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This study proposes a second digital divide measurement for university students in Guangdong, encompassing internet usage proficiency and internet-enabled learning levels. Drawing on information literacy theory, the dimensions of information awareness, acquisition, storage, retrieval, utilization, and ethical/security considerations are individually assessed. A total of 1038 valid responses were collected through a questionnaire survey, and data analysis was conducted using descriptive statistics and variance analysis. The internet usage and enabled learning levels of university students in Guangdong are found to be moderately above average. The second-level digital divide among university students in Guangdong is complex, resulting from the interactive effects of personal factors, growth environment, and university environment. Personal characteristics (gender, grade), growth environment (average living expenses, parental education, family location, growth location, family learning environment), and campus learning environment have a significant impact on both the level of internet usage and internet-enabled learning. Conversely, personal characteristics (disciplinary category), and school location exhibit no significant impact on internet-enabled learning levels.

In this paper, two experimental procedures are reported, namely electro-deposition in the ultrathin liquid layer and chemical micro-etching. Firstly, a large area quasi-two-dimensional periodic membrane with adjustable density is deposited on a Si substrate driven by half-sinusoidal voltage, which is composed of raised ridges and a membrane between the ridges. The smaller the voltage frequency is, the larger the ridge distance is. The height of a raised ridge changes synchronously with the amplitude. The grain density distribution of membrane and raised ridge is uneven; the two structures change alternately, which is closely related to the change of growth voltage and copper ion concentration during deposition. The structural characteristics of membrane provide favorable conditions for micro-etching; stable etching speed and microscope real-time monitoring are the keys to achieve accurate etching. In the chemical micro-etching process, the membrane between ridges is removed, retaining the raised ridges, thus a large scale ordered micro-nano wires array with lateral growth was obtained. This method is simple and controllable, can be applied to a variety of substrates, and is the best choice for designing and preparing new functional materials. This experiment provides a basis for the extension of this method. [ABSTRACT FROM AUTHOR]

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment. [ABSTRACT FROM AUTHOR]

The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Binary neural networks are known to be promising candidates for on-device inference due to their extreme compute and memory savings over higher-precision alternatives. However, their existing training methods require the concurrent storage of high-precision activations for all layers, generally making learning on memory-constrained devices infeasible. In this article, we demonstrate that the backward propagation operations needed for binary neural network training are strongly robust to quantization, thereby making on-the-edge learning with modern models a practical proposition. We introduce a low-cost binary neural network training strategy exhibiting sizable memory footprint reductions while inducing little to no accuracy loss vs Courbariaux & Bengio's standard approach. These decreases are primarily enabled through the retention of activations exclusively in binary format. Against the latter algorithm, our drop-in replacement sees memory requirement reductions of 3-5x, while reaching similar test accuracy (±2 pp) in comparable time, across a range of small-scale models trained to classify popular datasets. We also demonstrate from-scratch ImageNet training of binarized ResNet-18, achieving a 3.78x memory reduction. Our work is open-source, and includes the Raspberry Pi-targeted prototype we used to verify our modeled memory decreases and capture the associated energy drops. Such savings will allow for unnecessary cloud offloading to be avoided, reducing latency, increasing energy efficiency, and safeguarding end-user privacy. [ABSTRACT FROM AUTHOR]

In the face of problems such as information overload and the information cocoon resulting from big data, it is a key point of current research to solve the problem of semantic fuzziness of online reviews and improve the accuracy of personalized recommendation algorithms by using online reviews. Based on the advantage of the probabilistic language term set to deal with fuzzy information and the historical data of online hotel reviews, this paper proposes a collaborative filtering recommendation algorithm for hotels. Firstly, the text data of hotel online reviews are crawled by a crawler and processed by jieba and TF-IDF tools. Secondly, the hotel evaluation attribute set is constructed, and the sentiment analysis of the review statements is carried out with the help of the HowNet sentiment dictionary and manual annotation method. The probabilistic language term set is used to classify the data and derive statistics, and the maximum deviation method is used to determine the weight of each attribute. Then, the cosine similarity formula is fused with the modified cosine similarity formula to calculate the similarity and construct the decision matrix. Finally, combined with the historical data of the user's hotel selection, the hotel recommendation results are generated. This paper collected review data from 10 hotels in Macau from the official "Ctrip" website. The proposed recommendation algorithm model was then applied to process and analyze the data, resulting in the generation of a ranked list of hotel recommendations. To validate the accuracy and effectiveness of this research, the recommendation results were compared with those produced by other algorithms. [ABSTRACT FROM AUTHOR]

Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever‐increasing computing requirements of these structures have contributed to a desire for novel technologies and paradigms, including memristor‐based hardware accelerators. Solutions based on memristive crossbars and analog data processing promise to improve the overall energy efficiency. However, memristor nonidealities can lead to the degradation of neural network accuracy, while the attempts to mitigate these negative effects often introduce design trade‐offs, such as those between power and reliability. In this work, authors design nonideality‐aware training of memristor‐based neural networks capable of dealing with the most common device nonidealities. The feasibility of using high‐resistance devices that exhibit high I‐V nonlinearity is demonstrated—by analyzing experimental data and employing nonideality‐aware training, it is estimated that the energy efficiency of memristive vector‐matrix multipliers is improved by almost three orders of magnitude (0.715 TOPs−1W−1 to 381 TOPs−1W−1) while maintaining similar accuracy. It is shown that associating the parameters of neural networks with individual memristors allows to bias these devices toward less conductive states through regularization of the corresponding optimization problem, while modifying the validation procedure leads to more reliable estimates of performance. The authors demonstrate the universality and robustness of this approach when dealing with a wide range of nonidealities. [ABSTRACT FROM AUTHOR]

Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quantized down to binary values. Network binarization on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the $K$ K -LUT, is capable of implementing far more than an XNOR: it can perform any $K$ K -input Boolean operation. Inspired by this observation, we propose LUTNet, an end-to-end hardware-software framework for the construction of area-efficient FPGA-based neural network accelerators using the native LUTs as inference operators. We describe the realization of both unrolled and tiled LUTNet architectures, with the latter facilitating smaller, less power-hungry deployment over the former while sacrificing area and energy efficiency along with throughput. For both varieties, we demonstrate that the exploitation of LUT flexibility allows for far heavier pruning than possible in prior works, resulting in significant area savings while achieving comparable accuracy. Against the state-of-the-art binarized neural network implementation, we achieve up to twice the area efficiency for several standard network models when inferencing popular datasets. We also demonstrate that even greater energy efficiency improvements are obtainable. [ABSTRACT FROM AUTHOR]

Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, however, and so methods for hardware-oriented approximation have become a hot topic. Research has shown that custom hardware-based neural network accelerators can surpass their general-purpose processor equivalents in terms of both throughput and energy efficiency. Application-tailored accelerators, when co-designed with approximation-based network training methods, transform large, dense, and computationally expensive networks into small, sparse, and hardware-efficient alternatives, increasing the feasibility of network deployment. In this article, we provide a comprehensive evaluation of approximation methods for high-performance network inference along with in-depth discussion of their effectiveness for custom hardware implementation. We also include proposals for future research based on a thorough analysis of current trends. This article represents the first survey providing detailed comparisons of custom hardware accelerators featuring approximation for both convolutional and recurrent neural networks, through which we hope to inspire exciting new developments in the field. [ABSTRACT FROM AUTHOR]

• Separation significantly enhanced the antibiofilm activity of the fractions. • Separation significantly increased the antibacterial activity of the fractions. • Through UPLC-MS analysis, 17 phenolic compounds were identified. • Separation dramatically improved antibacterial efficiency of the fraction in tofu. • It provides an efficient way to improve activity/efficiency of natural components. Separation and enrichment of phenolic components from Citrus medica L. var. sarcodactylis were performed to improve the antibiofilm and antibacterial activity/efficiency of the fractions for the first time. Through separation of the crude extracts, the preparation of four fractions was done by chromatography on macroporous resin. It was found that the antibiofilm and antibacterial activity of the fractions were significantly enhanced. The obtained 30% ethanol eluted fraction (EEF) and 60% EEF both significantly inhibited biofilm formation. After the second separation by polyamide resin chromatography, the activity of the fractions was also improved. Complete inhibition (100%) on biofilm formation of S. aureus was achieved at 2.0 mg/mL. The MIC of the fractions on S. aureus was decreased to 2.0 mg/mL. There has been variation of 7.3–185.6 mg/g of phenolic content in the fractions, and a strong correlation between the anti-biofilm, the antibacterial activity and the phenolic content. Chemical composition analysis showed the EEF comprised 17 phenolic compounds. Moreover, the obtained EEF exhibited much higher antibacterial activity in tofu than crude extract. Therefore, chromatography separation significantly improved the antibacterial and antibiofilm activity/efficiency of the fractions both in vitro and in tofu. [ABSTRACT FROM AUTHOR]

The purpose of this study is to understand the differentiated impact of politics- and science-oriented education on pro-environmental behavior among university students. A questionnaire was designed and sent to more than 14,000 university students from 152 universities in China using the snowball sampling method. In the questionnaire, the environmental knowledge was divided innovatively into two parts: Science-oriented knowledge spread by traditional environmental education and politics-oriented knowledge spread through political education. The structural equation model was used to understand the conduction path of pro-environmental knowledge, attitude, and behavior. It shows that politics-oriented knowledge has a quicker and stronger effect on improving behavior than science-oriented knowledge. Moreover, there is a significant positive correlation between science- and politics-oriented knowledge. However, the attitude is positively influenced by science-oriented knowledge, instead of politics-oriented knowledge. It suggests that traditional environmental education and political education should be integrated to promote the pro-environmental behavior of university students indirectly and directly, which may provide an opportunity for pro-environmental political education in other countries. The study contributes important theoretical and practical implications for environmental education and sustainable development. [ABSTRACT FROM AUTHOR]

Ischemic stroke is related to a variety of physiological and pathological processes including autophagy and apoptosis. Growth arrest-specific 5 (GAS5), a long non-coding RNA (lncRNA), is known to negatively regulate cell survival and plays a key role in the pathogenesis of numerous diseases. However, the function and molecular mechanism of lncRNA GAS5 in ischemic stroke have not been reported. Real-time PCR was used to detect GAS5 and microRNA-137 (miR-137) expression in the brain tissues of mice underwent middle cerebral artery occlusion (MCAO) surgery and oxygen-glucose deprivation (OGD)-treated mouse primary brain neurons. Gain- or loss-of-function approaches were used to manipulate GAS5, miR-137, and Notch1. The mechanism of GAS5 in ischemic stroke was evaluated both in vivo and in vitro via bioinformatics analysis, MTT, flow cytometry, luciferase assay, RNA immunoprecipitation, and Western blot. GAS5 level was up-regulated and negatively correlated with miR-137 expression in MACO-injured brain and in OGR-stimulated primary brain neurons. GAS5 siRNA notably increased the cell viability, suppressed the activation of caspase-3 and cell apoptosis in neurons subjected to OGD. Furthermore, we also found that GAS5 functioned as a competing endogenous RNA (ceRNA) for miR-137 to regulate the de-repression of its endogenous target Notch1 and decrease neuron survival through inactivation of the Notch1 signaling pathway. Taken together, these findings indicate that GAS5 may promote the progression of ischemic stroke through acting as a ceRNA for miR-137 to mediate the Notch1 signaling pathway, which contributes to an extensive understanding of ischemic stroke and may provide novel therapeutic options for this disease. [ABSTRACT FROM AUTHOR]

Grain number per spike, a pivotal agronomic trait dictating wheat yield, lacks a comprehensive understanding of its underlying mechanism in Pubing3228, despite the identification of certain pertinent genes. Thus, our investigation sought to ascertain molecular markers and candidate regions associated with grain number per spike through a high-density genetic mapping approach that amalgamates site-specific amplified fragment sequencing (SLAF-seq) and bulked segregation analysis (BSA). To facilitate this, we conducted a comparative analysis of two wheat germplasms, Pubing3228 and Jing4839, known to exhibit marked discrepancies in spike shape. By leveraging this methodology, we successfully procured 2,810,474 SLAF tags, subsequently resulting in the identification of 187,489 single nucleotide polymorphisms (SNPs) between the parental strains. We subsequently employed the SNP-index association algorithm alongside the extended distribution (ED) association algorithm to detect regions associated with the trait. The former algorithm identified 24 trait-associated regions, whereas the latter yielded 70. Remarkably, the intersection of these two algorithms led to the identification of 25 trait-associated regions. Amongst these regions, we identified 399 annotated genes, including three genes harboring non-synonymous mutant SNP loci. Notably, the APETALA2 (AP2) transcription factor families, which exhibited a strong correlation with spike type, were also annotated. Given these findings, it is plausible to hypothesize that these genes play a critical role in determining spike shape. In summation, our study contributes significant insights into the genetic foundation of grain number per spike. The molecular markers and candidate regions we have identified can be readily employed for marker-assisted breeding endeavors, ultimately leading to the development of novel wheat cultivars possessing enhanced yield potential. Furthermore, conducting further functional analyses on the identified genes will undoubtedly facilitate a comprehensive elucidation of the underlying mechanisms governing spike development in wheat., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Wang, Cheng and Ma.)

Vertebral compression fractures are becoming increasingly common with aging of the population; minimally invasive materials play an essential role in treating these fractures. However, the unacceptable processing-performance relationships of materials and their poor osteoinductive performance have limited their clinical application. In this review, we describe the advances in materials used for minimally invasive treatment of vertebral compression fractures and enumerate the types of bone cement commonly used in current practice. We also discuss the limitations of the materials themselves, and summarize the approaches for improving the characteristics of bone cement. Finally, we review the types and clinical efficacy of new vertebral implants. This review may provide valuable insights into newer strategies and methods for future research; it may also improve understanding on the application of minimally invasive materials for the treatment of vertebral compression fractures., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sui, Yu, Sun, Chao, Qin, Wang, Wang and Zheng.)