Search Results (15,419)

Background: The complete loss of S-methyl-5′-thioadenosine phosphorylase (MTAP) expression, often due to homozygous 9p21 deletion, creates a druggable vulnerability in cancer cells. Methods: A total of 769 primary pancreatic ductal adenocarcinomas were analyzed on tissue microarrays with MTAP immunohistochemistry (IHC) and 9p21 fluorescence in situ hybridization (FISH). Intratumoral heterogeneity was assessed on a “heterogeneity” TMA containing up to nine samples from different areas of 236 primary tumor and nodal metastases, and whole sections of all tumor blocks from 19 cancers. Results: MTAP expression loss was found in 181 (37.9%) of 478 interpretable primary tumors and was unrelated to pT, pN, grade, and tumor size. MTAP expression loss was homogenous in 37.6% and heterogeneous in 1.1% of the 181 tumors, with at least three evaluable samples on the heterogeneity TMA. On whole sections, 1 of 19 tumors showed heterogeneous MTAP loss. The correlation between IHC and FISH was nearly perfect, with 98.8% of MTAP-deficient samples showing a 9p21 deletion. Conclusions: MTAP expression loss is frequent, caused by homozygous deletion, and mostly homogeneous in pancreatic ductal adenocarcinomas. Considering also their aggressive clinical behavior, pancreatic adenocarcinomas may represent an ideal cancer type for studying new drugs targeting MTAP-deficient cancer cells in clinical trials. Full article

This research presents a hybrid approach of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) model for greenhouse environmental monitoring, integrating machine learning and Internet of Things (IoT)-based sensing to enhance climate prediction and classification. Unlike traditional single-method approaches, this dual-model system provides a comprehensive framework for real-time climate control, optimizing temperature and humidity forecasting while enabling accurate weather classification. The LSTM model excels in capturing sequential patterns, achieving superior temperature prediction performance with a Root-Mean-Square Error (RMSE) of 0.0766, Mean Absolute Error (MAE) of 0.0454, and coefficient of determination (R²) of 0.8825. For humidity forecasting, our comparative analysis revealed that the Simple Recurrent Neural Network (RNN) demonstrates the best accuracy (RMSE: 5.3034, MAE: 3.8041, R²: 0.8187), an unexpected finding that highlights the importance of parameter-specific model selection. Simultaneously, the SVM model classifies environmental states with an accuracy of 0.63, surpassing traditional classifiers such as Logistic Regression and K Nearest Neighbors (KNN). To enhance real-time data collection and transmission, the ESP NOW wireless protocol is integrated, ensuring low latency and reliable communication between greenhouse sensors. The proposed hybrid LSTM-SVM system, combined with IoT technology, represents a significant advancement in proactive greenhouse management, offering a scalable and sustainable solution for optimizing plant growth, resource allocation, and climate adaptation. Full article

Due to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization configuration method based on Particle Swarm Optimization (PSO) for the capacity configuration of long-endurance hydrogen-powered hybrid unmanned aerial vehicles (UAVs). By constructing a hydrogen-powered hybrid UAV energy system model, an uncertainty model for the energy system, and multi-timescale comprehensive evaluation indicators and corresponding objective functions, the capacity configuration is determined using a two-stage stochastic programming model solved by CPLEX in MATLAB. The two-stage stochastic programming model consists of the first stage, which involves capacity optimization through PSO, and the second stage, which employs Monte Carlo method for random wind field sampling. The research provides a theoretical foundation for the application of the two-stage optimization capacity configuration method in the field of long-endurance hydrogen-powered hybrid UAVs. Full article

With the continuous development of intelligent transportation systems (ITSs), traffic flow prediction methods have become the cornerstone of this technology. This paper comprehensively reviews the traffic flow prediction methods used in ITSs and divides them into three categories: statistics-based, machine learning-based, and deep learning-based methods. Although statistics-based methods have lower data requirements and machine learning methods have faster calculation speeds, this paper concludes that deep learning methods have the best overall effect after a comprehensive analysis of the principles, advantages, limitations, and practical applications of each method. Deep learning methods can overcome many limitations that traditional statistical methods and machine learning methods cannot surpass, such as the ability to model complex nonlinear relationships. Experimental results show that hybrid neural networks are significantly superior to traditional methods in terms of their prediction accuracy and generalization abilities. By combining multiple models and techniques, hybrid neural networks can improve the accuracy of traffic flow prediction under different conditions. Although deep learning methods have achieved remarkable success in short-term prediction, challenges still exist, such as the generalization of models in different traffic scenarios and the difficulty of long-term traffic flow prediction. Finally, this paper discusses future research directions and anticipates the future development of ITS technology. Full article

Nanostructured hybrid materials based on the combination of semiconductor QDs and GO are promising candidates for different optoelectronic and catalytic applications and being able to produce such hybrid materials in solution will expand their possible range of applications. In the current work, capping ligand-exchange procedures have been developed to replace the lead oleate normally found on the surface of PbS QDs synthesized by the popular hot-injection method. After the capping ligand-exchange process, the QDs are water soluble, which makes them soluble in most GO solutions. Solution-processed nanostructured hybrid materials based on GO flakes decorated with ligand-exchanged (EDT, TBAI and L-Cysteine) PbS QDs were synthesized by combining PbS QDs and GO solutions. Afterward, the resulting hybrid materials were thoroughly characterized by means of FTIR, XPS, Raman, UV-Vis-NIR and photoluminescence spectroscopy, as well as SEM and TEM techniques. The results indicate a clear surface chemistry variation in the capping ligand-exchanged PbS QDs, showing the presence of the exchanged ligand molecules. Thin films from the solution-processed nanostructured hybrid materials were deposited by the spin coating technique, and their optoelectronic properties were studied. Depending on the capping ligand molecule, the photoresponse and resistance of the thin films varied; the sample with the EDT ligand exchange showed the highest photoresponse and the lowest resistance. This surface chemistry had a direct effect on the charge carrier transfer and transport behavior of the nanostructured hybrid materials synthesized. These results show a novel and accessible route for synthesizing solution-processed and affordable nanostructured hybrid materials based on semiconductor QDs and GO. Additionally, the importance of the surface chemistry displayed by the PbS QDs and GO was clearly seen in determining the final optoelectronic properties displayed by their hybrid materials. Full article

This study addresses the growing demand for scalable data encryption by evaluating the performance of AES (Advanced Encryption Standard) encryption and decryption using CBC (Cipher Block Chaining) and CTR (Counter Mode) modes across various CPU (Central Processing Unit) and GPU (Graphics Processing Unit) hardware models. The objective is to highlight GPU acceleration benefits and propose an optimized hybrid CPU–GPU workflow for large-scale data security. Methods include benchmarking encryption performance with provided data, mathematical models, and computational analysis. The results indicate significant performance gains with GPU acceleration, particularly for large datasets, and demonstrate that the hybrid CPU–GPU approach balances speed and resource utilization efficiently. Full article

This study presents an enhanced Faster R-CNN framework that incorporates elliptical bounding boxes to significantly improve building detection in off-nadir imagery, effectively reducing severe geometric distortions caused by oblique sensor angles. Off-nadir imagery enhances architectural detail capture and reduces occlusions, but conventional bounding boxes, such as axis-aligned and rotated bounding boxes, often fail to localize buildings distorted by extreme perspectives. We propose a hybrid method integrating elliptical bounding boxes for curved structures and rotated bounding boxes for tilted buildings, achieving more precise shape approximation. In addition, our model incorporates a squeeze-and-excitation mechanism to refine feature representation, suppress background noise, and enhance object boundary alignment, leading to superior detection accuracy. Experimental results on the BONAI dataset demonstrate that our approach achieves a detection rate of 91.96%, significantly outperforming axis-aligned bounding boxes (65.75%) and rotated bounding boxes (87.13%) in detecting irregular and distorted buildings. By providing a highly robust and adaptable detection strategy, our approach establishes a new standard for accurate and shape-aware building recognition in off-nadir imagery, significantly improving the detection of distorted, rotated, and irregular structures. Full article

Flexible electrochromic devices (ECDs) represent a distinctive category in optoelectronics, leveraging advanced materials to achieve tunable coloration under applied electric voltage. This review delves into recent advancements in electrode materials for ECDs, with a focus on silver nanowires, metal meshes, conductive polymers, carbon nanotubes, and transparent conductive ceramics. Each material is evaluated based on its manufacturing methods and integration potential. The analysis highlights the prominent role of transparent conductive ceramics and conductive polymers due to their versatility and scalability, while also addressing challenges such as environmental stability and production costs. Use of other alternative materials, such as metal meshes, carbon materials, nanowires and others, are presented here as a comparison as well. Emerging hybrid systems and advanced coating techniques are identified as promising solutions to overcome limitations regarding flexibility and durability. This review underscores the critical importance of electrode innovation in enhancing the performance, sustainability, and application scope of flexible ECDs for next-generation technologies. Full article

α-Tocopherol (α-T) predominates in photosynthetic tissues, while tocotrienols (T3s) are reported very rarely. The genus Hypericum stands out as one of the few exceptions. Given the potential health benefits associated with tocotrienols, sourcing them from natural origins is of interest. The proper selection of plant material and the drying conditions are crucial steps in this process. Therefore, in the present study, we investigated the effects of four different drying techniques (freeze-drying, microwave–vacuum-, infrared oven and air-drying) on the tocochromanol content in leaves of three Hypericum species: H. androsaemum, H. pseudohenryi, and H. hookerianum and one hybrid H. × inodorum. The total tocochromanol content in the freeze-dried leaves harvested in September was 68.1–150.6 mg/100 g dry weight. α-T constituted 66.7–85.9% (w/w), while tocotrienols constituted 13–32% (w/w). H. pseudohenryi was characterized by the lowest tocotrienol content, while H. androsaemum and H. hookerianum had the highest, with δ-T3 and γ-T3, respectively, being predominant. Tocotrienols were more stable during drying than α-T. The greatest decrease in α-T content was observed during air-drying in the presence of sunlight, with a 27% difference compared to the absence of sunlight. The species and harvest time are factors that more strongly affect the tocotrienol content in the Hypericum leaves than the selected drying method. Full article

Tea is one of the world’s major non-alcoholic beverages, popular for its health benefits and flavor. Purple-bud tea is particularly rich in anthocyanins, the concentration of which varies depending on the tea cultivar and cultivation conditions. While the genetic regulation of anthocyanin accumulation is well understood, the impact of environmental factors, such as light, on anthocyanin synthesis is less documented. In this study, we analyzed the anthocyanin content and the expression levels of anthocyanin biosynthesis genes (ABGs), CsAN1, and CsHY5, under different light intensities and durations. The expression of both CsAN1 and ABGs was significantly induced by light, with an intensity of 8000 lx particularly effective in promoting anthocyanin accumulation. Furthermore, we explored the effect of shading on anthocyanin content, finding that fifty percent shading reduced anthocyanin content by nearly half. Finally, dual-luciferase reporter assays and yeast one-hybrid assays confirmed the direct regulation of CsHY5 on CsAN1. These findings offer insights into the regulatory mechanisms underlying light-induced anthocyanin biosynthesis in tea plants and suggest a potential method for controlling anthocyanin accumulation in tea production. Full article

Accurate primer and probe design is essential for molecular applications, including PCR, qPCR, and molecular multiparameter assays like microarrays. The novel software tool AssayBLAST addresses this need by simulating interactions between oligonucleotides and target sequences. AssayBLAST handles large sets of primer and probe sequences simultaneously and supports comprehensive assay designs by allowing users to identify off-target binding, calculate melting temperatures, and ensure strand specificity, a critical but often overlooked aspect. AssayBLAST performs two optimized BLAST-based searches for each primer or probe sequence, checking the forward and reverse strands for off-target interactions and strand-specific binding accuracy. The results are compiled into a mapping table containing binding sites, mismatches, and strand orientation, allowing users to validate large sets of oligonucleotides across predefined custom databases for a complete and optimal theoretical assay design. AssayBLAST was evaluated against experimental Staphylococcus aureus microarray data, achieving 97.5% accuracy in predicting probe–target hybridization outcomes. This high accuracy demonstrates the method’s effectiveness in reliably using BLAST hits and mismatch counts to predict microarray results. AssayBLAST provides a reliable, scalable solution for in silico primer and probe validation, effectively supporting large-scale assay designs and optimizations. Its accurate prediction of hybridization outcomes demonstrates its utility in enhancing the efficiency and reliability of molecular assays. Full article

Manual geometric and semantic alignment of inspection data with existing digital models (field-to-model data registration) and on-site access to relevant information (model-to-field data registration) represent cumbersome procedures that cause significant loss of information and fragmentation, hindering the efficiency of civil infrastructure inspections. To address the bidirectional registration challenge, this study introduces a high-accuracy automatic registration method and system based on Augmented Reality (AR) that streamlines data exchange between the field and a knowledge graph-based Digital Twin (DT) platform for infrastructure management, and vice versa. A centimeter-level 6-DoF pose estimation of the AR device in large-scale, open unprepared environments is achieved by implementing a hybrid approach based on Real-Time Kinematic and Visual Inertial Odometry to cope with urban-canyon scenarios. For this purpose, a low-cost and non-invasive RTK receiver was prototyped and firmly attached to an AR device (i.e., Microsoft HoloLens 2). Multiple filters and latency compensation techniques were implemented to enhance registration accuracy. The system was tested in a real-world scenario involving the inspection of a highway viaduct. Throughout the use case inspection, the system seamlessly and automatically provided field operators with on-field access to existing DT information (i.e., open BIM models) such as georeferenced holograms and facilitated the enrichment of the asset’s DT through the automatic registration of inspection data (i.e., images) with the open BIM models included in the DT. This study contributes to DT-based civil infrastructure management by establishing a bidirectional and seamless integration between virtual and physical entities. Full article

This review analyzes the role of artificial intelligence (AI) and automation in optimizing vegetable production within hydroculture systems. Methods: Following the PRISMA methodology, this study examines research on IoT-based monitoring and AI techniques, particularly Deep Neural Networks (DNNs), K-Nearest Neighbors (KNNs), Fuzzy Logic (FL), Convolutional Neural Networks (CNNs), and Decision Trees (DTs). Additionally, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models were analyzed due to their effectiveness in processing temporal data and improving predictive capabilities in nutrient optimization. These models have demonstrated high precision in managing key parameters such as pH, temperature, electrical conductivity, and nutrient dosing to enhance crop growth. The selection criteria focused on peer-reviewed studies from 2020 to 2024, emphasizing automation, efficiency, sustainability, and real-time monitoring. After filtering out duplicates and non-relevant papers, 72 studies from the IEEE, SCOPUS, MDPI, and Google Scholar databases were analyzed, focusing on the applicability of AI in optimizing vegetable production. Results: Among the AI models evaluated, Deep Neural Networks (DNNs) achieved 97.5% accuracy in crop growth predictions, while Fuzzy Logic (FL) demonstrated a 3% error rate in nutrient solution adjustments, ensuring reliable real-time decision-making. CNNs were the most effective for disease and pest detection, reaching a precision rate of 99.02%, contributing to reduced pesticide use and improved plant health. Random Forest (RF) and Support Vector Machines (SVMs) demonstrated up to 97.5% accuracy in optimizing water consumption and irrigation efficiency, promoting sustainable resource management. Additionally, LSTM and RNN models improved long-term predictions for nutrient absorption, optimizing hydroponic system control. Hybrid AI models integrating machine learning and deep learning techniques showed promise for enhancing system automation. Conclusion: AI-driven optimization in hydroculture improves nutrient management, water efficiency, and plant health monitoring, leading to higher yields and sustainability. Despite its benefits, challenges such as data availability, model standardization, and implementation costs persist. Future research should focus on enhancing model accessibility, interoperability, and real-world validation to expand AI adoption in smart agriculture. Furthermore, the integration of LSTM and RNN should be further explored to enhance real-time adaptability and improve the resilience of predictive models in hydroponic environments. Full article

The control methods of energy systems can be categorized into grid-following and grid-forming types. The grid-following control method relies on grid synchronization and is prone to stability issues in weak grid conditions. By contrast, the grid-forming control method exhibits synchronous machine characteristics, providing voltage support to the system, but potentially introducing stability risks under strong grid conditions. Constructing a grid-following and grid-forming hybrid renewable energy plant can effectively enhance the system’s support capability and ensure reliable operation. However, the interactions among multiple inverters are complex, and traditional modeling methods are inadequate to meet the modeling requirements for such systems. To effectively address this problem, this paper presents a reduced-order modeling method that simplifies the complex system into a simple system consisting of an equivalent grid-following, an equivalent grid-forming, and grid impedance through frequency decoupling and the aggregation of similar inverters. Furthermore, this study employs both the Nyquist stability criterion and the harmonic characteristic analysis method to elucidate how the capacity ratio between grid-following and grid-forming affects system stability. Full article

To systematically address the intricate multiple criteria decision-making (MCDM) challenges to practical situations where uncertain and hesitant information plays a critical role in guiding optimal choices. In this article, we introduce the concept of $\mathfrak{m}$-polar Q-hesitant fuzzy ($\mathcal{MPQHF}$) $\mathfrak{B}\mathrm{C}\mathbb{K}/\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras, combining $\mathfrak{m}$-PFS theory with Q-hesitant fuzzy set theory in the framework of $\mathfrak{B}\mathrm{C}\mathbb{K}$/$\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras. This innovative approach enhances the attitudes of uncertainty, vagueness, and hesitance of data in decision-making processes. We investigate the features and actions of this proposed hybrid approach to fuzzy sets and hesitant fuzzy sets, focusing on $\mathcal{MPQHF}$ subalgebras, and explore the characteristics of several kinds of ideals under $\mathfrak{B}\mathrm{C}\mathbb{K}$/$\mathfrak{B}\mathrm{C}\mathbb{I}$ algebras. It also showed that it can better represent complex levels of uncertainty than regular sets. The proposed method’s theoretical framework offers a better way to show uncertain data in areas like engineering, computer science, and computational mathematics. By linking theoretical advancements of $\mathcal{MPQHF}$ sets with practical applications, we highlight the benefits and challenges of this approach. Demonstrating the practical uses of the $\mathcal{MPQHF}$ sets aims to encourage broader adoption. Symmetry plays a vital role in algebraic structure and is used in various fields like decision-making, encryption, pattern recognition problems, and automata theory. Furthermore, this work enhances the understanding of algebraic structures and offers a robust tool for career exploration and development through improved decision-making methodologies. Full article