a multistage triaxial test. This characterization is required to understand the stability of the host rock under the related stress changes and potential mineralogical changes that may occur. Typically, there is a shortage of geologic samples. A single multistage triaxial test eliminates the need for twin samples and provides an economic and time-saving protocol compared to conventional methods. A single multistage triaxial (MST) test allows a constitutive model to be developed for a host rock. This work establishes a protocol for performing these tests with minimal corrections to the measurements. Acoustic emissions were measured on five different samples undergoing multistage triaxial tests. Two different behaviors were observed. For the coarse grained samples, designated Group 1 (Miocene sandstone, Wilcox Formation, and Cambrian sandstone), the number of AE events did not show a strong dependence on confining stress. They did show an exponential increase in AE events with increasing deviatoric stress during each stage. In contrast, the Group 2 samples (Niobrara Marl and Niobrara Chalk) exhibited significantly different stress-dependent AE behaviors. The amplitude of the AE events is significantly smaller than in the quartz-dominated samples, indicating a more ductile and diffuse failure mechanism. The correlation between maximum compressive strength and the point of positive dilatancy is 1.2 for both groups of samples, even though a different pattern of AE events is observed. Full article

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication framework that deliberately separates high-data-rate vibration monitoring from low-data-rate inclination-status monitoring while maintaining common requirements for preservation of available time information, data-source identification, and backend interpretability. The smart-building path integrates an ADXL355 triaxial accelerometer, ESP32-S3, Power over Ethernet (PoE), and Message Queuing Telemetry Transport (MQTT) for 200 Hz vibration acquisition, together with a second-order 10 Hz low-pass filter, 40-record batching, and a Flash LittleFS-based store-and-recovery mechanism that interleaves live and replayed records after reconnection. The construction-site path combines an SCL3300-D01 inclinometer with LoRaWAN, baseline-referenced relative-angle estimation, and a hysteresis state machine with distinct alarm and recovery thresholds. In a 24 h validation, four vibration nodes delivered all 69,120,000 expected records, and four forced-outage trials recovered all offline records while live transmission continued. Frequency-domain analysis confirmed attenuation of high-frequency components while retaining the dominant low-frequency response. The inclination path demonstrated quantifiable angle accuracy, correct alarm/recovery transitions, continuous LoRaWAN frame delivery over the observed interval, and correct backend decoding. The results show that path-specific communication design, combined with a common traceability concept, supports prototype functionality under the reported test conditions, not immediate construction-site deployment. Full 3D visual synchronization, BIM/GIS asset mapping, and digital-twin platform interfacing were not implemented and remain future development tasks. Full article

The effect of rotary swaging (RS) at room temperature and subsequent annealing at 350 °C on the microstructure, mechanical properties, and fatigue strength of Cu–35.8%Zn two-phase brass was studied. A structure with grains of α and β′ phases elongated along the deformation direction was formed after RS. It was also shown that subgrains of 200–300 nm in size, shear bands 100–200 nm wide, and deformation twins 10–30 nm wide were formed inside the α-phase grains. RS caused the increase in the yield stress (YS) from 93 ± 4 to 717 ± 6 MPa and the ultimate tensile strength (UTS) from 332 ± 2 to 744 ± 19 MPa with a decrease in ductility (El) from 71.0 ± 2.0 to 10.3 ± 1.7%. The fatigue limit also increased from 240 to 415 MPa after RS. Subsequent annealing at 350 °C induced recrystallization of the α-phase with the formation of equiaxed grains 2.2–3.6 µm in size, which resulted in a decrease in UTS to 462–466 MPa and an increase in ductility to 44–45%. Extending the annealing time to 4 h did not affect the strength and ductility values of the alloy. Full article

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

Vanishing twin syndrome (VTS) denotes the spontaneous loss of one conceptus from an initially recognized multiple pregnancy, most often during the first trimester. Its clinical relevance has increased with in vitro fertilization and embryo transfer (IVF-ET), as multiple embryo transfer creates the substrate for dizygotic VTS, while blastocyst transfer may be followed by monozygotic splitting and serial early ultrasonography increases detection. However, the literature remains heterogeneous, with empty gestational sacs, losses after fetal cardiac activity, and later single fetal demise often grouped together despite different biological and prognostic implications. New evidence published in recent years on frozen embryo transfer outcomes, cell-free DNA screening, and early childhood development warrants an updated synthesis of vanishing twin syndrome to inform individualized risk assessment, counselling, and standardized research reporting. Overall, the surviving singleton appears to have a lower risk than an ongoing twin pregnancy but a higher risk than a primary singleton, particularly for preterm birth, low birthweight, and small-for-gestational-age birth, with risk increasing when loss occurs after fetal cardiac activity or later in gestation. Nevertheless, evidence is not unanimous, as several well-characterized cohorts and earlier meta-analyses reported no measurable perinatal disadvantage. Two reports from a single tertiary center, based on substantially overlapping recruitment periods, further suggest that VTS may be proportionally less frequent among established twin pregnancies after IVF/ICSI than after spontaneous conception, while IVF-associated VTS has been linked to placental abnormalities, diabetes, and fetal growth restriction; this observation requires independent confirmation. Contemporary frozen embryo transfer cohorts indicate that VTS remains relevant in modern practice, whereas limited long-term data have not demonstrated impaired early childhood growth or development. Management should document the initial number of gestational sacs, embryonic structures, cardiac activity, chorionicity, and timing of loss, while adapting aneuploidy screening to residual trophoblastic DNA and individualizing fetal-growth surveillance. Elective single-embryo transfer remains the most effective preventive strategy, although it cannot eliminate monozygotic twinning. Standardized definitions and long-term offspring follow-up are needed. Full article

The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by ubiquitous connectivity of vehicles, roadside infrastructure, pedestrians, edge/cloud platforms, and smart-city services. With the IoV evolving towards highly connected, autonomous and data-driven mobility ecosystems, it needs to meet challenging requirements for low latency, scalability, interoperability, security, privacy and trust. This paper presents a comprehensive cross-layer approach for intelligent, secure and privacy-preserving IoV systems. It is built upon an analytical framework and systematically studies the perception, communication, edge/cloud computing, blockchain-enabled trust and application layers of IoV technologies. In addition, the paper presents an in-depth review of the enabling techniques such as machine learning (ML), deep learning (DL), reinforcement learning (RL), federated learning (FL), blockchain, cybersecurity mechanisms, digital twins, edge computing, 6G integration, and resource allocation. Moreover, it discusses the interplay and trade-offs between intelligence, security, privacy, computation, latency, and scalability. The survey also covers other significant challenges like intrusion detection, decentralized authentication, privacy-preserving learning, blockchain overhead, semantic interoperability, post-quantum security, and standardized datasets. This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions. Full article

Supraventricular tachycardias (SVTs), including atrial fibrillation (AF), atrioventricular nodal re-entrant tachycardia (AVNRT), atrioventricular re-entrant tachycardia (AVRT), and focal atrial tachycardias, can possibly arise from the interaction of genetic predisposition and environmental exposures. While genome-wide association studies (GWASs) have identified 525 loci for atrial fibrillation and a small number of loci for AVNRT and accessory pathway-mediated tachycardia, the contribution of air pollution, lifestyle factors and psychosocial stress to epigenetic remodeling of atrial tissue remains insufficiently integrated into current mechanistic models of arrhythmogenesis. This review aims to synthesize evidence on how environmental exposures, including PM_2.5, NO₂, ozone, tobacco smoke, obesity, alcohol use, and physical inactivity, modulate epigenetic pathways relevant to SVT susceptibility, and to evaluate whether AF-derived epigenetic insights can be extrapolated to other SVTs. Thus, a narrative synthesis was conducted across studies examining environmental determinants, epigenetic mechanisms (DNA methylation, histone modifications, non-coding RNAs), and genetic susceptibility in SVTs. Literature from cardiac tissue studies, circulating epigenetic biomarker analyses, and mechanistic AF models was integrated to construct a unified gene–environment–epigenome framework. In atrial fibrillation, environmental exposures are consistently associated with epigenetic alterations affecting atrial electrophysiology, inflammation, oxidative stress and structural remodeling, and air pollutants and lifestyle factors modulate methylation signatures, histone-modifying enzymes and microRNA networks implicated in atrial conduction and re-entry. For AVNRT, AVRT and focal atrial tachycardia the evidential position is different. Large prospective cohort and case-crossover analyses now link air pollution to incident and acute supraventricular tachycardia, and genome-wide association studies have identified susceptibility loci for AVNRT and for accessory-pathway-mediated tachycardia, including one gene encoding a cardiac chromatin-remodeling protein; but no epigenomic profiling of nodal or accessory-pathway tissue has been reported, and no study has measured an environmental exposure, an atrial epigenetic mark and a non-AF SVT endpoint in the same participants. Twin data indicate that approximately 35% of SVT risk is attributable to genetic and 65% to unique environmental factors, which motivates a gene–environment–epigenome framework without validating its mechanistic detail outside AF. Mapping GWAS-identified loci onto environmentally responsive regulatory pathways identifies candidate convergence points between inherited risk and exposure-driven remodeling; for non-AF SVT, these are designated working hypotheses rather than established mechanisms. Air pollution and lifestyle factors are associated with supraventricular arrhythmia across the spectrum, and in atrial fibrillation there is direct evidence that they act, in part, through epigenetic reprogramming of atrial tissue. No epigenetic panel has been prospectively validated for the prediction of any supraventricular arrhythmia, and precision risk stratification therefore remains a research objective rather than a near-term clinical horizon. Integrating environmental exposure data with genetic and epigenomic profiling is nonetheless the most plausible route toward it. Future priorities include exposure-stratified, cell-resolved epigenomic profiling of atrial and nodal tissue, prospective validation of candidate circulating markers against incident arrhythmia, and replication in non-European populations and in both sexes. Full article

In microcavity displays, different device structures can produce nearly identical normal-incidence electroluminescence spectra, making non-destructive identification difficult. This is especially relevant when narrow-band QLED emission is compared with cavity-narrowed OLED emission. Angle-resolved measurements can distinguish these cases, but the need for mechanical rotation limits measurement throughput. Here, we developed a digital twin-assisted method that uses a single normal-incidence spectrum for structural identification. The optical model was parameterized with measured material properties and checked against measured electroluminescence spectra. It was then used to generate spectra with ±1 nm electrode-thickness variations, and measured spectra were also included during training. Four machine-learning classifiers were compared for eight QLED/OLED device structures. The Tanh-activated multilayer perceptron gave the highest testing accuracy of 93.94%, compared with 84.85% for logistic regression. These results show that small differences in the full spectral shape can support structural identification when peak wavelength and linewidth alone are ambiguous. The method provides a practical basis for rotation-free optical screening of microcavity display structures. Full article

Artificial intelligence (AI) is embedded in healthcare through decision support, imaging, documentation, monitoring, digital twins, and smart-hospital infrastructures. This scoping review mapped technologies, healthcare contexts, socio-technical dimensions, governance mechanisms, and implementation conditions of AI-enabled healthcare systems. The review followed PRISMA-ScR. Scopus, Web of Science Core Collection, PubMed, and IEEE Xplore were searched on 1 July 2026 for English-language sources published from 2021 to 2026. All four authors participated in source selection; each record was assessed by two reviewers, and disagreements were resolved by consensus. Data were charted in matrices and synthesized descriptively and thematically. Of 2422 records, 426 duplicates were removed and 1996 were screened. Among 185 full-text reports, 124 were excluded, including 18 for insufficient methodological or empirical information, and 61 were included. Included sources then underwent a complementary seven-criterion cross-design appraisal scored from 1 to 3, without altering the final corpus. Technologies included machine learning, deep learning, decision support, explainable AI, natural language processing, large language models, interoperability frameworks, blockchain/IoMT, and digital twins. Challenges involved validation, data quality, interoperability, accountability, privacy, security, explainability, trust, bias, equity, and workforce readiness. Reported implementation facilitators included interoperable infrastructure, participatory design, lifecycle governance, continuous validation, and context-sensitive implementation. Full article

Food cold chains rely on refrigeration, cold storage, refrigerated transport, packaging and monitoring systems that consume electricity and fuel while preserving food safety, quality and shelf life. Although many studies propose energy-saving technologies or optimization models for individual cold-chain operations, less is known about how energy-efficiency actions are distributed across refrigeration, logistics and digital monitoring domains, which actors must collaborate to implement them, and which benefits and barriers shape adoption. This paper presents a systematic literature review supported by bibliometric and structured content analysis. Searches in Scopus and Web of Science identified 3930 records before deduplication. After removing out-of-year records and duplicates, 2368 unique records were screened; 896 reports were sought for full-text assessment; 751 reports were retrieved and assessed; 466 studies were included in the final review corpus; and 408 were coded as an applied/action corpus. The synthesis identifies ten energy-efficiency action families, seven cold-chain stage classes, multi-actor configurations, evidence types, collaboration-intensity levels, energy benefits, non-energy benefits and implementation barriers. Transport, routing and distribution is the largest action family (134 records), followed by cold storage and refrigeration technology (66), digital monitoring and information sharing (58), life-cycle assessment, energy assessment and decision support (36), energy systems and renewable cooling (34), packaging and thermal insulation (33), and inventory, and planning and coordination (27). The findings show that food cold-chain energy efficiency is not only a technical refrigeration problem but also a collaborative implementation challenge: many actions require information sharing, coordinated operating decisions, joint investment, data governance or cost/benefit-sharing mechanisms. The review contributes an action-oriented framework that links energy-saving actions to stages, actors, collaboration requirements, benefits and barriers, and it identifies future research priorities on comparable energy metrics, measured savings, renewable cooling, digital twins, demand-side flexibility and governance of collaborative energy-efficiency investments. Full article

Nanoparticle-based drug delivery systems have become an important component of modern nanomedicine, enabling improved drug protection, controlled release, targeted delivery, and the modulation of pharmacokinetic behavior. Their therapeutic performance is governed by physicochemical properties such as size, shape, surface chemistry, and material composition, which influence biological interactions, biodistribution, cellular uptake, and clearance. This review examines major nanoparticle platforms, including polymeric, lipid-based, inorganic, carbon-based, and hybrid systems, together with passive and active targeting and endogenous and externally triggered release strategies. Current and emerging applications in oncology, infectious diseases, central nervous system disorders, gene therapy, and vaccines are discussed alongside theranostic and combination-delivery approaches. Particular emphasis is placed on computational modeling, artificial intelligence, and digital twins for formulation optimization and personalized nanomedicine. Key barriers to clinical translation, including manufacturing scalability, biological variability, limitations of EPR-mediated targeting, regulatory standardization, and long-term safety, are critically evaluated. Finally, emerging directions in sustainable nanomanufacturing and biomimetic delivery are discussed. By integrating biological mechanisms with computational, manufacturing, regulatory, and clinical considerations, this review provides a translational perspective on advancing nanoparticle drug-delivery systems from laboratory development toward clinical implementation. Full article

Irrigation scheduling plays a pivotal role in bridging water resource allocation and farmland production management. For decades, scheduling in irrigation districts has predominantly relied on operators’ experience and relatively rigid water delivery plans, making it difficult to simultaneously meet the demands for timely responsiveness and precise water allocation under the combined influence of meteorological variability, changing crop water requirements, and the dynamic adjustments of water conveyance and distribution systems. The advancement of digital technologies, such as the Internet of Things, machine learning, deep reinforcement learning, and digital twins, has opened new technical pathways for optimizing irrigation scheduling. Focusing on the development of smart irrigation and scheduling optimization in irrigation districts, this paper systematically reviews the relevant literature published from January 2000 to June 2026 and delineates its evolution into three stages. Early-stage research was grounded in physical models, empirical rules, and hydraulic simulations, establishing fundamental methods for evapotranspiration estimation, crop water requirement calculation, and canal water delivery simulation. The middle stage, marked by the introduction of the Internet of Things and machine learning, enabled real-time monitoring of hydrological conditions, soil moisture, and meteorological data and promoted a data-driven transformation of water demand forecasting methods. The recent stage is characterized by the integration of deep reinforcement learning, digital twins, and knowledge graphs, which extends irrigation district scheduling from isolated single-point optimization toward multi-agent coordination and closed-loop management. Existing evidence confirms that digital technologies have yielded water-saving and yield-increasing benefits at the field scale and improved water distribution efficiency in several demonstration irrigation districts; however, their wider deployment at the district scale still faces bottlenecks such as inadequate sensing of physical execution processes, underdeveloped multi-objective trade-off mechanisms, and limited model transferability and long-term operational sustainability. To address these challenges, this paper proposes future research directions oriented toward real-time perception of water delivery and distribution status, multi-objective robust optimization, explainable artificial intelligence, and human–machine collaborative decision-making, thereby providing a reference for the theoretical development, engineering deployment, and operational management of smart irrigation district scheduling systems. Full article

The increasing complexity of environmental analysis requires new approaches for interactive-scale simulation across indoor and urban spaces. While computational fluid dynamics (CFD) models provide detailed representations of gas dispersion and aerosol transport, they are often computationally intensive for interactive environmental analysis and integration into digital twin platforms. This pilot study presents a GPU-accelerated voxel simulation framework for modeling three-dimensional gas dispersion and aerosol transport using structured voxel representations derived from BIM, LiDAR, GIS, and other 3D built-environment datasets. The framework provides physically informed, CFD-inspired simulation at sub-meter to meter-scale spatial resolutions while maintaining interactive runtime performance suitable for building management, ventilation analysis, environmental monitoring, hazard assessment, and emergency response applications. Transport dynamics are modeled using a discretized advection–diffusion formulation incorporating airflow-driven advection, diffusion, source emissions, and voxel-level sink mechanisms. A key contribution is the development of a voxel-native GPU-parallel computational architecture implemented in Python 3.10.20 using Taichi kernels. Prototype simulations and comparative validation against a benchmark ANSYS Fluent 20.1 simulation demonstrate stable transport behavior, encouraging agreement with the CFD solution, browser-based three-dimensional visualization, and efficient execution on commodity GPU hardware. Experimental scenarios include a voxelized three-story Industry Foundation Classes (IFC) building model comprising approximately 34.5 million active voxels ($582\times 382\times 155$ voxels) and an urban-scale 3D city model spanning approximately $300\times 300\times 150$ m and containing up to 13.9 million active voxels. Simulations containing tens of millions of voxels were completed within minutes on a single consumer-grade GPU, demonstrating the scalability of the framework. These results demonstrate that the proposed framework provides an efficient voxel-based approximation of gas dispersion suitable for interactive environmental analysis and can support future integration with digital twin and AI-assisted environmental simulation systems. Full article

Digital twin-based real-time quality control in gear machining lacks closed-loop feedback, classification without visual recognition, and low-latency I/O synchronization. This paper proposes a digital twin-driven intelligent quality control framework built upon a four-layer cyber–physical architecture comprising three tightly coupled subsystems: a parent-object assignment mechanism that enables precise workpiece type tracking and routing through hierarchical container queries, eliminating the need for computationally expensive visual type classification while maintaining near-perfect tracking accuracy; a rhythm-adaptive multi-robot behavioral control scheme that adjusts production cadence via a global rhythm coefficient without altering spatial trajectories; and a lightweight in-memory key-value store-based I/O synchronization mechanism that achieves architecturally bounded signal update latency (communication over a dedicated localhost TCP/IP path) well below the critical process cycle. Validation on a physical gear hub production line with six operations and three robots demonstrates a single-piece inspection cycle of 11.5 s, collision-free operation, and average error rates of 0.4% for inline quality inspection. The framework provides a low-cost, low-latency, and formally grounded solution for real-time quality control in gear machining workshops. Full article

Mobile industrial devices increasingly cross administrative domains. A maintenance terminal certified in one factory can be dispatched to another, while autonomous vehicles cross fog domains while reaching cloud digital twins. Existing solutions force a trade-off. Long-lived certificates expose a stable identity that every visited domain can track and that is clonable once a device is captured, while single-gateway token services concentrate issuing power in one trusted node and asynchronous revocation leaves an unquantified window in which a revoked device is still accepted. Here, we present HRCred, which converts hardware identities rooted in physical unclonable functions (PUFs) into domain-bound, epoch-bound, and threshold-issued short-lived pseudonymous credentials. A device proves possession of its reconstructed root key to its home domain using only symmetric primitives. A set of fog issuers jointly signs each credential with a t-of-n threshold BLS signature, so no coalition of fewer than t issuers can mint one. Finally, a monotonic revocation-epoch mechanism yields a configurable upper bound on how long a revoked credential can still be accepted, which we prove and validate. On constrained hardware, the device side costs 3.6 mJ (18.8 ms authentication and 5.4 ms verification, on par with the lightest single-gateway token) while resisting up to $t-1$ compromised issuers, cutting cross-domain linkage AUC to 0.55, and keeping all 12,000 measured post-revocation acceptances below the analytical bound. Full article