Search Results (14,889)

With the continuous progress and innovation of petroleum engineering technology, the development of new oilfield additives with superior environmental benefits has attracted widespread attention. Konjac glucomannan (KGM) is a natural resource characterized by abundant availability, low cost, biodegradability, and environmental compatibility. Konjac gum easily forms a weak gel network in water, but its water solubility and thermal stability are poor, and it is easily degraded at high temperatures. Therefore, its application in drilling fluid and fracturing fluid is limited. In this paper, a method of carboxymethyl modification of KGM was developed, and a carboxymethyl group was introduced to adjust KGM’s hydrogel forming ability and stability. Carboxymethylated Konjac glucomannan (CMKG) is a water-soluble anionic polysaccharide derived from natural Konjac glucomannan. By introducing carboxymethyl groups, CMKG overcomes the limitations of the native polymer, such as poor solubility and instability, while retaining its safe and biocompatible nature, making it an effective natural polymer additive for oilfield applications. The results show that when used as a drilling fluid additive, CMKG can form a stable three-dimensional gel network through molecular chain cross-linking, significantly improving the rheological properties of the mud. Its unique gel structure can enhance the encapsulation of clay particles and inhibit clay hydration expansion. When used as a fracturing fluid thickener, the viscosity of the gel system formed by CMKG at 0.6% (w/v) is superior to that of the weak gel system of KGM. The heat resistance/shear resistance tests confirm that the gel structure remains intact under high-temperature and high-shear conditions, meeting the sand-carrying capacity requirements for fracturing operations. The gel-breaking experiment shows that the system can achieve controlled degradation within 300 min, in line with on-site gel-breaking specifications. This modification process not only improves the rheological properties and water solubility of the CMKG gel but also optimizes the gel stability and controlled degradation through molecular structure adjustment. Full article

Credit card fraud detection remains a major challenge due to severe class imbalance and the constantly evolving nature of fraudulent behaviors. To address these challenges, this paper proposes a hybrid framework that integrates a Variational Autoencoder (VAE) for probabilistic anomaly detection, a Graph Attention Network (GAT) for capturing inter-transaction relationships, and a stacking ensemble with XGBoost for robust prediction. The joint use of VAE anomaly scores and GAT-derived node embeddings enables the model to capture both feature-level irregularities and relational fraud patterns. Experiments on the European Credit Card and IEEE-CIS Fraud Detection datasets show that the proposed approach outperforms baseline models by up to 15% in F1-score, achieving values above 0.980 with AUCs reaching 0.995. These results demonstrate the effectiveness of combining unsupervised anomaly detection with graph-based learning within an ensemble framework for highly imbalanced fraud detection problems. Full article

Background: Polycarbonate–urethane (PCU) is a recently developed bearing surface used in prosthetic hip surgery. It offers several theoretical advantages, including an elasticity modulus similar to that of natural cartilage, good lubrication properties, low wear, and the possibility of using large heads. However, comparative clinical experience is limited. The purpose of this study was to analyze the results of the PCU bearing surface and compare them with those of ceramic-on-ceramic (CoC) bearings using the same femoral stem model. (2) Methods: Following a propensity score matching analysis of a prospectively collected database, patients with a primary total hip arthroplasty aged between 18 and 60 years were included. Subjects were divided into two groups (PCU and CoC). Demographic, patient satisfaction, and implant survival data were recorded. Clinical results were evaluated using the Harris Hip Score (HHS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). (3) Results: A total of 105 patients were included in each group. All patients exhibited a positive evolution on both the HHS and the WOMAC subscales between pre-op and one year post-op, no statistically significant differences being found between the groups with respect to improvement on the HHS (p = 0.172) or the pain (p = 0.523), stiffness (p = 0.448), and physical function (p = 0.255) subscales of the WOMAC. Head sizes in the PCU group were found to be larger, but this was not seen to have any effect on the patients’ clinical status or the prostheses’ dislocation rate. Although the complication rate was similar across the groups (p = 0.828), the incidence of squeaking was higher in the PCU group (p = 0.010). No differences were observed when comparing the implant survival rate (p = 0.427). nor in mean patient satisfaction (p = 0.138). (4) Conclusions: No differences were found in terms of clinical results, complications, implant survival, or patient satisfaction between the bearing surfaces under analysis, indicating that all of them are valid alternatives in total hip replacement, although the higher proportion of squeaking observed makes it advisable to exercise some caution. Full article

Hepatocellular carcinoma (HCC) is the third most common cancer worldwide, widely prevalent across many countries, and poses a serious threat to human health. With changes in its epidemiology, the incidence of HCC is expected to continue rising. As a class of organic molecules widely distributed in nature, quinone compounds possess notable antioxidant, antibacterial, and antitumor properties. This article selects several quinone compounds that have shown notable research progress in recent years and artificially categorizes them into “plant-derived quinone compounds” and “non-plant-derived quinone compounds.” We then provide a detailed review of the research findings regarding HCC in vitro and in vivo experiments and clinical trials, including their potential toxic side effects. Additionally, based on the varying toxicity reduction of several selected plant-derived quinones when combined with doxorubicin, we further hypothesize that these plant-derived quinone compounds may also exert detoxifying effects on other non-plant-derived quinones discussed in this article. In summary, quinone compounds still hold significant research value and development potential in the fight against HCC. At the same time, we hope our review will provide valuable insights and inspiration for future research in this field. Full article

The study presents the results of investigations into various types of anhydrous pyrolysis aimed at determining the kinetic parameters of hydrocarbon generation processes from source rocks. Surface outcrop samples from the Silesian, Dukla, and Skole units, characterized by a low level of thermal maturity, were used as experimental material. The samples predominantly represented the Menilite Beds from the aforementioned three units, but also included Istebna, Lgota, Verovice, and Spas beds, which exhibit significantly lower parameters that describe generation properties. The anhydrous pyrolysis experiments provided information on the rate of organic matter decomposition (TG/DSC), the degree of conversion (Rock-Eval), the quality of the obtained products (Py/GC), and the isotopic composition of the gaseous products (Py/GC/IRMS). Chromatographic analyses confirmed the oil-prone nature of kerogen contained in the Menilites from the Dukla Unit (Tylawa area), the Silesian Unit (Iwonicz fold), and the Skole Unit, revealing an equal share of all hydrocarbon fractions: C₁–C₉, C₁₀–C₁₅, and C₁₅₊. Through the integration of pyrolytic studies conducted on potential source rocks in the polish Outer Carpathians, a new type of information was obtained regarding the rate of organic matter decomposition, as well as the fractional and isotopic composition of the pyrolysis products. The set of obtained results was used to estimate the activation energy and characterize the potential source levels. The innovative aspect of this approach involved the isotopic characterization of gaseous products generated during thermal degradation of the source rocks. These data were subsequently used to establish genetic correlations with natural gases accumulated in hydrocarbon reservoirs of the Carpathian region. It has been demonstrated that pyrolysis using PY-GC-IRMS can yield results comparable to those obtained through generation in natural geological conditions. Full article

We present a (Light)weight (S)atellite–(A)erial feature (M)atching framework (LiteSAM) for robust UAV absolute visual localization (AVL) in GPS-denied environments. Existing satellite–aerial matching methods struggle with large appearance variations, texture-scarce regions, and limited efficiency for real-time UAV applications. LiteSAM integrates three key components to address these issues. First, efficient multi-scale feature extraction optimizes representation, reducing inference latency for edge devices. Second, a Token Aggregation–Interaction Transformer (TAIFormer) with a convolutional token mixer (CTM) models inter- and intra-image correlations, enabling robust global–local feature fusion. Third, a MinGRU-based dynamic subpixel refinement module adaptively learns spatial offsets, enhancing subpixel-level matching accuracy and cross-scenario generalization. The experiments show that LiteSAM achieves competitive performance across multiple datasets. On UAV-VisLoc, LiteSAM attains an RMSE@30 of 17.86 m, outperforming state-of-the-art semi-dense methods such as EfficientLoFTR. Its optimized variant, LiteSAM (opt., without dual softmax), delivers inference times of 61.98 ms on standard GPUs and 497.49 ms on NVIDIA Jetson AGX Orin, which are 22.9% and 19.8% faster than EfficientLoFTR (opt.), respectively. With 6.31M parameters, which is 2.4× fewer than EfficientLoFTR’s 15.05M, LiteSAM proves to be suitable for edge deployment. Extensive evaluations on natural image matching and downstream vision tasks confirm its superior accuracy and efficiency for general feature matching. Full article

Financial time series prediction remains a challenging task due to the inherent non-stationarity, noise, and complex temporal dependencies present in market data. Traditional forecasting methods often fail to capture the multifaceted nature of financial markets, where temporal proximity, trend dynamics, and volatility patterns simultaneously influence price movements. To address these limitations, this paper proposes the Enhanced Multi-Aspect Transformer (EMAT), a novel deep learning architecture specifically designed for stock market prediction. EMAT incorporates a Multi-Aspect Attention Mechanism that simultaneously captures temporal decay patterns, trend dynamics, and volatility regimes through specialized attention components. The model employs an encoder–decoder architecture with enhanced feed-forward networks utilizing SwiGLU activation, enabling superior modeling of complex non-linear relationships. Furthermore, we introduce a comprehensive multi-objective loss function that balances point-wise prediction accuracy with volatility consistency. Extensive experiments on multiple stock market datasets demonstrate that EMAT consistently outperforms a wide range of state-of-the-art baseline models, including various recurrent, hybrid, and Transformer architectures. Our ablation studies further validate the design, confirming that each component of the Multi-Aspect Attention Mechanism makes a critical and quantifiable contribution to the model’s predictive power. The proposed architecture’s ability to simultaneously model these distinct financial characteristics makes it a particularly effective and robust tool for financial forecasting, offering significant improvements in accuracy compared to existing approaches. Full article

This study presents the design and evaluation of a dexterous prosthetic hand featuring five fingers, ten independently actuated joints, and four passively driven joints. The hand’s dexterity is enabled by a novel rigid–flexible coupled finger mechanism that incorporates a 1-active–1-passive joint configuration, which can enhance the dexterity of traditional rigid actuators while achieving a human-like workspace. Each finger is designed with a specific degree of rotational freedom to mimic natural opening and closing motions. This study also elaborates on the mapping of eight-channel electromyography to finger grasping force through improved TCN, as well as the control algorithm for grasping flexible objects. A functional prototype of the prosthetic hand was fabricated, and a series of experiments involving adaptive grasping and handheld manipulation tasks were conducted to validate the effectiveness of the proposed mechanical structure and control strategy. The results demonstrate that the hand can stably grasp flexible objects of various shapes and sizes. This work provides a practical solution for prosthetic hand design, offering promising potential for developing lightweight, dexterous, and highly anthropomorphic robotic hands suitable for real-world applications. Full article

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential. Full article

Instagram is a hugely popular social media site; however, it has also been cited in many times as being a source of low self-esteem, unhappiness, and body dissatisfaction. Despite this, there is potential to use Instagram as a self-care delivery system and create positive changes in users’ mental health by showing them a specific type of image. In this paper, we use Stress Reduction Theory to demonstrate that viewing images of nature on Instagram can improve well-being (H1), by increasing feelings of connectedness with nature (H2). Furthermore, we posit this same influence will elicit more altruistic behaviour from users (H3). In a laboratory experiment, participants accessed images using either the #naturephotography hashtag, or a control hashtag (#bookshelves). Analyses showed that, in line with the proposed positive effects of SRT, viewing natural images improved well-being and positive emotions, and this was at least partially mediated by increased connectedness to nature. Future studies that use a more longitudinal approach, and examine how images can be presented within a more robust psychiatric intervention are then discussed. Full article

The extracranial–intracranial (EC–IC) bypass is a complex neurosurgical procedure performed for cerebral flow augmentation or flow replacement. Anesthetic management of these patients poses significant challenges due to the delicate balance required to maintain cerebral perfusion, often complicated by extensive cardiovascular comorbidities. Despite the complexity of these cases, current literature offers limited guidance on optimal anesthetic strategies. At our high-volume academic institution, we developed a standardized multimodal anesthetic protocol aimed at achieving intraoperative hemodynamic stability and facilitating timely postoperative emergence. A dedicated team of neuroanesthesiologists manages these cases in constant communication with the surgical team, ensuring real-time adjustments aligned with surgical needs and patient physiology. Our experience highlights the importance of individualized anesthetic planning and interdisciplinary coordination. Given the scarcity of published data and the specialized nature of EC–IC bypass procedures, we believe our institutional approach may serve as a useful reference for other centers, particularly those with limited exposure to this complex patient population, and lay the foundation for future prospective trials on optimal anesthetic care for this patient population. Full article

Advancements in electroencephalography (EEG) technology and feature extraction methods have paved the way for wearable, non-invasive systems that enable continuous sleep monitoring outside clinical environments. This study presents the development and evaluation of an EEG-based acquisition system for sleep staging, which can be adapted for wearable applications. The system utilizes a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board to acquire EEG signals from the forehead and in-ear regions under various conditions, including visual and auditory stimuli. Afterward, the acquired signals were processed to extract a wide range of features in time, frequency, and non-linear domains, selected based on their physiological relevance to sleep stages and disorders. The feature set was reduced using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and Principal Component Analysis (PCA), resulting in a compact and informative subset of principal components. Experiments were conducted on the Bitbrain Open Access Sleep (BOAS) dataset to validate the selected features and assess their robustness across subjects. The feature set extracted from a single EEG frontal derivation (F4-F3) was then used to train and test a two-step deep learning model that combines Long Short-Term Memory (LSTM) and dense layers for 5-class sleep stage classification, utilizing attention and augmentation mechanisms to mitigate the natural imbalance of the feature set. The results—overall accuracies of 93.5% and 94.7% using the reduced feature sets (94% and 98% cumulative explained variance, respectively) and 97.9% using the complete feature set—demonstrate the feasibility of obtaining a reliable classification using a single EEG derivation, mainly for unobtrusive, home-based sleep monitoring systems. Full article

Tourist loyalty is vital for destination success, fostering repeat visits and positive word-of-mouth. This study explores the psychological and safety-related factors driving tourist loyalty to natural attractions in Oman, a rising destination known for its stability and safety. Using Social Cognitive Theory as a foundation, the research incorporates perceived risk and novelty seeking as key moderating variables. Data were collected via an online survey of 165 international tourists and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings show that attachment, satisfaction, and novelty seeking significantly affect both attitudinal and behavioral loyalty. While perceived value strongly influences behavioral loyalty, its impact on attitudinal loyalty appears more complex, suggesting possible unobserved mediators. Additionally, risk perception and novelty seeking moderate the link between destination familiarity and loyalty, underscoring the role of tourists’ internal evaluations of safety and desire for new experiences. This study advances the limited literature on tourist loyalty in developing countries by integrating psychological and risk-related dimensions. It offers actionable insights for tourism planners and marketers in Oman: emphasizing the country’s safety reputation, improving satisfaction levels, and crafting experiences that blend familiarity with novelty can enhance tourist loyalty and ensure sustained competitiveness in the global tourism market. Full article

As a sustainable construction material, mudbrick can be used widely in areas where common modern construction materials are not easily accessible but high clay content soil is available. The inclusion of locally available natural fibres in mudbrick could improve its mechanical and erosion resistance performance. This study examines the performance of fibre-reinforced mudbrick from spinifex and laterite soil which are abundant in Australia. The main objective of this study is to evaluate the mechanical and durability performance of spinifex fibre-reinforced mudbricks made with Australian laterite soil, focusing on the influence of fibre content, fibre length, and cement stabilisation. Spinifex fibre length (30 mm, 40 mm, 50 mm), spinifex fibre percentage (0.3%, 0.6%, 0.9%), and cement percentage (5% and 10%) are considered as the experiment variables. Results show that compressive strength generally decreases with fibre size. In this regard, specimens with 0.3% spinifex fibre, 40 mm fibre length, and 10% cement, with an average compressive strength value of 4.1 MPa, were found to have the highest strength among all design mixes. The elastic Young’s modulus was highest for the specimens with 0.3% spinifex fibre, 30 mm fibre length, and 10% cement with a 36.1 MPa. A low amount of longer fibres was found to be more effective in reducing water absorption in samples with higher cement content. Water absorption and compressive strength results suggest that, on average, 0.3–0.5% spinifex content of size 30 mm improves both low and high cement content mudbricks properties. Full article

The development of dressing materials mainly protects the wound, prevents infection, and assists in wound healing. Apart from the most common gauze on the market, different dressing materials can accelerate wound healing. Bacterial cellulose (BC) dressings have had many related studies and applications so far, and other natural or artificial compounds that are beneficial to tissue repair may also be added during the manufacturing process. This study compared the wound healing efficacies of BC dry membrane developed by our team, gauze, commercially available “Tegaderm^TM Hydrocolloid Dressing”, and “AQUACEL^® EXTRA Hydrofiber Dressing”. This study used rats as experimental animals and injured them by scalding. Moreover, Staphylococcus aureus was used to infect wounds to compare the effects on wound healing. We first used NIH-3T3 cells for an in vitro model to confirm that the BC membrane is not harmful to cells. In the animal experiment, wounds were created by scalding and then treated with different dressing materials and doses of S. aureus. After 10 days of treatment, the wound recovery in the BC membrane and AQUACEL^® groups was the most obvious, including angiogenesis in the dermal layer and regeneration of the epidermis layer. Especially without S. aureus infection, inflammatory markers such as cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) expression levels were reduced to those of healthy tissue. In conclusion, we confirmed that the BC dry membrane can accelerate wound healing. In the future, it may provide high-efficiency and less expensive options in the dressing market. Full article