Search

The Shale pore structure of the Wufeng-Longmaxi formation in the Jiaoshiba area of southeastern Sichuan is complex, and the clarification of controlling factors is of guiding significance for the effective development of shale gas. Shale pore types were analyzed by organic carbon content, thermal evolution degree, whole-rock "X" ray diffraction, argon ion polishing electron microscope observation, and low-temperature nitrogen adsorption experiment. Based on the fractal geometry theory of low-temperature nitrogen adsorption, the fractal characteristics of shale pores were studied, and the effects of mineral composition, pore structure, organic matter content, and maturity on the fractal dimension were discussed. Shale pores are mainly organic, dissolution, and intergranular pores, the internal characteristics are mainly ink bottle-shaped and slit type. Shale pores have dual fractal characteristics, and the fractal dimension is between 2.5389 and 2.9126. D1 and D2 represent pore surface fractal dimension and structure fractal dimension with relative pressure less than 0.45 and greater than 0.45, D1 is between 2.5389 and 2.7199, and D2 is between 2.7343 and 2.9126. Quartz mineral, organic matter content, and maturity positively correlate with D1 and D2. Quartz and organic matter are the carriers of pore development. The maturity of organic matter controls the development of micropores and mesopores of organic matter. Micropores have a greater influence on pore structure parameters. Carbonate minerals and feldspar mainly provide macropores and increase the pore size, which is negatively correlated with D1 and D2. The clay mineral content is also negatively correlated with D1 and D2, directly related to the compaction leading to the simplification of pore morphology and the decrease of micropores. [ABSTRACT FROM AUTHOR]

The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity. IAO emulates the hunting behaviors of Aquila, elucidating each step of the hunting process. The IAO algorithm contains innovative elements to boost its optimization capabilities. It combines a combination of low flight with a leisurely descent for exploitation, high-altitude vertical dives, contour flying with brief gliding attacks for exploration, and controlled swooping maneuvers for effective prey capture. To assess the effectiveness of IAO, Herein, numerous experiments were carried out. Firstly, IAO was compared using 23 classical optimization functions. The achieved results demonstrate that the proposed model outperforms various champion algorithms. Secondly, the proposed algorithm is applied to five real-world engineering problems. The achieved results prove effectiveness in diverse application domains. The key findings of the research work highlight IAO's resilience and adaptability in solving challenging optimization issues and its importance as a strong optimization tool for real-world engineering applications. Convergence curves compare the speed of proposed algorithms with selected algorithms for 1000 iterations. Time complexity analysis shows that the best time is 0.00015225 which is better as compared to other algorithms also Wilcoxon ranksum test is carried out to calculate the p-value is less than 0.05 rejecting the null hypothesis. The research emphasizes the potential of IAO as a tool for tackling real-world optimization challenges by explaining its efficacy and competitiveness compared to other optimization procedures via comprehensive testing and analysis. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is one of the most common malignant tumors and is associated with poor prognosis. To improve the prognosis of GC patients, an effective immune-related prognostic biomarker is urgent. Here, we aim to explore the correlation between the expression of procalcitonin gamma subfamily A, 10 (PCDHGA10) and clinicopathological characteristics, especially its relation with tumor-infiltrating immune cells (TILs) in GC. Methods: The differential mRNA expression of PCDHGA10 between GC tissues and normal gastric mucosa and prognostic potential were assessed from The Cancer Genome Atlas (TCGA). Then, based on tissue microarrays (TMAs) with multiplex immunohistochemistry (mIHC) from GC patients, we statistically assess the correlation between PCDHGA10 protein expression and the clinical profiles and prognosis of the patients. Additionally, with IHC and mIHC, we applied the machine-learning algorithms to evaluate the localization and expression levels of TILs and immune checkpoints in the tumor microenvironment. We analyzed the relationship between PCDHGA10 protein expression and TILs and immune checkpoints. Results: Through the database and TMA analysis, the expression of PCDHGA10 was significantly higher in GC tissues compared with normal tissues. High PCDHGA10 expression independently predicted poor prognosis in GC. Additionally, elevated PCDHGA10 expression was positively associated with the number of CD8+ T cells, CD68+ macrophages, Foxp3+ T cells, and CD4+ T cells in GC tissues and the stromal region. Besides, the expression of PCDHGA10 was positively correlated with immune checkpoints, including CTLA-4, LAG3, and PD-L1. Conclusions: PCDHGA10 might be a potential prognostic marker and an immunological therapeutic target for GC. [ABSTRACT FROM AUTHOR]

This paper proposes a novel polar coding scheme tailored for indoor visible light communication (VLC) systems. Simulation results demonstrate a significant reduction in bit error rate (BER) compared to uncoded transmission, with a coding gain of at least 5 dB. Furthermore, the reliable communication area of the VLC system is substantially extended. Building on this foundation, this study explores the joint design of polar codes and physical-layer network coding (PNC) for VLC systems. Simulation results illustrate that the BER of our scheme closely approaches that of the conventional VLC relay scheme. Moreover, our approach doubles the throughput, cuts equipment expenses in half, and boosts effective bit rates per unit time-slot twofold. This proposed design noticeably advances the performance of VLC systems and is particularly well-suited for scenarios with low-latency demands. [ABSTRACT FROM AUTHOR]

In recent years, graph convolutional networks (GCNs) have seen widespread utilization within micro-video recommendation systems, facilitating the understanding of user preferences through interactions with micro-videos. Despite the commendable performance exhibited by GCN-based methodologies, several persistent issues demand further scrutiny. Primarily, most user-micro-video interactions involve implicit behaviors, such as clicks or abstentions, which may inadvertently capture irrelevant micro-video content, thereby introducing significant noise (false touches, low watch-ratio, low ratings) into users' histories. Consequently, this noise undermines the efficacy of micro-video recommendations. Moreover, the abundance of micro-videos has resulted in fewer interactions between users and micro-video content. To tackle these challenges, we propose a noise-resistant and anti-sparse graph learning framework for micro-video recommendation. Initially, we construct a denoiser that leverages implicit multi-attribute information (e.g., watch-ratio, timestamp, ratings, and so on) to filter noisy data from user interaction histories. This process yields high-fidelity micro-video information, enabling a more precise modeling of users' feature preferences. Subsequently, we employ a multi-view reconstruction approach and utilize cross-view self-supervised learning to gain insights into user and micro-video features. This strategic approach effectively mitigates the issue of data sparsity. Extensive experiments conducted on two publicly available micro-video recommendation datasets validate the effectiveness of our proposed method. For in-depth details and access to the code, please refer to our repository at "https://github.com/kbk12/ANAGL.git." [ABSTRACT FROM AUTHOR]

Objective: Colorectal cancer (CRC) is a form of malignancy that exhibits a comparatively elevated occurrence and fatality rate. Given the relatively slower progress in diagnostic and therapeutic approaches for CRC, there is a need to investigate more accurate and efficient biomarkers. Methods: Core regulatory genes were screened using the TCGA database, and the expression of neurexophilin 4 (NXPH4) and its prognostic implications were validated using tissue microarray staining. The assessment of NXPH4 functions involved a range of experiments, including cellular, organoid, and murine models. Furthermore, a regulatory network between m5C, NXPH4, and HIF1A was established through several in vitro experiments. Results: The overexpression of NXPH4 is associated with unfavorable prognoses in patients with CRC and hepatocellular carcinoma. Additionally, it facilitates the progression of malignant tumors both in laboratory settings and in living organisms of colorectal carcinoma. Our research also reveals that NXPH4 mRNA can avoid degradation through RNautophagy, relying on an m5C-dependent mechanism. Moreover, NXPH4 amplifies the HIF signaling pathway and stabilizes HIF1A by competitively binding to PHD4. Conclusions: NXPH4, regulated by m5C, promotes malignant tumor progression and regulates the HIF pathway. Consequently, targeting NXPH4 through molecular therapies could potentially serve as an efficacious therapeutic strategy for the management of CRC exhibiting elevated NXPH4 expression. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Semantic segmentation of point cloud data, an efficient 3-D scattered point representation, is a fundamental task for various applications, such as autonomous driving and 3-D telepresence. In recent years, deep learning techniques have achieved significant progress in semantic segmentation, especially in the 2-D image setting. However, due to the irregularity of point clouds, most of them cannot be applied to this special data representation directly. While recent works are able to handle the irregularity problem and maintain the permutation invariance, most of them fail to capture the valuable high-dimensional local feature in fine granularity. Inspired by fuzzy mathematical methods and the analysis on the drawbacks of current state-of-the-art works, in this article, we propose a novel deep neural model, Fuzzy3DSeg, that is able to directly feed in the point clouds while maintaining invariant to the permutation of the data feeding order. We deeply integrate the learning of the fuzzy neighborhood feature of each point into our network architecture, so as to perform operations on high-dimensional features. We demonstrate the effectiveness of this network architecture level integration, compared with methods of the fuzzy data preprocessing cascading neural network. Comprehensive experiments on two challenging datasets demonstrate that the proposed Fuzzy3DSeg significantly outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Semi-supervised node classification is a crucial challenge in relational data mining and has attracted increasing interest in research on graph neural networks (GNNs). However, previous approaches merely utilize labeled nodes to supervise the overall optimization, but fail to sufficiently explore the information of their underlying label distribution. Even worse, they often overlook the robustness of models, which may cause instability of network outputs to random perturbations. To address the aforementioned shortcomings, we develop a novel framework termed Hybrid Curriculum Pseudo-Labeling (HCPL) for efficient semi-supervised node classification. Technically, HCPL iteratively annotates unlabeled nodes by training a GNN model on the labeled samples and any previously pseudo-labeled samples, and repeatedly conducts this process. To improve the model robustness, we introduce a hybrid pseudo-labeling strategy that incorporates both prediction confidence and uncertainty under random perturbations, therefore mitigating the influence of erroneous pseudo-labels. Finally, we leverage the idea of curriculum learning to start from annotating easy samples, and gradually explore hard samples as the iteration grows. Extensive experiments on a number of benchmarks demonstrate that our HCPL beats various state-of-the-art baselines in diverse settings. [ABSTRACT FROM AUTHOR]

The article introduces MetaMMF, a dynamic multimodal fusion framework for micro-video recommendation. It is reported that MetaMMF dynamically assigns parameters to fusion functions based on meta-learning, treating multimodal fusion for each micro-video as an independent task.It is further noted that extensive experiments show significant improvements over state-of-the-art models.

Given the high compatibility with Li metal anodes, ethereal electrolytes have found widespread use in Li metal batteries. Unfortunately, their applications in high‐voltage Li metal batteries are hampered by a limited electrochemical window. In this study, a diluted ethereal electrolyte (with Li salt concentration < 1.5 m) is developed containing 1 m lithium bis(fluorosulfonyl) imide (FSI) and 0.3 m LiNO3 in a N‐methyl‐N‐propylpiperidinium bis(trifluoromethanesulfonyl)imide (PP13TFSI):dimethyl ether (DME) (v:v = 1:4) mixture for Li metal batteries with an aggressive high‐voltage cathode via the formation of an anion‐enriched solvation sheath. In contrast to high‐concentration electrolytes, the formation of the anion‐enriched solvation sheath in this design is facilitated by the addition of ionic liquids. Further theoretical calculations indicate that the presence of FSI− and NO3− anions in the first solvation sheath weakens the desolvation energy of the DME solvent, suggesting a faster desolvation process at the electrode interphase. Consequently, the designed electrolyte enables long‐term cycling of Li || LiNi0.8Co0.1Mn0.1O2 (NCM811) full cells over 1000 cycles at a high rate of 10 C. More notably, it also allows for a long cycle life of 100 cycles under a high rate of 5 C, even with limited negative capacity to positive capacity (N/P) ratio of 1. [ABSTRACT FROM AUTHOR]

Biochar has the potential to provide a multitude of benefits when used in soil remediation and increasing soil organic matter enrichment. Nevertheless, the intricated, hydrophobic pores and groups weaken its water-holding capacity in dry, sandy soils in arid lands. In order to combat this issue, starch-carbon-based material (SB), sodium alginate-carbon-based material (SAB), and chitosan-carbon-based material (CB) have been successfully synthesized through the graft-polymerization of biochar (BC). A series of soil column simulations were used to scrutinize the microstructure of the carbon-based material and explore its water absorption properties and its effects on sandy soil water infiltration, water retention, and aggregation. The results indicated that SB, SAB, and CB achieved water maximum absorption rates of 155, 188, and 172 g g−1, respectively. Considering their impact on sandy soils, SB, SAB, and CB lengthened infiltration times by 1920, 3330, and 3880 min, respectively, whilst enhancing the water retention capabilities of the soil by 18%, 25%, and 23% in comparison to solely adding BC. The utilization of these innovative materials notably encouraged the formation of sandy soil aggregates ranging from 2.0 to 0.25 mm, endowing the aggregates with enhanced structural stability. Findings from potting experiments suggested that all three carbon-based materials were conducive to the growth of soybean seeds. Thus, it is evident that the carbon-based materials have been fabricated with success, and they have great potential not only to significantly augment the water retention capacities and structural robustness of sandy soils in arid areas, but also to bolster the development of soil aggregates and crop growth. These materials possess significant application potential for enhancing the quality of sandy soils in arid and semi-arid regions. Highlights: Novel carbon-based materials were prepared using biochar and biodegradable organic matter. The novel materials exhibited excellent water absorption capacity. The novel materials greatly decreased the water infiltration capacity of sandy soil. The novel materials increased the water retention capacity and aggregate size of sandy soils. [ABSTRACT FROM AUTHOR]

In recommender systems, top-N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top-N recommendation, there are increasing concerns on appropriate evaluation of recommendation algorithms. It therefore is important to study how recommendation algorithms can be reliably evaluated and thoroughly verified. This work presents a large-scale, systematic study on six important factors from three aspects for evaluating recommender systems. We carefully select 12 top-N recommendation algorithms and eight recommendation datasets. Our experiments are carefully designed and extensively conducted with these algorithms and datasets. In particular, all the experiments in our work are implemented based on an open sourced recommendation library, Recbole [139], which ensures the reproducibility and reliability of our results. Based on the large-scale experiments and detailed analysis, we derive several key findings on the experimental settings for evaluating recommender systems. Our findings show that some settings can lead to substantial or significant differences in performance ranking of the compared algorithms. In response to recent evaluation concerns, we also provide several suggested settings that are specially important for performance comparison. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM AUTHOR]

Coupling learning is designed to estimate, discover, and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand—it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this article, we develop a distributed collaborative coupling learning system, which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favorable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs. [ABSTRACT FROM AUTHOR]

Existing research exploits the semantic information from reviews to complement user-item interactions for item recommendation. However, as these approaches either defer the user-item interactions until the prediction layer or simply concatenate all the reviews of a user/item into a single review, they fail to capture the complex correlations between each user-item pair or introduce noises. Thus, we propose a novel Hierarchical and Interactive Gate Network (HIGnet) model for rating prediction. Modeling local word informativeness and global review semantics in a hierarchical manner enable us to exploit textual features of users/items and capture complex semantic user-item correlations at different levels of granularities. Experiments on five challenging real-world datasets demonstrate the state-of-the-art performance of the proposed HIGnet model. To facilitate community research, the implementation of the proposed model is made publicly available (https://github.com/uqjwen/higan). [ABSTRACT FROM AUTHOR]

This study compares the seasonal and interannual-to-decadal variability in the strength and position of the Kuroshio Extension front (KEF) using high-resolution satellite-derived sea surface temperature (SST) and sea surface height (SSH) data. Results show that the KEF strength has an obvious seasonal variation that is similar at different longitudes, with a stronger (weaker) KEF during the cold (warm) season. However, the seasonal variation in the KEF position is relatively weak and varies with longitude. In contrast, the low-frequency variation of the KEF position is more distinct than that of the KEF strength even though they are well correlated. On both seasonal and interannual-to-decadal time scales, the western part of the KEF (142°–144°E) has the greatest variability in strength, while the eastern part of the KEF (149°–155°E) has the greatest variability in position. In addition, the relationships between wind-forced Rossby waves and the low-frequency variability in the KEF strength and position are also discussed by using the statistical analysis methods and a wind-driven hindcast model. A positive (negative) North Pacific Oscillation (NPO)-like atmospheric forcing generates positive (negative) SSH anomalies over the central North Pacific. These oceanic signals then propagate westward as Rossby waves, reaching the KE region about three years later, favoring a strengthened (weakened) and northward (southward)-moving KEF. [ABSTRACT FROM AUTHOR]

Unlike traditional video recommendations, micro-video inherits the characteristics of social platforms, such as social relation. A large amount of micro-videos showing explosive growth is badly affecting the user's choice. In this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users' best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users' interests, we interactively learn the user–item representations and perform the hidden item category clustering. The resulted categorical representations are interacted with user representations to model user preferences at different levels of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Social media has evolved into one of the most important channels to share micro-videos nowadays. The sheer volume of micro-videos available in social networks often undermines users’ capability to choose the micro-videos that best fit their interests. Recommendation appear as a natural solution to this problem. However, existing video recommendation methods only consider the users’ historical preferences on videos, without exploring any video contents. In this paper, we develop a novel latent genre aware micro-video recommendation model to solve the problem. First, we extract user-item interaction features, and auxiliary features describing both contextual and visual contents of micro-videos. Second, these features are fed into the neural recommendation model that simultaneously learns the latent genres of micro-videos and the optimal recommendation scores. Experiments on real-world dataset demonstrate the effectiveness and the efficiency of our proposed method compared with several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Chemical lithiation of amorphous FePO with LiI in acetonitrile is performed to form amorphous LiFePO. The amorphous FePO·2HO precursor is synthesized by co-precipitation method from equimolar aqueous solutions of FeSO·7HO and NHHPO, using HO (hydrogen peroxide) as the oxidizing agent. The nanocrystalline LiFePO/C is obtained by annealing the amorphous LiFePO and in situ carbon coating with sucrose in a reducing atmosphere. The particle size of FePO·2HO precursor decreases with increasing reaction temperature. The final LiFePO/C products completely maintain the shape and size of the precursor even after annealing at 700 °C for 2 h. The excellent electrochemical properties of these nanocrystalline LiFePO/C composites suggest that to decrease the particle size of LiFePO is very effective in enhancing the rate capability and cycle performance. The specific discharge capacities of LiFePO/C obtained from the FePO·2HO precursor synthesized at 75 °C are 151.8 and 133.5 mAh g at 0.1 and 1 C rates, with a low capacity fading of about 0.075 % per cycle over 50 cycles at 0.5 C rate. [ABSTRACT FROM AUTHOR]