Search Results (244)

Content caching and recommendation for content delivery over the Internet are two key techniques for improving the content delivery effectiveness determined by delivery efficiency and user satisfaction, which is increasingly important in the booming Internet of Things (IoT). While content caching seeks the “greatest common denominator” among users to reduce end-to-end delay in content delivery, personalized recommendation, on the contrary, emphasizes users’ differentiation to enhance user satisfaction. Existing studies typically address them separately rather than jointly due to their contradictory objectives. They focus mainly on heuristics and deep reinforcement learning methods without the provision of performance guarantees, which are required in many real-world applications. In this paper, we study the problem of joint optimization of caching and recommendation in which recommendation is performed in the cached contents instead of purely according to users’ preferences, as in the existing work. We show the NP-hardness of this problem and present a greedy solution with a performance guarantee by first performing content caching according to user request probability without considering recommendations to maximize the aggregated request probability on cached contents and then recommendations from cached contents to incorporate user preferences for cache hit rate maximization. We prove that this problem has a monotonically increasing and submodular objective function and develop an efficient algorithm that achieves a $1-{\displaystyle \frac{1}{e}}\approx 0.63$ approximation ratio to the optimal solution. Experimental results demonstrate that our algorithm dramatically improves the popular least-recently used (LRU) algorithm. We also show experimental evaluations of hit rate variations by Jensen–Shannon Divergence on different parameter settings of cache capacity and user preference distortion limit, which can be used as a reference for appropriate parameter settings to balance user preferences and cache hit rate for Internet content delivery. Full article

The sound speed profile (SSP) is an important feature in the field of ocean acoustics. The accurate estimation of SSP is significant for the development of underwater position, communication, and associated fundamental marine research. The Res-SACNN model is proposed for SSP inversion based on the convolutional neural network (CNN) embedded with the residual network and self-attention mechanism. It combines the spatiotemporal characteristics of sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) data and establishes a nonlinear relationship between satellite remote sensing data and sound speed field by deep learning. The single empirical orthogonal function regression (sEOF-r) method is used in a comparative experiment to confirm the model’s performance in both the time domain and the region. Experimental results demonstrate that the proposed model outperforms sEOF-r regarding both spatiotemporal generalization ability and inversion accuracy. The average root mean square error (RMSE) is decreased by 0.92 m/s in the time-domain experiment in the South China Sea, and the inversion results for each month are more consistent. The optimization ratio hits 71.8% and the average RMSE decreases by 7.39 m/s in the six-region experiment. The Res-SACNN model not only shows more superior inversion ability in the comparison with other deep-learning models, but also achieves strong generalization and real-time performance while maintaining low complexity, providing an improved technical tool for SSP estimation and sound field perception. Full article

Introduction: Herpes zoster (HZ), or shingles, is a vaccine-preventable disease with two approved vaccines: the live-attenuated vaccine (LZV) and the adjuvanted recombinant zoster vaccine (RZV). Evidence on the immunogenicity and adverse events (AEs) following co-administration with other vaccines in adults is limited. This systematic review and meta-analysis aims to evaluate the immunogenicity and safety of HZ vaccines when co-administered with other vaccines. Methods: We followed PRISMA 2020 guidelines and systematically searched multiple databases (January 1950 to February 2024) for studies on HZ vaccination with concomitant vaccines in adults (≥18 years). Observational studies, randomized controlled trials (RCTs), and non-randomized controlled trials were included, excluding reviews, case series, case reports, editorials, and non-English publications. Risk of bias was assessed using Cochrane tools (RoB 2 and ROBINS-I). A meta-analysis compared geometric mean concentration (GMC) ratios and vaccine response rates (VRRs) for RZV, applying the Hartung–Knapp adjustment. For LZV, meta-analysis was not feasible, and results were described narratively. AEs were analyzed using risk ratios and presented in forest plots. Results: Out of 369 search hits, ten RCTs were included. In six RCTs, RZV was co-administered with influenza, COVID-19, pneumococcal vaccines (PCV13, PPSV23), or Tdap. The pooled GMC mean difference was −0.04 (95% CI: −0.10 to 0.02, p = 0.19), and the pooled VRR was 1.00 (95% CI: 0.99 to 1.01, p = 0.59). Local and systemic AEs showed pooled relative risks of 0.99 (95% CI: 0.95 to 1.03, p = 0.73) and 1.01 (95% CI: 0.91 to 1.11, p = 0.90), respectively. LZV co-administration was investigated in four RCTs and was safe; however, co-administration with PPSV23 resulted in reduced immunogenicity. Conclusions: The co-administration of RZV with other vaccines was safe and immunogenic. However, limited evidence suggests that co-administration of LZV with PPSV23 reduced the immunogenicity of LZV through an unknown mechanism. Still, RZV co-administration could enhance vaccine uptake in vulnerable populations. Full article

This study aimed to improve the mechanical properties and water resistance of calcined gypsum from phosphogypsum (CGP) by incorporating organic additives and inorganic admixtures. The effects of the dosage of these additives—including kaolin, nano-SiO₂, polycarboxylic acid superplasticizer, and sodium methyl silicate—on the properties (flexural strength, compressive strength, water absorption, and softening coefficient) of CGP composites (CGPCs) were investigated. A high water resistance of the CGPCs was achieved using nano-SiO₂ and sodium methyl silicate modification, superplasticizer addition, and the partial replacement of gypsum with mineral admixtures. The results showed that the flexural and compressive strength of the composites hit 4.61 MPa and 19.54 MPa, respectively, while the softening coefficient was 0.70 and the water absorption rate was 19.85%. Microstructural investigation confirmed that the combination of nano-SiO₂ and kaolin led to the formation of calcium silicate hydrate. Additionally, the superplasticizer played a crucial role in reducing the water-to-cement ratio, while unhydrated mineral particles had a filling effect, thereby enhancing the density of the hardened paste. The sodium methyl silicate formed a hydrophobic film on the surface of the hardened paste, increasing the contact angle to 109.01° and improving the water resistance of the CGPCs. Full article

With the continued advancement of shale gas development, the issue of frac hit has become increasingly prominent and has emerged as a key factor influencing the production of shale gas wells. Quantitative evaluation of the impact of frac hit on shale gas wells and proposing different methods to prevent frac hit are of great significance for the efficient development of shale gas. This research puts forward a machine learning-based workflow that incorporates geological and engineering factors to evaluate the impacts of frac hit. The “Frac Hit Pressure Integral Index (FPI)” quantifies the dynamic pressure responses by means of the ratios of initial pressure to shut-in pressure. Pearson analysis is employed to reduce the dimensionality of parameters, and Random Forest and K-means++ algorithms are utilized to classify the risks of frac hit. Among numerous influencing factors, it has been found that the brittleness index and well spacing possess the highest weights among the geological and engineering influencing factors, reaching 20.4 and 16.1, respectively. The L well area of southern Sichuan shale gas lies in the Fuji syncline of the Huaying Mountain tectonic system’s low-fold Fujian zone. When applied to the L well area in the Sichuan Basin, the results pinpoint the brittleness index, fluid intensity, and well spacing as crucial factors. It is recommended that, for reservoirs with high fracturability, reducing fluid intensity and increasing well spacing can minimize inter-well interference. This workflow classifies risks into low (FPI ≤ 265.43), medium (265.43 < FPI < 658.56), and high levels (FPI ≥ 658.56) and recalibrates natural fracture zones based on pressure and flowback data, thereby enhancing the alignment between geological and engineering aspects by 10%. This framework optimizes fracturing designs and mitigates inter-well interference, providing support for the efficient development of shale gas. Full article

The Warburg effect, a unique glycolytic phenomenon in cancer cells, presents a promising target for developing selective anticancer agents. Previously, BH10, a hit compound disrupting glycolytic metabolism, was identified via phenotypic screening, with Kelch-like ECH-associated protein 1 (Keap1) proposed as a potential target. To enhance its potency and selectivity, a library of BH10-derived salt compounds was synthesized. Among these, 7b exhibited nanomolar anticancer activity (IC₅₀ = 22.97 nM) and a high selectivity ratio (IC₅₀ of non-cancerous cells/IC₅₀ of cancer cells = 41.43). Molecular docking revealed that all naphthoimidazole salt analogues (7a–f) bind to Keap1 via carbonyl-mediated interactions, with variations in hydrogen-bonding residues (e.g., VAL606, ILE559). Full article

As the e-commerce industry rapidly expands, the number of users and items continues to grow, making it increasingly difficult to capture users’ purchasing patterns. Sequential recommendation models have emerged to address this issue by predicting the next item that a user is likely to purchase based on their historical behavior. However, most previous studies have focused primarily on modeling item sequences using item IDs without leveraging rich item-level information. To address this limitation, we propose a sequential recommendation model called ITS-Rec that incorporates various types of textual item information, including item titles, descriptions, and online reviews. By integrating these components into item representations, the model captures both detailed item characteristics and signals related to purchasing motivation. ITS-Rec is built on a self-attention-based architecture that enables the model to effectively learn both the long- and short-term user preferences. Experiments were conducted using real-world Amazon.com data, and the proposed model was compared to several state-of-the-art sequential recommendation models. The results demonstrate that ITS-Rec significantly outperforms the baseline models in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Further analysis showed that online reviews contributed the most to performance gains among textual components. This study highlights the value of incorporating textual features into sequential recommendations and provides practical insights into enhancing recommendation performance through richer item representations. Full article

This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel fiber (SF) and synthetic polypropylene fiber (Syn-PF)), different fiber lengths (19 mm and 38 mm), and various Syn-PF contents (0%, 0.2%, and 1%). Prisms with dimensions of 100 × 100 × 400 mm from each mixture underwent a four-point monotonic flexure load while collecting the emitted acoustic waves via attached AE sensors. AE parameter-based analyses, including b-value, improved b-value (Ib-value), intensity, and rise time/average signal amplitude (RA) analyses, were performed using the raw AE data to highlight the change in the AE activity associated with different stages of damage (micro- and macro-cracking). The results showed that the number of hits, average frequency, cumulative signal strength (CSS), and energy were higher for the waves released at −20 °C compared to those obtained at 25 °C. The onset of the first visible micro- and macro-cracks was noticed to be associated with a significant spike in CSS, historic index (H (t)), severity (S_r) curves, a noticeable dip in the b-value curve, and a compression in bellows/fluctuations of the Ib-value curve for both testing temperatures. In addition, time and load thresholds of micro- and macro-cracks increased when samples were cooled down and tested at −20 °C, especially in the mixtures with higher w/b, longer fibers, and lower fiber content. This improvement in mechanical performance and cracking threshold limits was associated with higher AE activity in terms of an overall increase in CSS, S_r, and H (t) values and an overall reduction in b-values. In addition, varying the concrete mixture design parameters, including the w/b ratio as well as fiber type, content, and length, showed a significant impact on the flexural behavior and the AE activity of the tested mixtures at both temperatures (25 °C and −20 °C). Intensity and RA analysis parameters allowed the development of two charts to characterize the detected AE events, whether associated with micro- and macro-cracks considering the temperature effect. Full article

In this paper, we address the challenge of dynamic evolution of user preferences and propose an attribute-sequence-based recommendation model to improve the accuracy and interpretability of recommendation systems. Traditional approaches usually rely on item sequences to model user behavior, but ignore the potential value of attributes shared among different items for preference characterization. To this end, this paper innovatively replaces items in user interaction sequences with attributes, constructs attribute sequences to capture fine-grained preference changes, and reinforces the prioritization of current interests by maintaining the latest state of attributes. Meanwhile, the item–attribute relationship is modeled using LightGCN and a variant of GAT, fusing multi-level features using gated attention mechanism, and introducing rotary encoding to enhance the flexibility of sequence modeling. Experiments on four real datasets (Beauty, Video Games, Men, and Fashion) showed that the model in this paper significantly outperformed the benchmark model in both NDCG@10 and Hit Ratio@10 metrics, with a highest improvement of 6.435% and 3.613%, respectively. The ablation experiments further validated the key role of attribute aggregation and sequence modeling in capturing user preference dynamics. This work provides a new concept for recommender systems that balances fine-grained preference evolution with efficient sequence modeling. Full article

Climate change and rapid urbanization have increased the risk of urban flooding, making timely and accurate flood prediction crucial for disaster response. However, conventional physics-based models are limited in real-time applications due to their high computational costs. Recent advances in deep learning have enabled the development of efficient surrogate models that capture complex nonlinear relationships in hydrological processes. This study presents a deep learning-based surrogate model designed to efficiently reproduce the spatiotemporal evolution of urban pluvial flooding using data from physics-based models. For the Oncheon-cheon catchment in Busan, the spatiotemporal evolution of inundation at a 10 m spatial resolution was simulated using the physics-based model for various synthetic inundation scenarios to train the deep learning model based on a Convolutional Neural Network (CNN). The training dataset was constructed using synthetic rainfall scenarios based on probabilistic rainfall data, while the model was validated using both a synthetic flood event and a historical flood event from July 2020 with observed ground-based rainfall measurements. The model’s performance was evaluated using quantitative metrics, including the Hit Rate (HR), False Alarm Ratio (FAR), and Critical Success Index (CSI), by comparing results against both synthetic and real (historical) flood events. Validation results demonstrated high reproducibility, with a CSI of 0.79 and 0.73 for the synthetic and real experiments, respectively. In terms of computational efficiency, the deep learning model achieved a speedup 16.4 times the parallel version and 82.2 times the sequential version of the physics-based model, demonstrating its applicability for near real-time flood prediction. The findings of this study contribute to the advancement of urban flood prediction and early warning systems by offering a cost-effective, computationally efficient alternative to conventional physics-based flood modeling, enabling faster and more adaptive flood risk management. Full article

Phosphodiesterase-5 (PDE5) is a potent therapeutic target for the treatment of male erectile dysfunction and pulmonary arterial hypertension with several drugs available on the market. However, most of the reported PDE5 inhibitors lack specificity over PDE6, a holoenzyme in eleven PDE families, which may cause various adverse effects. Targeting a unique allosteric pocket has proved to be an effective approach to designing selective PDE5 inhibitors. In the present study, an integrated virtual screening procedure consisting of pharmacophore modeling screening, molecular docking, molecular dynamics simulations, and binding free energy calculations was applied to the discovery of novel PDE5 inhibitors targeting the allosteric pocket. Seven out of thirty-three molecules purchased from the SPECS database (a hitting accuracy of 21%) with novel scaffolds were PDE5 inhibitors with enzymatic inhibition ratios of more than 50% at a concentration of 10 μM. Predicted binding patterns indicate these hits fit well in the allosteric pocket in PDE5. In particular, compound AI-898/12177002 (IC₅₀ = 1.6 μM) demonstrates over 10-fold selectivity towards PDE6, providing a novel scaffold for the optimization of potent and selective PDE5 inhibitors with less adverse effects. Full article

A chromosome segment substituted line (CSSL) represents an ideal resource for studying quantitative traits like thermotolerance. To develop wheat inter-varietal CSSLs with E6015-3S (a heat-sensitive genotype) being the recipient parent, genome-wide unique DNA markers are urgently needed for marker-assisted selection. In this study, 11,016 primer pairs targeting 5036 indel sites were successfully designed for E6015-3S, with an average density of 0.36 indels per Mbp. These primer pairs are believed to be unique and polymorphic in the wheat genome; as gathered from the evidence, (i) 76.18 to 99.34% of the 11,016 primer pairs yielded a single hit during sequence alignment with 18 sequenced genomes, (ii) 83.59 to 90.98% of 1042 synthesized primer pairs amplified a single band in 16 wheat accessions, and (iii) 59.69 to 99.81% of the tested 1042 primer pairs were polymorphic between E6015-3S and 15 individual wheat accessions. These primer pairs are also anticipated with excellent resolvability on agarose or polyacrylamide gels, since most of them have indel sizes from 15 to 46 bp, amplicon sizes from 141 to 250 bp, and polymorphism ratios from 6.0 to 25.0%. Collectively, these primer pairs are ideal DNA markers for inter-varietal CSSL development and more broad applications, like germplasm classification, seed purity testing, genetic linkage mapping, and marker-assisted breeding in wheat, owing to their uniqueness, polymorphism, and easy-to-use characteristics. Full article

Information-centric networking (ICN) changes the way data are accessed by focusing on the content rather than the location of devices. In this model, each piece of data has a unique name, making it accessible directly by name. This approach suits the Internet of Things (IoT), where data generation and real-time processing are fundamental. Traditional host-based communication methods are less efficient for the IoT, making ICN a better fit. A key advantage of ICN is in-network caching, which temporarily stores data across various points in the network. This caching improves data access speed, minimizes retrieval time, and reduces overall network traffic by making frequently accessed data readily available. However, IoT systems involve constantly updating data, which requires managing data freshness while also ensuring their validity and processing accuracy. The interactions with cached data, such as updates, validations, and replacements, are crucial in optimizing system performance. This research introduces an ICN-IoT method to manage and process data freshness in ICN for the IoT. It optimizes network traffic by sharing only the most current and valid data, reducing unnecessary transfers. Routers in this model calculate data freshness, assess its validity, and perform cache updates based on these metrics. Simulation results across four models show that this method enhances cache hit ratios, reduces traffic load, and improves retrieval delays, outperforming similar methods. The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. This precision highlights its effectiveness in maintaining data currentness and validity while reducing network overhead. Full article

The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases. Full article

Recent research in algorithmic trading has primarily focused on ultra-high-frequency strategies and index estimation. In response to the need for a low-frequency, real-world trading model, we developed an enhanced algorithm that builds on existing models with high hit ratios and low maximum drawdowns. We utilized established price indicators, including the stochastic oscillator and Williams %R, while introducing a volume factor to improve the model’s robustness and performance. The refined algorithm achieved superior returns while maintaining its high hit ratio and low maximum drawdown. Specifically, we leveraged 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other variables. The dataset comprised SPY ETF price and volume data spanning from 2010 to 2023, over 13 years. Our enhanced algorithmic model outperformed both the benchmark and previous iterations, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of 519.3%, and an annualized return (AnnR) of 15.1%. This analysis demonstrates that the model’s simplicity, ease of use, and interpretability provide valuable tools for investors, although it is important to note that past performance does not guarantee future returns. Full article