Search Results (18,669)

Therapeutic peptides have emerged as promising tools in oncology due to their high specificity, favorable safety profile, and capacity to target molecular hallmarks of cancer. Their clinical translation, however, remains limited by poor stability, rapid proteolytic degradation, and inefficient biodistribution. Iron oxide nanoparticles (IONPs) offer a compelling solution to these challenges. Owing to their biocompatibility, magnetic properties, and ability to serve as both drug carriers and imaging agents, IONPs have become a versatile platform for precision nanomedicine. The integration of peptides with IONPs has generated a new class of hybrid systems that combine the biological accuracy of peptide ligands with the multifunctionality of magnetic nanomaterials. Peptide functionalization enables selective tumor targeting and deeper tissue penetration, while the IONP core supports controlled delivery, MRI-based tracking, and activation of therapeutic mechanisms such as magnetic hyperthermia. These hybrids also influence the tumor microenvironment (TME), facilitating stromal remodeling and improved drug accessibility. Importantly, the iron-driven redox chemistry inherent to IONPs can trigger regulated cell death pathways, including ferroptosis and autophagy, inhibiting opportunities to overcome resistance in aggressive or refractory tumors. As advances in peptide engineering, nanotechnology, and artificial intelligence accelerate design and optimization, peptide–IONP conjugates are poised for translational progress. Their combined targeting precision, imaging capability, and therapeutic versatility position them as promising candidates for next-generation cancer theranostics. Full article

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe how second-opinion services for rare diseases are organized and governed, to characterize technological and workflow models, to summarize benefits and barriers, and to identify priority evidence gaps for implementation. Methods: Using a population–concept–context approach, we included peer-reviewed studies describing implemented second-opinion systems for rare diseases and excluded isolated case reports, purely conceptual proposals, and work outside this focus. Searches in August 2025 covered PubMed/MEDLINE, Scopus, Web of Science Core Collection, Cochrane Library, IEEE Xplore, ACM Digital Library, and LILACS without date limits and were restricted to English, Portuguese, or Spanish. Two reviewers screened independently, and the data were charted with a standardized, piloted form. No formal critical appraisal was undertaken, and the synthesis was descriptive. Results: Initiatives were clustered by scale (European networks, national programs, regional systems, international collaborations) and favored hybrid models over asynchronous and synchronous ones. Across settings, services shared reproducible workflows and provided faster access to expertise, quicker decision-making, and more frequent clarification of care plans. These improvements were enabled by transparent governance and dedicated support but were constrained by platform complexity, the effort required to assemble panels, uneven incentives, interoperability gaps, and medico-legal uncertainty. Conclusions: Systematized second-opinion services for rare diseases are feasible and clinically relevant. Progress hinges on usability, aligned incentives, and pragmatic interoperability, advancing from registries toward bidirectional electronic health record connections, alongside prospective evaluations of outcomes, equity, experience, effectiveness, and costs. Full article

Background: PTSD diagnosis is challenging. Symptoms overlap with depression and panic attacks. This causes misdiagnosis and delayed treatment. Current methods lack objective biomarkers. This study presents a hybrid AI framework. It combines CNNs and SVMs. The system detects PTSD from ECG signals. Methods: ECG data from 79 participants were analyzed. Four groups were included. PTSD patients numbered 20. Depression patients numbered 20. Panic attack patients numbered 19. Healthy controls numbered 20. Wavelet transform created scalograms. Three CNN models were tested. AlexNet, GoogLeNet, and ResNet50 were used. Deep features were extracted. SVMs classified the features. Five-fold validation was performed. Statistical tests confirmed significance. Results: Hybrid models performed robustly. ResNet50 + SVM and AlexNet + SVM achieved statistically equivalent results with accuracies of 97.05% and 97.26%, respectively. AUC reached 1.00 for multi-class tasks. PTSD detection was highly accurate. The system distinguished PTSD from other disorders. Hybrid models beat standalone CNNs. SVM integration improved results significantly. Conclusions: This is the first ECG-based AI for PTSD diagnosis. The hybrid approach achieves clinical-level accuracy. PTSD is distinguished from depression and panic attacks. Objective biomarkers support psychiatric assessment. Early intervention becomes possible. Full article

This study presents the techno-economic optimization of a hybrid distillation-membrane process for the complete fractionation of liquefied petroleum gas (LPG), targeting high-purity propane, n-butane, and isobutane recovery. The process employs an initial distillation column to separate propane (99% purity) from a propane-enriched stream, which is subsequently fed to a two-stage membrane system using an MFI zeolite hollow-fiber membrane for n-butane/isobutane separation. Through systematic simulation and sensitivity analysis, different membrane configurations were evaluated. The two-stage process with a partial residue-side reflux configuration demonstrated superior economic performance, achieving a total operating cost of 31.58 USD/h. Key membrane parameters—area, permeance, and separation factor—were optimized to balance separation efficiency with energy consumption and cost. The analysis identified an optimal configuration: a membrane area of 800 m², an n-butane permeance of 0.9 kg·m⁻²·h⁻¹, and a separation factor of 40. This setup ensured high n-alkane recovery while effectively minimizing energy use and capital investment. The study concludes that the optimized distillation-membrane hybrid process offers a highly efficient and economically viable strategy for the full utilization of LPG components. Full article

Cabbage (Brassica oleracea L. var. capitata) is an important leafy vegetable crop, and the development of homozygous parental lines is essential for F₁ hybrid breeding. Isolated microspore culture (IMC) provides a rapid approach for producing haploid and doubled haploid (DH) lines. However, its efficiency in cabbage remains highly dependent on genotype, donor plant growth conditions, and culture conditions. This study aimed to optimize key factors affecting microspore embryogenesis and plant regeneration in a Korean green cabbage (‘SJ-Ca 13’) and to evaluate the ploidy and genetic characteristics of regenerated plants. Microspore yield and embryogenesis were strongly influenced by flower bud size. Bud size of 4.0 ± 0.5 mm yielded the highest number of microspores (4.17 × 10⁴ per bud) and exclusively produced microspore-derived embryos (2.33 embryos per Petri dish), whereas smaller or larger buds failed to induce embryogenesis. Heat shock treatment at 32.5 °C was essential for embryogenesis, with 24 or 48 h of treatment inducing embryo formation, while prolonged exposure (72 h) completely inhibited embryogenesis. Efficient shoot regeneration was achieved when microspore-derived embryos were cultured on semi-solid MS medium with reduced salt strength (1/3×) and higher agar concentration (1.0%), resulting in the highest shoot regeneration rate. Ploidy test revealed that 50% of regenerated plants were spontaneous doubled haploids. SSR analysis using 26 markers detected no genetic polymorphism among regenerated plants. Overall, this study establishes an efficient IMC and regeneration system for cabbage and demonstrates its potential for rapid DH line production to support cabbage breeding programs. Full article

To address the challenge of bearing load monitoring in elastically supported marine shafting systems, a multi-data fusion-based prediction model is constructed. In view of the small-sample nature of measured bearing load data, transfer learning is adopted to migrate the physical relationships embedded in finite element simulations to the measurement domain. A limited number of actual samples are used to correct the simulation data, forming a high-fidelity hybrid training set. The system—supported by air-spring isolators mounted on the raft—is divided into multiple sub-regions according to their spatial layout, establishing local mappings from air-spring pressure variations to bearing load increments to reduce model complexity. On this basis, a Stacking ensemble learning framework is further incorporated into the prediction model to integrate multi-source information such as air-spring pressure and raft strain, thereby enriching the model’s information acquisition and improving prediction accuracy. Experimental results show that the proposed transfer learning-based multi-sub-region bearing load prediction model performs significantly better than the full-parameter input model. Furthermore, the strain-enhanced Stacking-based multi-data fusion bearing load prediction model improves the characterization of shafting features and reduces the maximum prediction error. The proposed multi-data fusion modeling strategy offers a viable approach for condition monitoring and intelligent maintenance of marine shafting systems. Full article

Secure and auditable data sharing in large-scale Internet of Things (IoT) environments remains a significant challenge due to weak trust coordination, limited scalability, and susceptibility to emerging quantum attacks. This study introduces a hybrid blockchain-based framework that integrates post-quantum cryptography with intelligent anomaly detection to ensure end-to-end data integrity and resilience. The proposed system utilizes Hyperledger Fabric for permissioned device lifecycle management and Ethereum for public auditability of encrypted telemetry, thereby providing both private control and transparent verification. Device identities are established using quantum-entropy-seeded credentials and safeguarded with lattice-based encryption to withstand quantum adversaries. A convolutional long short-term memory (CNN–LSTM) model continuously monitors device behavior, facilitating real-time trust scoring and autonomous revocation via smart contract triggers. Experimental results demonstrate 97.4% anomaly detection accuracy and a 0.968 F1-score, supporting up to 1000 transactions per second with cross-chain latency below 6 s. These findings indicate that the proposed architecture delivers scalable, quantum-resilient, and computationally efficient data sharing suitable for mission-critical IoT deployments. Full article

This work investigates a quadrotor equipped with dual-stage robotic arms and a cable-suspended payload, developing a unified methodology for modeling and control. A 10-DOF Lagrangian model captures vehicle-arm-payload coupling through structured mass matrices. A hierarchical control architecture combines SO(3)-based attitude regulation with cooperative swing compensation via partial feedback linearization, exploiting coupling matrices to distribute control between platform and arm actuators. Model accuracy is enhanced through physics-informed system identification, achieving improved prediction correlation with bounded corrections. Lyapunov analysis establishes semi-global practical stability with explicit robustness bounds. High-fidelity simulations in MuJoCo demonstrate a 40–70% swing reduction compared to PD control across multiple scenarios, with low computational overhead at kHz-level control rates, making it suitable for embedded implementation. The framework provides a theoretical foundation and implementation guidelines for cooperative aerial manipulation systems. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article

Reliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents a novel machine learning-based predictive framework that integrates Paschen’s Law with simulated and empirical data to estimate the breakdown voltage $\left(V_{bk}\right)$ of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage $\left(V_{bk}\right)$ using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning. Full article

This study investigates the interaction of Ru₃ clusters with pristine and defective anatase (101) TiO₂ surfaces using density functional theory (DFT) to evaluate their structural stability, electronic modifications, and photocatalytic potential. The results show that Ru₃ clusters strongly bind to both pristine and defective surfaces, with oxygen vacancies acting as anchoring sites that further stabilize the clusters. Electronic structure analysis reveals the formation of mid-gap states due to hybridization between Ru and Ti orbitals, extending visible light absorption. On defective surfaces, synergistic effects between Ru₃ clusters and vacancy-induced states further enhance charge separation and reduce recombination. Band structure and wavefunction analyses confirm these findings, highlighting Ru₃-decorated anatase TiO₂ as a promising system for hydrogen evolution and CO₂ reduction. The outcomes of this computational investigation provide valuable insights into the rational design of advanced photocatalysts for sustainable energy applications. Full article

Low work function (LWF) materials are essential for enabling efficient systems’ behavior in applications ranging from vacuum electronics to energy conversion devices and next-generation opto-electronic interfaces. Recent advances in theory, characterization, and materials engineering have dramatically expanded the candidates for LWF systems, including alkali-based compounds, perovskites, borides, nitrides, barium and scandium oxides, 2D materials, MXenes, functional polymers, carbon materials, and hybrid architectures. This review provides a comprehensive overview of the fundamental mechanisms governing the work function (WF) and discusses the state-of-the-art measurement techniques, as well as the most used computational approaches for predicting and validating WF values. The recent breakthroughs in engineering LWF surfaces through different methods are discussed. Special emphasis is placed on the relationship between predicted and experimentally measured WF values, highlighting the role of surface contamination, reconstruction, and environmental stability. Performance, advantages, and limitations of major LWF material families are fully analyzed, identifying emerging opportunities for next applications. Finally, current and fundamental challenges in achieving scalable, stable, and reproducible LWF surfaces are considered, presenting promising research directions such as high-throughput computational discovery and in situ surface engineering with protective coatings. This review aims to provide a unified framework for understanding, achieving, and advancing LWF materials toward practical and industrially relevant technologies. Full article

The large-scale integration of Distributed Energy Resources (DERs) in smart grids creates challenges related to real-time optimization, system scalability, and operational security. This paper presents GridOpt, a hybrid edge–cloud framework designed to address these challenges through distributed intelligence and coordinated control. In GridOpt, edge nodes handle latency-sensitive tasks, while cloud resources support the processing of large-scale grid data. Security is addressed through the integration of homomorphic encryption and blockchain-based consensus, together with an interoperability layer that enables coordination among heterogeneous grid components. Simulation results show that GridOpt achieves an average latency of 76 ms and an energy consumption of 25 Joules under high-throughput conditions. The framework further maintains scalability beyond 10 requests per second with a resource utilization of 54% in dense deployment scenarios. Comparative analysis indicates that GridOpt outperforms ECCGrid, JOintCS, and EdgeApp across key performance metrics. Full article

Precision agriculture has increasingly adopted controlled agricultural microclimates, particularly smart greenhouses, as a strategy to enhance crop yields while maintaining environmental conditions within suitable ranges for each crop. Among the variables that govern the water balance in these systems, air humidity plays a critical role; therefore, accurate humidity forecasting is essential for implementing timely control actions that support productivity levels. However, greenhouse conditions are frequently perturbed by extreme weather events, which lead to nonlinear and non-stationary humidity dynamics. In this context, the aim of this study was to design an optimized evolving fuzzy inference system for humidity forecasting that can adapt to changing and unforeseen situations in agricultural microclimates. A prototyping-based methodology was followed, including phases of communication, quick planning, modeling and quick design, construction of the prototype, and deployment. A hybrid genetic algorithm was used to optimize the parameters of an evolving Mamdani-type fuzzy inference system, extended to handle missing values in online data streams. Thirty independent optimization runs were performed, and the best configuration achieved a mean squared error of 1.20 × 10⁻² in humidity forecasting using one minute of data for three months. The resulting model showed high interpretability, with an average number of 1.35 rules, tolerance for missing values, imputing 2% of the data, and robustness to sudden changes in the data stream with a p-value of 0.01 for the Augmented Dickey–Fuller test at alpha = 0.05. In general, the optimized evolving fuzzy inference system obtained an effectiveness rate greater than 90% and demonstrated adaptability to extreme weather conditions, suggesting its applicability to other phenomena with similar characteristics. Full article

Drought poses a severe threat to grassland biodiversity and ecosystem function. However, quantitative frameworks that capture the interactive effects of drought intensity and duration on productivity remain scarce, limiting impact assessment accuracy. To bridge this gap, we developed and validated a novel hybrid modeling framework to quantify drought impacts on net primary productivity (NPP) across Inner Mongolia’s major grasslands (1961–2012). Drought was characterized using the Standardized Precipitation Index (SPI), and ecosystem productivity was simulated with the Biome-BGC model. Our core innovation is the hybrid model, which integrates linear and nonlinear components to explicitly capture the compounded, nonlinear influence of combined drought intensity and duration. This represents a significant advance over conventional single-perspective approaches. Key results demonstrate that the hybrid model substantially outperforms linear and nonlinear models alone, yielding highly significant regression equations for all grassland types (meadow, typical, desert; all p < 0.001). Independent validation confirmed its robustness and high predictive skill (NSE ≈ 0.868, RMSE = 20.09 gC/m²/yr). The analysis reveals two critical findings: (1) drought duration is a stronger driver of productivity decline than instantaneous intensity, and (2) desert grasslands are the most vulnerable, followed by typical and meadow grasslands. The hybrid model serves as a practical tool for estimating site-specific productivity loss, directly informing grassland management priorities, adaptive grazing strategies, and early-warning system design. Beyond immediate applications, this framework provides a transferable methodology for assessing drought-induced vulnerability in biodiverse ecosystems, supporting conservation and climate-adaptive management. Full article