Search Results (18,733)

The AKT1 oncogene is related to various cancers due to its critical role in the PIC3CA/AKT1 pathway; however, most of the studies screened the hotspot mutation AKT1 (E17K) with various incidences. Low frequency or lack of AKT1 (E17K) mutation was reported in prostate cancer (PC) patients. This study aims to explore genetic alterations in the AKT1 PH domain by extending the sequencing to include AKT1 gene exons 3 and 4. Genomic DNA was extracted from 84 Formalin-Fixed Paraffin-Embedded samples of PC patients in Jordan, and then subjected to PCR and sequencing for the targeted exons. This study revealed the presence of two novel mutations (N53Y and Q59K) and a high frequency of mutations in exon 4, with a lack of mutations in the E17K hotspot. Nine missense and two synonymous mutations were detected in exon 4 (Phe27Tyr, Phe27Leu, Ala58Thr, Ser56Phe, Arg41Trp, Phe35Leu, Asp32Glu, Phe35Tyr, and Gln43Lys) and (Ser56 and Glu40), respectively. Two synonymous mutations were detected in exon 3 (Leu12 and Ser2). It is concluded that there is a high frequency of AKT1 mutation in PC patients in Jordan with two novel missense mutations in the Pleckstrin homology (PH) domain. E17K hotspot mutation was not detected in any tested samples, which underlined the significant role of mutations in other AKT1 exons in PC development. Full article

We analyze a rotating black hole (BH) in a four-dimensional space-time embedded in five-dimensional flat bulk. In Boyer–Lindquist coordinates, we use a generic extension of the Kerr metric by the line element of Gürses–Gürsey metric. We discuss their horizon properties and shadow cast which is tailored by the influence of the extrinsic curvature. By means of the model based on the Nash–Greene theorem, we analyze the Gürses–Gürsey metric embedded in five dimensions acting as a rotating “charged” BH which may be regarded as a source of ultrahigh-energy cosmic rays (UHECRs). We also show that this type of BH presents a different structure of the accretion disk which is modified by the extrinsic curvature leading to an enlargement of the photons ring and an increase in the BH’s inner shadow. In the presence of a magnetic field, our initial results suggest that such BHs may be efficient free-test particle accelerators orbiting the inner stable circular orbit (ISCO). Full article

The rapid increase in scientific publications has made it challenging to keep up with the latest advancements. Conducting systematic reviews using traditional methods is both time-consuming and difficult. To address this, new review formats like rapid and scoping reviews have been introduced, reflecting an urgent need for efficient information retrieval. This challenge extends beyond academia to many organizations where numerous documents must be reviewed in relation to specific user queries. This paper focuses on improving document ranking to enhance the retrieval of relevant articles, thereby reducing the time and effort required by researchers. By applying a range of natural language processing (NLP) techniques, including rule-based matching, statistical text analysis, word embeddings, and transformer- and LLM-based approaches like Mistral LLM, we assess the article’s similarities to user-specific inputs and prioritize them according to relevance. We propose a novel methodology, Weighted Semantic Matching (WSM) + MiniLM, combining the strengths of the different methodologies. For validation, we employ global metrics such as precision at K, recall at K, average rank, median rank, and pairwise comparison metrics, including higher rank count, average rank difference, and median rank difference. Our proposed algorithm achieves optimal performance, with an average recall at 1000 of 95% and an average median rank of 185 for selected articles across the five datasets evaluated. These findings give promising results in pinpointing the relevant articles and reducing the manual work. Full article

Promoting the deployment and application of embedded systems in complex forest scenarios is an inevitable developmental trend in advanced intelligent forestry equipment. Unstructured roads, which lack effective artificial traffic signs and reference objects, pose significant challenges for driverless technology in forest scenarios, owing to their high nonlinearity and uncertainty. In this research, an unstructured road parameterization construction method, “DeepLab-Road”, based on tight coupling of multisource information is proposed, which aims to provide a new segmented architecture scheme for the embedded deployment of a forestry engineering vehicle driving assistance system. DeepLab-Road utilizes MobileNetV2 as the backbone network that improves the completeness of feature extraction through the inverse residual strategy. Then, it integrates pluggable modules including DenseASPP and strip-pooling mechanisms. They can connect the dilated convolutions in a denser manner to improve feature resolution without significantly increasing the model size. The boundary pixel tensor expansion is then completed through a cascade of two-dimensional Lidar point cloud information. Combined with the coordinate transformation, a quasi-structured road parameterization model in the vehicle coordinate system is established. The strategy is trained on a self-built Unstructured Road Scene Dataset and transplanted into our intelligent experimental platform to verify its effectiveness. Experimental results show that the system can meet real-time data processing requirements (≥12 frames/s) under low-speed conditions (≤1.5 m/s). For the trackable road centerline, the average matching error between the image and the Lidar was 0.11 m. This study offers valuable technical support for the rejection of satellite signals and autonomous navigation in unstructured environments devoid of high-precision maps, such as forest product transportation, agricultural and forestry management, autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation. Full article

Hyperbolic metamaterials (HMMs) are highly anisotropic materials with the unique property of generating electromagnetic modes. Understanding how these materials can be applied to control the propagation of light waves remains a major focus in photonics. In this study, we inserted a finite-size HMM rod into the point defect of two-dimensional photonic crystals (PhCs) and investigated the unique cavity modes of this hybrid system. The HMM enhances the efficiency of the cavity system in controlling light transmission. Numerical results demonstrate that the cavity modes based on HMMs can be categorized into various types, showing high Q-factors and promising potential for resonant modulation. Furthermore, the switching performance of the cavity with an HMM rod was examined, revealing that the finite-size HMM modes are highly frequency-sensitive and suitable for nonlinear controlled all-optical switching. These switches, characterized by low power consumption and high extinction ratios, are highly suitable for integration into photonic systems. Our investigation on the new type of HMM cavity illustrates that anisotropic materials can be effectively applied in cavity systems to generate highly efficient modes for filtering and switching. Full article

Yarrow (Achillea millefolium L., AM) is a widely used medicinal plant, with its essential oil highly valued in the cosmetic industry. In view of the numerous biological effects, however, microencapsulation, due to its ability to protect sensitive constituents, transform liquids into solid-state material, and provide modification of release kinetics, might open up new possibilities for the biomedical utilization of yarrow essential oil (AMO). In the current work, yarrow plantation was established by its propagation from spontaneous flora. Following the steam distillation of aerial parts, the chemical composition of the essential oil was determined by GC-MS analysis and compared with two commercial samples. This study concludes that Achillea millefolium L. from this region, given the environmental conditions, produces high-azulene-content essential oil. Furthermore, microencapsulation of AMO was successfully performed by complex coacervation into gelatin (GE) and gum arabic (GA) based core–shell microcapsules (MCs). According to the optical microscopic investigation, the particle sizes of the formed polynucleated microcapsules ranged from 14 to 132 µm, with an average of 47 µm. The assessment of morphology by SEM analysis of the freeze-dried form revealed a sponge-like character with embedded circular structures. The microencapsulation was confirmed by FT-IR spectroscopy and differential scanning calorimetry (DSC), while an encapsulation efficiency of 87.6% was determined by UV spectroscopy. GC-MS analysis revealed that microencapsulation preserves the key components of the essential oil. It was concluded that AMO can be effectively processed by complex coacervation followed by freeze-drying into solid-state material for new applications. Full article

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network’s adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms. Full article

In the last few years, the use of convolutional neural networks (CNNs) in intrusion detection domains has attracted more and more attention. However, their results in this domain have not lived up to expectations compared to the results obtained in other domains, such as image classification and video analysis. This is mainly due to the datasets used, which contain preprocessed features that are not compatible with convolutional neural networks, as they do not allow a full exploit of all the information embedded in the original network traffic. With the aim of overcoming these issues, we propose in this paper a new efficient convolutional neural network model for network intrusion detection based on raw traffic data (pcap files) rather than preprocessed data stored in CSV files. The novelty of this paper lies in the proposal of a new method for adapting the raw network traffic data to the most suitable format for CNN models, which allows us to fully exploit the strengths of CNNs in terms of pattern recognition and spatial analysis, leading to more accurate and effective results. Additionally, to further improve its detection performance, the structure and hyperparameters of our proposed CNN-based model are automatically adjusted using the self-adaptive differential evolution (SADE) metaheuristic, in which symmetry plays an essential role in balancing the different phases of the algorithm, so that each phase can contribute in an equal and efficient way to finding optimal solutions. This helps to make the overall performance more robust and efficient when solving optimization problems. The experimental results on three datasets, KDD-99, UNSW-NB15, and CIC-IDS2017, show a strong symmetry between the frequency values implemented in the images built for each network traffic and the different attack classes. This was confirmed by a good predictive accuracy that goes well beyond similar competing models in the literature. Full article

Composite materials such as molybdenum carbides, nitrides, oxides, and mixed anionic compounds like Mo(C,N,O)_x embedded in carbonaceous matrix exhibit promising potential as anode materials for lithium batteries, with a preference for fine-grained morphologies. In this study, we present a novel synthetic approach involving an inorganic–organic hybrid precursor precipitated from aqueous solutions of ammonium heptamolybdate and one of two organic species: 1,8-diaminonaphthalene (1,8-DAN) or hexamethylenediamine (HMD). The precipitation reaction can be carried out in a beaker and in a continuous process using a microjet reactor. This enables the synthesis of precursor material on the gram scale within minutes. The pyrolysis of these precursors yields mixtures of Mo(C,N,O)_x, MoO₂, Mo₂C, Mo₂N, and Mo, with the choice of organic compound significantly influencing the resulting phases and the excess carbon content in the pyrolyzed product. Notably, the pyrolysis process maintains the size and morphology of the micro- to nanometer-sized starting materials. Full article

Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes an estimation method considering the influence of frequency and voltage characteristics on the power deficit during transients. Specifically, the traditional swing equations-based inertia estimation model is improved by embedding linearized frequency and voltage factors. On this basis, the snake optimization algorithm is utilized to identify the power system inertia constant due to its strong global search ability and fast convergence speed. Finally, the proposed inertia estimation method is validated in four test systems, and the results show the effectiveness of the proposed method. Full article

Interior design is considered and practiced as a visual discipline in architecture. The environment and buildings are appreciated through visual representations. We explored how sensory interactions shape a genuine multisensory experience with visual stimulation as a primary focus in architecture and interior design. It is important to consider different factors that contribute to visual stimulation when designing spaces. Visual stimulation is experienced differently, depending on the observer, and it is important to understand how children perceive stimuli. Therefore, we determined the visual factors captured by children. The embedded design method was used for qualitative mapping of visual factors and 3D animation creation for visualization. Eye-tracking experiments were conducted to examine the factors that captured the attention of children. Children were attracted to moving objects such as videos, followed by images on walls, playgrounds, windows, and furniture. Fostering positive distraction is important in designing spaces for children. Full article

The ongoing replacement of synchronous machine generators (SMs) with converter-interface generators (CIGs) is raising the voltage unbalance of power systems, affecting power quality and grid stability. This paper focuses on a key power quality index for power systems, i.e., the voltage unbalance factor. The purpose of this work is twofold. First, it presents the generalization of a three-phase power flow algorithm developed by University of Padova, named PFPD_3P, to assess the voltage unbalance factors of power systems supplied by CIGs. In particular, it is demonstrated that CIGs can be modelled as three-phase PV/PQ constraints embedding their positive-, negative- and zero-sequence admittances. Then, the concept of three-phase contingency analysis is introduced. Indeed, for static security evaluation, the classical single-phase contingency analysis may no longer be sufficient, as it lacks power quality computations, e.g., voltage/current unbalance factors. Numerical simulations evaluating the unbalance factors due to different generation mix scenarios and contingencies are tested on the Italian extra-high-voltage/high-voltage (EHV/HV) grid. The choice of this network relies on its representativeness, as CIGs are the majority of new installations in the Italian generation mix. Full article

4-Nitroaniline (PNA) is a toxic organic compound commonly found in wastewater, posing significant environmental concerns due to its toxicity and potential carcinogenicity. In this study, the recovery of PNA from aqueous solutions was investigated using a supported liquid membrane (SLM). The membrane, which consists of polypropylene Celgard 2500 (PP-Celg), was embedded with the extractant tributyl phosphate (TBP). Various factors influencing the efficiency of PNA transportation were studied, including the concentration of PNA in the source phase, pH of the source phase, NaOH concentration in the receiving phase, and choice of stripping agents. Optimal conditions for the experiment were determined to be a source phase PNA concentration of 20 ppm at pH 7, distilled water as the receiving phase, TBP as the carrier in the organic phase, and a transport time of 8 h. The extraction process was conducted under ambient temperature and pressure conditions, yielding results indicative of a first-order linearized reaction. Additionally, membrane stability and liquid membrane loss were evaluated. Full article

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks. Full article

Real-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring numerous extensions and applications in several areas. In this review, we present a thorough examination of GATs, covering both diverse approaches and a wide range of applications. We examine the principal GAT-based categories, including Global Attention Networks, Multi-Layer Architectures, graph-embedding techniques, Spatial Approaches, and Variational Models. Furthermore, we delve into the diverse applications of GATs in various systems such as recommendation systems, image analysis, medical domain, sentiment analysis, and anomaly detection. This review seeks to act as a navigational reference for researchers and practitioners aiming to emphasize the capabilities and prospects of GATs. Full article