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Background: Agropyron cristatum (L.) is a valuable genetic resource for expanding the genetic diversity of common wheat. Pubing3228, a novel wheat-A. cristatum hybrid germplasm, exhibits several desirable agricultural traits, including high grain number per spike (GNS). Understanding the genetic architecture of GNS in Pubing3228 is crucial for enhancing wheat yield. This study aims to analyze the specific genetic regions and alleles associated with high GNS in Pubing3228. Methods: The study employed a recombination inbred line (RIL) population derived from a cross between Pubing3228 and Jing4839 to investigate the genetic regions and alleles linked to high GNS. Quantitative Trait Loci (QTL) analysis and candidate gene investigation were utilized to explore these traits. Results: A total of 40 QTLs associated with GNS were identified across 16 chromosomes, accounting for 4.25–17.17% of the total phenotypic variation. Five QTLs (QGns.wa-1D, QGns.wa-5 A, QGns.wa-7Da.1, QGns.wa-7Da.2 and QGns.wa-7Da.3) accounter for over 10% of the phenotypic variation in at least two environments. Furthermore, 94.67% of the GNS QTL with positive effects originated from Pubing3228. Candidate gene analysis of stable QTLs identified 11 candidate genes for GNS, including a senescence-associated protein gene (TraesCS7D01G148000) linked to the most significant SNP (AX-108,748,734) on chromosome 7D, potentially involved in reallocating nutrients from senescing tissues to developing seeds. Conclusion: This study provides new insights into the genetic mechanisms underlying high GNS in Pubing3228, offering valuable resources for marker-assisted selection in wheat breeding to enhance yield. [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]

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]

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]

As a fundamental thermodynamic parameter, pressure can cause matters to produce new phases with unique physical properties. In this paper, the pressure-induced behavior of La 2 S 3 has been investigated in depth by in-situ high pressure synchrotron angle dispersive X-ray diffraction (ADXRD), and the pressure is up to 26 GPa. We find the conventional La 2 S 3 which was stable at ambient pressure, was destabilized with the application of pressure and decomposed into La 3 S 4 and LaS 2 at 13.4 GPa. This is the first time that the high pressure decomposition of La 2 S 3 has been found in the experiments. The La 2 S 3 possesses the lowest formation enthalpy at atmospheric pressure, enthalpy change increases with the pressure increasing, leading to the decomposition of La 2 S 3. The high pressure inhibits the ability of S atoms to accept electrons and promotes the polymerization of S atoms to form shared electron pairs, brings in the change of S–S bonding modes, and also further leads to the change of structural. The study of pressure relief experiment shows that the pressure induced decomposition in La 2 S 3 is an irreversible phase transition. The different radial direction has the different compression effects under pressure, the initial structure decays rapidly driven by pressure. With the pressure rising, the decay rate becomes gentle. The volume expansion of La 3 S 4 and LaS 2 may be due to some defects, and the smaller the volume, the greater the bulk modulus. This work provides a strong support for the theoretical research results and is expected to provide guidance for the experiment study of the physical properties and structural characteristics of La–S system and other rare earth sulfur compounds. The research on the high-pressure behavior in La 2 S 3 powder using in-situ synchrotron XRD was presented. La 2 S 3 decomposed into La 3 S 4 and LaS 2 at 13.4 GPa. The increase of enthalpy change leads to the decomposition of La 2 S 3 with the pressure increasing. The high pressure brings in the change of S–S bonding modes, also leading to the change of structural. The pressure induced decomposition in La 2 S 3 is an irreversible phase transition. [Display omitted] • The high-pressure behavior in La 2 S 3 using in-situ synchrotron XRD was studied. • The La 2 S 3 decomposed into La 3 S 4 and LaS 2 at 13.4 GPa. • The increase of enthalpy change leads to the decomposition of La 2 S 3. • The change of S–S bonding modes leads to the change of structural. • The pressure-induced decomposition is irreversible. [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]

The AAO template arranged in hexagonal regular arrays with the highly ordered structure and constant diameter is introduced in this paper. FeCo nanowire arrays were successfully produced using the porous orderly AAO template. The CoFe 2 O 4 nanowire arrays are indirectly obtained by annealing the FeCo alloy. The size of diameter is consistent with the AAO template pores and the FeCo and CoFe 2 O 4 nanowire arrays have a large aspect ratio and shape anisotropy. The analyzing of magnetic reveals that the FeCo and CoFe 2 O 4 nanowire arrays are easier to magnetize along the axis of the nanowire, and the perpendicular to the axis of the nanowires is the hard magnetization axis. The magnetic properties are different in different directions of the crystal orientation, showing both have the magneto-crystalline anisotropy. The FeCo and CoFe 2 O 4 nanowire arrays exhibit ferromagnetic behavior, and the coercivity of CoFe 2 O 4 nanowire arrays obviously increases twice than that of FeCo. The significantly increased coercivity proves that the CoFe 2 O 4 nanowire arrays have more obvious magneto-crystalline anisotropy. • The highly ordered AAO templates were successfully synthesized. • The FeCo nanowires have a large aspect ratio and shape anisotropy. • The FeCo alloys with Fe content in the range of 60–70 wt% have a higher magnetism. • The CoFe 2 O 4 nanowires are easier to magnetize along the axis of the nanowire. • The CoFe 2 O 4 nanowires have larger coercivity than that of FeCo. [ABSTRACT FROM AUTHOR]

The GaN:Mn nanostructure was prepared using direct current (DC) arc discharge plasma method. No catalysts or templates are involved in the experiment. The test of energy dispersive x-ray spectroscopy (EDXS) detected the signals of Ga, N, Mn, and the Mn signal strength is slightly stronger with the increasing Mn content. The X-ray diffraction peak (XRD) is sharp, and there is a clear XRD peak shift towards larger angle with the increase of doping concentration. It is indicated that GaN:Mn nanoparticles have good crystallinity, and the introduced Mn has changed the lattice parameter. Photoluminescence (PL) shows that the absorption of visible light is a relatively broad range. The Mn doped induced the energy levels in the band gap, leading to the increasing of yield of electron-hole pair, and the optical property enhanced with the increasing of Mn concentration. Meanwhile the complicated magnetic transition has been found due to the quantum tunneling of magnetization. The magnetism is from the doped Mn, and its concentration impacts the strength of magnetic coupling. [ABSTRACT FROM AUTHOR]

The high-pressure behaviors at room temperature of Ho 2 S 3 and Tm 2 S 3 powder have been researched using in-situ synchrotron XRD, and been reported for the first time. Their initial phase structures have isotypism, and both transform from the initial δ phase to the γ phase. The pressure induced phase transitions in Ho 2 S 3 and Tm 2 S 3 are initiated at 4.6 GPa and 5.2 GPa, the beginning pressure point decreases with the increasing of ionic radius. The volume collapse of Ho 2 S 3 is larger than that of Tm 2 S 3 ; a larger collapse may lead to the decrease of phase transition pressure point. Ho 2 S 3 and Tm 2 S 3 transform into cubic from monoclinic phase, the bulk modulus of both are enhanced 25.4 GPa and 20.9 GPa, a greater increase may be related to larger volume collapse. These results indicate that the lattice structure plays a crucial role in the high pressure behavior. • The high-pressure behavior of Ho2S3 and Tm2S3 are researched at room temperature. • The Ho2S3 and Tm2S3 are isotypism, and both transform into γ-phase from δ-phase. • The phase transition of Ho2S3 occurs early and progresses quickly. • The pressure point of transition decreases with the increasing of ionic radius. • Subtle differences in structures induce differences in high-pressure behavior. [ABSTRACT FROM AUTHOR]

The continuous development of e-commerce recommendation systems enables people to better obtain the products they need, saving economic and time costs. When recommending products, massive online user reviews can objectively reflect the efficacy of products and provide more practical reference value. However, due to the fuzziness of expression, there exists great uncertainty about the information in user reviews. The probabilistic linguistic term set (PLTS) is a useful tool that can help solve the information ambiguity and distinguish the importance of different terms. This paper regards PLTS as a data statistical tool to depict the information of user reviews. Based on the theory of PLTS, this paper proposes a hotel recommendation algorithm. First, we analyse the statements of online user hotel reviews with the aid of Jieba and TF-IDF. Second, the statements of reviews are translated into PLTSs and stored in the evaluation matrix. Third, we adopt the maximum deviation method to derive the attribute weights of hotels. Finally, we use probabilistic linguistic cosine similarity to calculate the similarity degrees of hotels and rank the recommended hotels according to similarity. With respect to different hospital products of users, the recommendation orders of hotels are given according to their degrees of similarity. To verify the effectiveness and superiority of the proposed algorithm, this paper selects 10 hotels in the city of Zhengzhou for case application and conducts a comparative analysis with other recommendation methods. [ABSTRACT FROM AUTHOR]