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A novel Cr-doped BaTiO3 aerogel was successfully synthesized using a co-gelation technique that involves two metallic alkoxides and a supercritical drying method. This freshly prepared aerogel has a high specific surface area of over 100 m2/g and exhibits improved responsiveness to the simulated sunlight spectrum. Methyl orange (MO) was chosen as the simulated pollutant, and the results reveal that the Cr-doped BaTiO3 aerogel, when modified with the noble metal silver (Ag), achieves a pollutant removal rate approximately 3.2 times higher than that of the commercially available P25, reaching up to 92% within 60 min. The excellent photocatalytic performance of the Ag-modified Cr-doped BaTiO3 aerogel can be primarily attributed to its extensive specific surface area and three-dimensional porous architecture. Furthermore, the incorporation of Ag nanoparticles effectively suppresses the recombination of photo-generated electrons and holes. Stability and reusability tests have confirmed the reliability of the Ag-modified Cr-doped BaTiO3 aerogel. Therefore, this material emerges as a highly promising candidate for the treatment of textile wastewater. [ABSTRACT FROM AUTHOR]

Enhancing soil stability through the incorporation of straw and biochar is well documented. Nevertheless, the combined impact of straw, biochar, and nitrogen supplementation on soil aggregates and organic carbon still needs to be explored, with limited attention given to various sieving methods in the existing literature. Therefore, the current experiment used four sieving methods—routine wet sieving (RoutW), fast-wetting sieving (FastW), slow-wetting sieving (SlowW), and wetting–stirring sieving (WetS)—to investigate the effects of adding straw (0 or 4.5 t ha−1), biochar (from maize straw, 0 or 15 t ha−1), and N (0 or 100 kg ha−1) on soil aggregate stability and soil organic C in silt–loam soil of rainfed farmland in northwest China. The field experiment was started in 2014; soil samples were collected in 2021. The results revealed that straw returned, biochar, and N addition significantly increased soil mean weight diameter (MWD) and soil organic C (SOC). Compared to CN0 (zero-amendment) plots, straw returned with nitrogen addition (SN100) significantly increased the MWD of aggregates by 130.3% (RoutW), 121.66% (FastW), 73.94% (SlowW), and 91.78% (WetS) in the 0–30 cm soil layer. The addition of biochar and nitrogen (BN100) treatment showed the most significant effects on the relative slaking index (RSI), relative mechanical breakdown index (RMI), and SOC; compared with CN0 treatment, BN100 plots can reduce RSI and RMI by 42.90% and 54.66% and increase SOC by 53.27% for all soil layers. Therefore, adding organic materials with N can enhance the stability of soil aggregates and SOC of silt–loam soils in northwest China. Integrating biochar as an organic soil amendment in the agricultural practices of northwest China presents a multifaceted solution that addresses soil health, crop productivity, and environmental sustainability. The current study provides valuable insights that support adopting this innovative approach, paving the way for future sustainable agricultural practices that can benefit both the region and the global community. [ABSTRACT FROM AUTHOR]

For the actual collected point cloud data, there are widespread challenges such as semantic inconsistency, density variations, and sparse spatial distribution. A network called PointMM is developed in this study to enhance the accuracy of point cloud semantic segmentation in complex scenes. The main contribution of PointMM involves two aspects: (1) Multi-spatial feature encoding. We leverage a novel feature encoding module to learn multi-spatial features from the neighborhood point set obtained by k-nearest neighbors (KNN) in the feature space. This enhances the network's ability to learn the spatial structures of various samples more finely and completely. (2) Multi-head attention pooling. We leverage a multi-head attention pooling module to address the limitations of symmetric function-based pooling, such as maximum and average pooling, in terms of losing detailed feature information. This is achieved by aggregating multi-spatial and attribute features of point clouds, thereby enhancing the network's ability to transmit information more comprehensively and accurately. Experiments on publicly available point cloud datasets S3DIS and ISPRS 3D Vaihingen demonstrate that PointMM effectively learns features at different levels, while improving the semantic segmentation accuracy of various objects. Compared to 12 state-of-the-art methods reported in the literature, PointMM outperforms the runner-up by 2.3% in OA on the ISPRS 3D Vaihingen dataset, and achieves the third best performance in both OA and MioU on the S3DIS dataset. Both achieve a satisfactory balance between OA, F1, and MioU. [ABSTRACT FROM AUTHOR]

Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV's flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR's end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research. [ABSTRACT FROM AUTHOR]

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds. [ABSTRACT FROM AUTHOR]

In order to achieve more effective online monitoring of distribution cables, a deployment scheme of the distribution cable condition monitoring devices based on a wireless sensor network (WSN) has been proposed. The proposed deployment scheme can improve the coverage rate and extend the lifetime of the sensor network. Moreover, the clustering method, node density, and node deployment method of the wireless sensor routing algorithm are improved, and based on isosceles triangle non-uniform deployment, a routing optimization algorithm has been proposed to achieve the balance of the energy consumption of each node in the network. Firstly, according to the energy consumption model of the network, the energy consumption of each cluster is calculated, and then by taking the minimum energy consumption of data transmission between clusters as the constraint condition, the optimal clustering distance of the network is solved. Then, according to the scale of network nodes, the density of routing nodes in each cluster is calculated, and the routing nodes in the cluster are deployed by an isosceles triangle. Finally, according to the cluster head election formula, the routing cluster head nodes in each cluster are selected, and the network data information is transmitted to the base station in a multi-hop manner through the routing cluster head nodes. The simulation results indicated that, compared with the traditional routing optimization algorithms, the proposed algorithm could keep the routing coverage at more than 200% all the time, and can effectively balance the energy consumption of nodes, improve the energy utilization efficiency of the routing nodes, and at least extend the lifetime of the network by two times. Moreover, the longer the cables, the more cost savings and the better the life cycle optimization effect of the proposed scheme. In addition, the proposed scheme can greatly reduce the economic cost of network investment, compared with using the demodulator to obtain monitoring data. [ABSTRACT FROM AUTHOR]

A solid oxide fuel cell (SOFC) is a clean, efficient energy conversion device with wide fuel applicability. Metal-supported solid oxide fuel cells (MS-SOFCs) exhibit better thermal shock resistance, better machinability, and faster startup than traditional SOFCs, making them more suitable for commercial applications, especially in mobile transportation. However, many challenges remain that hinder the development and application of MS-SOFCs. High temperature may accelerate these challenges. In this paper, the existing problems in MS-SOFCs, including high-temperature oxidation, cationic interdiffusion, thermal matching, and electrolyte defects, as well as lower temperature preparation technologies, including the infiltration method, spraying method, and sintering aids method, are summarized from different perspectives, and the improvement strategy of existing material structure optimization and technology integration is put forward. [ABSTRACT FROM AUTHOR]

Neutron thermalization leads to the complexity of the scattering cross-section calculation, which influences the accuracy of the neutron transport calculation in the thermal energy range. The higher precision of thermal scattering data is demanded in the small modular reactors (SMRs) design, especially for small-sized PWRs and SCWRs. Additionally, the thermal neutron scattering problems in supercritical water have not yet been solved. In this study, the thermal neutron scattering problems in subcritical water are tested. Based on thermal neutron scattering theory, the GA model and IKE model were analyzed. This work selected the corresponding input parameters, such as the frequency spectrum, the discrete oscillator energy, weight parameters and so on, as well as preliminary studies on how to calculate the thermal scattering data for HinH2O to accomplish the calculation at various temperatures by developing LIPER code. The deviation between the calculated and reference results, which were both obtained by the Monte Carlo code, COSRMC, was below 0.2 pcm. The deviation of the scattering cross-section between the calculation results and reference was below 0.1%, indicating the reasonability of this study's thermal scattering data calculation. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) prediction of rolling bearings based on vibration signals has attracted widespread attention. It is not satisfactory to adopt information theory (such as information entropy) to realize RUL prediction for complex vibration signals. Recent research has used more deep learning methods based on the automatic extraction of feature information to replace traditional methods (such as information theory or signal processing) to obtain higher prediction accuracy. Convolutional neural networks (CNNs) based on multi-scale information extraction have demonstrated promising effectiveness. However, the existing multi-scale methods significantly increase the number of model parameters and lack efficient learning mechanisms to distinguish the importance of different scale information. To deal with the issue, the authors of this paper developed a novel feature reuse multi-scale attention residual network (FRMARNet) for the RUL prediction of rolling bearings. Firstly, a cross-channel maximum pooling layer was designed to automatically select the more important information. Secondly, a lightweight feature reuse multi-scale attention unit was developed to extract the multi-scale degradation information in the vibration signals and recalibrate the multi-scale information. Then, end-to-end mapping between the vibration signal and the RUL was established. Finally, extensive experiments were used to demonstrate that the proposed FRMARNet model can improve prediction accuracy while reducing the number of model parameters, and it outperformed other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The energy group structure of a multi-group cross section library matched with a deterministic method has a significant influence on shielding calculation. The complex resonance cross section of Fe-56 has a significant influence on accuracy with different energy group structures when processing multi-group cross section data. In this study, in order to more accurately test the influence of the iron resonance phenomenon on shielding calculations, this group structure is modified by the 199-group with 69 points interpolated into its group boundaries at an energy range from 1.1 keV to 3.1164 MeV. The new 269-group library is then tested with selected SINBAD iron sphere benchmarks and compared with the results of 199-group, 299-group, and 172-group libraries and measurements. Upon analysis, it is shown that the resonance of Fe-56 has a great influence on the accuracy of the calculated leakage. Further, it is noted that the finer the energy group division, the greater the calculated leakage fluctuation, and the deeper the neutron passes through the iron sphere, the larger the calculated leakage fluctuation. This study and its leakage results may provide a good reference for the development of new multi-group structures for present and future shielding design. [ABSTRACT FROM AUTHOR]

Carbon capture and storage (CCS) technology is considered as one of the most effective short-term solutions in reducing atmospheric CO2 concentrations. A key of CCS technology is to seek the absorbent with low cost, fast absorption rate, and high stability. In this study, we show that 2-ethyl-4-methylimidazole is particularly suitable for efficient CO2 capture. The aqueous solution of 2-ethyl-4-methylimidazole displays a maximum CO2 molar absorption capacity of 1.0 mol∙mol−1 and the absorbed CO2 can be completely released through heating the solution at a relatively low temperature (<100 °C). Stability tests show that the aqueous system is quite stable, with less than 10% loss of the molar absorption capacity after eight absorption–desorption cycles. Time-related in-situ attenuated total reflection infrared absorption spectroscopy and 13C nuclear magnetic resonance spectroscopy studies reveal that the intermediates are HCO3− and H2CO3 in the process of CO2 absorption–desorption. These intermediates are easily decomposed, which are responsible for the low CO2 desorption temperature and high desorption efficiency of the system. Moreover, the aqueous solution of 2-ethyl-4-methylimidazole is able to separate and purify CO2 from flue gas and even ambient air. Consequently, 2-ethyl-4-methylimidazole is a promising low-cost CO2 absorbent for industrial implementation. [ABSTRACT FROM AUTHOR]

Recently, polyurea has been applied to improve the anti-blast performance of metal plates, masonry walls, and concrete structures. However, the strengthening effectiveness of polyurea on ultra-high performance concrete (UHPC) slabs with an overall response is still unclear. Hence, this paper examined the strengthening effectiveness of polyurea on the anti-blast performance of UHPC slabs under near-field explosion by the finite element (FE) method. First, a benchmark finite element model for UHPC and polyurea-UHPC (PUHPC) slabs under blast loading was established and validated by field blast tests, with scaled distances ranging from 0.4 m/kg1/3 to 0.8 m/kg1/3. After that, parametric analysis was conducted to fully understand the strengthening effectiveness of polyurea on the anti-blast performance of the UHPC slab. Factors including the scaled distance, polyurea thickness, span-to-depth ratio of the slab, and longitudinal reinforcement ratio were considered. The results showed that (1) spraying polyurea on the rear face of the UHPC slab can reduce the width of cracks and mitigate the damage of specimens; (2) the strengthening effectiveness of polyurea on the UHPC slab became prominent when the UHPC slab suffered a larger maximum deflection; (3) in terms of the deflection and energy absorption capacity of PUHPC slabs, the optimum thickness of sprayed polyurea was determined to be 8 mm to 12 mm; and (4) by adopting the multiple nonlinear regression method, a prediction formula was developed to quickly obtain the end rotation of the UHPC slab strengthened with polyurea under near-field explosions. [ABSTRACT FROM AUTHOR]

Tunnels play an essential role in the transportation network. Tunnel entrances are usually buried at a shallow depth. In the event of an internal explosion, the blast pressure will cause severe damage or even collapse of the tunnel entrance, paralyzing the traffic system. Therefore, an accurate assessment of the damage level of tunnel entrances under internal blast loading can provide effective assistance for the anti-blast design of tunnels, post-disaster emergency response, and economic damage assessment. In this paper, four tunnel entrance specimens were designed and fabricated with a scale ratio of 1/5.5, and a series of field blast tests were carried out to examine the damage pattern of the tunnel entrances under internal explosion. Subsequently, static loading tests were conducted to obtain the maximum bearing capacity of the intact specimen and residual bearing capacities of the post-blast specimens. After that, an explicit non-linear analysis was carried out and a numerical finite element (FE) model of the tunnel entrance under internal blast loading was established by adopting the arbitrary Lagrangian–Eulerian (ALE) method and validated based on the data obtained from the field blast and static loading tests. A probabilistic vulnerability analysis of a typical tunnel entrance subjected to stochastic internal explosions (assuming various charge weights and detonation points) was then carried out with the validated FE model. For the purpose of damage assessment, the residual bearing capacity of the tunnel entrance was taken as the damage criterion. The vulnerability curves corresponding to various damage levels were further developed based on the stochastic data from the probabilistic vulnerability analysis. When the charge weight was 200 kg, the tunnel entrance exhibited slight or moderate damage, while the tunnel entrance suffered severe or even complete damage as the charge weight increased to 1000 kg. However, the tunnel entrance's probability of complete damage was less than 10% when the TNT charge weight did not exceed 1000 kg. [ABSTRACT FROM AUTHOR]

As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the L S E F S T E 100 and L S E S & P 500 are higher than L S E S Z I , which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy. [ABSTRACT FROM AUTHOR]

Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) can profile genome-wide epigenetic marks associated with regulatory genomic elements. However, conventional ChIP-seq is challenging when examining limited numbers of cells. Here, we developed a new technique by supplementing carrier materials of both chemically modified mimics with epigenetic marks and dUTP-containing DNA fragments during conventional ChIP procedures (hereafter referred to as 2cChIP-seq), thus dramatically improving immunoprecipitation efficiency and reducing DNA loss of low-input ChIP-seq samples. Using this strategy, we generated high-quality epigenomic profiles of histone modifications or DNA methylation in 10–1000 cells. By introducing Tn5 transposase-assisted fragmentation, 2cChIP-seq reliably captured genomic regions with histone modification at the single-cell level in about 100 cells. Moreover, we characterized the methylome of 100 differentiated female germline stem cells (FGSCs) and observed a particular DNA methylation signature potentially involved in the differentiation of mouse germline stem cells. Hence, we provided a reliable and robust epigenomic profiling approach for small cell numbers and single cells. [ABSTRACT FROM AUTHOR]

The enhanced permeability and retention effect (EPR effect) is a crucial phenomenon for understanding the pathophysiological characteristics of blood vasculature and microenvironments in solid tumors. It is also an essential concept for designing anticancer drugs that can be selectively delivered into tumor tissue via the unique extravasation and retention mechanism for macromolecular drugs. As tumor vasculature is highly heterogeneous, the intensities of the EPR effect vary according to the types and locations of solid tumors in different species. However, the EPR effect is universally observed in a broad spectrum of solid tumors in human cancer as well as experimental animal tumor models. The matter is how to utilize the EPR effect for drug design and clinical application. Many hypotheses were proposed and tested to enhance the EPR effect in solid tumors in order to increase the efficacy of drug delivery. However, we should focus on increasing the blood flow in tumors so that more drugs can be perfused and accumulated inside tumor tissue and execute anticancer activities. Angiotensin II co-administration and the approach of intratumor arterial infusion should be considered to achieve selective tumor tissue perfusion for nanodrugs. [ABSTRACT FROM AUTHOR]

Based on the acid–base neutralization, the (1-methylimidazolium)(tetrazol-1-yl)borane was successfully synthesized by taking advantage of the acidity of the tetrazole and the basicity of the 1-methylimidazole borane complex. Through HRMS, NMR, and FT−IR, the structure of synthetic compounds was characterized in detail. Concerning about the (1-methylimidazolium)(tetrazol-1-yl)borane, it had an ignition−delay time of about 25 ms and a density specific impulse over 351 s·g/cm3, making it a suitable candidate for green hypergolic fuels. Moreover, it also demonstrated that introducing tetrazole into the borane could be an appropriate strategy to adjust the performance of the energy of those borane compounds. [ABSTRACT FROM AUTHOR]

Distribution centers are quite important for logistics. In order to save costs, reduce energy consumption and deal with increasingly uncertain demand, it is necessary for distribution centers to select the location strategically. In this paper, a two-stage model based on an improved clustering algorithm and the center-of-gravity method is proposed to deal with the multi-facility location problem arising from a real-world case. First, a distance function used in clustering is redefined to include both the spatial indicator and the socio-economic indicator. Then, an improved clustering algorithm is used to determine the optimal number of distribution centers needed and the coverage of each center. Third, the center-of-gravity method is used to determine the final location of each center. Finally, the improved method is compared with the traditional clustering method by testing data from 12 cities in Inner Mongolia Autonomous Region in China. The comparison result proves the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user's in-group membership. The local implicit trust relationship and the user's in-group membership were adjusted by the adaptive weight to determine the degree of each part's influence. Then, the author integrated the user's score of items and the user's implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations. [ABSTRACT FROM AUTHOR]

In this study, we conduct a reliability characterisation study of cold standby systems. Utilising synthetic rectangular formulas and cold preparedness equivalent models for cold standby systems, we analyse the lifetimes of several typical configurations, including series, parallel, and k/n:m voting systems. This study proposes system equivalent models for various types of cold standby systems, all composed of components that follow the same exponential distribution. We use the equivalent model to determine the optimal timing for the use of cold spares and derive the reliability function and residual lifetime function for each type of system. To demonstrate the validity of our model, the Monte Carlo simulation is strategically designed based on the system failure rate function. The experimental results are then compared with those obtained from the numerical model, highlighting that the numerical method incurs a lower time cost. [ABSTRACT FROM AUTHOR]

10.3390/math10091364 18 Wang Y., Yang L., Wu J., Song Z., Shi L. Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method. 10.3390/math10193659 6 Cao X., Luo Q., Wu P. Filter-GAN: Imbalanced Malicious Traffic Classification Based on Generative Adversarial Networks with Filter. With the emergence of big data and the resulting information explosion, computational and mathematical methods provide effective tools to handle the vast amounts of data and information used in big data analytics, knowledge discovery and distillation, and decision-making for solving complex problems in the world. The results show that the feature image representation method can effectively characterize the original session traffic, and Filter-GAN can generate more efficient samples. [Extracted from the article]

The effect of track geometry on vehicle vibration is a major concern in high-speed rail (HSR) operation from the perspectives of ride comfort and safety. However, how to quantitatively characterize the relation between them remains a problem to be solved in track quality assessment. By using fractal analysis, this paper studies the detailed correlation between track surface and alignment irregularities and car body vertical and lateral acceleration in various wavelength ranges. The time-frequency features of the track irregularity and car-body acceleration are first analyzed based on empirical mode decomposition (EMD). Then, the fractal features of the inspection data are determined by calculating the Hurst exponent of their intrinsic mode functions (IMFs). Finally, the fractal dimensions of the track irregularity and car-body acceleration are obtained, and the correlation between their fractal dimensions with respect to different IMFs is revealed using regression analysis. The results show that the fractal dimension is only related to the roughness of the IMF waveforms of the track irregularity and car-body vibration and is irrelevant to the amplitude of the time series of the data; the correlation coefficient of the fractal dimension of the track irregularity and car-body acceleration is greater than 0.7 for wavelengths greater than 30 m, indicating that the relationship between track irregularity and car-body vibration acceleration is more obvious for long wavelengths. The findings of this research could be used for optimizing HSR track maintenance work from the viewpoint of the ride quality of high-speed trains. [ABSTRACT FROM AUTHOR]

Mining-induced hydromechanical behavior of sandstone is critical to mining safety and disaster prevention. To investigate the evolution behavior of the mechanical and permeability properties of sandstone, mining-induced stress was imitated by increasing axial stress and decreasing confining stress, and a set of hydromechanical experiments were further performed, incorporating the effect of in situ stress, pore pressure, and mining stress. The results show the similar variation tendencies of the deformation and permeability of sandstone under different loading paths of in situ stress and pore pressure. Most sandstone samples maintain a compression state for the peak stress condition. The failure mode evolved from shear failure to shear–tension failure with the increase in in situ stress. The stress-relief effect significantly effects the permeability, since the permeability of sandstone increases exponentially with decreasing effective confining stress. The growth rate of permeability in Stage II is significantly greater than that in Stage I. One order of magnitude of permeability was presented at the peak stress situation. A fitting exponential model based on the alteration of effective confining stress was proposed to describe the permeability evolution dominated by the stress-relief effect, and the discovered permeability model can accurately describe the experimental results. The research results provide significant guidance for understanding the hydromechanical behavior and water hazard prevention for underground coal mines. [ABSTRACT FROM AUTHOR]

Herbicides are useful tools for managing weeds and promoting food production and sustainable agriculture. In this study, we report on the development of a novel class of lipophilic pyrimidine-biphenyl (PMB) herbicides. Firstly, three PMBs, Ia, IIa, and IIIa, were rationally designed via a scaffold hopping strategy and were determined to inhibit acetohydroxyacid synthase (AHAS). Computational simulation was carried out to investigate the molecular basis for the efficiency of PMBs against AHAS. With a rational binding mode, and the highest in vitro as well as in vivo potency, Ia was identified as a preferable hit. Furthermore, these integrated analyses guided the design of eighteen new PMBs, which were synthesized via a one-step Suzuki–Miyaura cross-coupling reaction. These new PMBs, Iba-ic, were more effective in post-emergence control of grass weeds compared with Ia. Interestingly, six of the PMBs displayed 98–100% inhibition in the control of grass weeds at 750 g ai/ha. Remarkably, Ica exhibited ≥ 80% control against grass weeds at 187.5 g ai/ha. Overall, our comprehensive and systematic investigation revealed that a structurally distinct class of lipophilic PMB herbicides, which pair excellent herbicidal activities with new interactions with AHAS, represent a noteworthy development in the pursuit of sustainable weed control solutions. [ABSTRACT FROM AUTHOR]

DNA methylation is a form of epigenetic regulation, having pivotal parts in controlling cellular expansion and expression levels within genes. Although blood DNA methylation has been studied in humans and other species, its prominence in cattle is largely unknown. This study aimed to methodically probe the genomic methylation map of Xinjiang brown (XJB) cattle suffering from bovine respiratory disease (BRD), consequently widening cattle blood methylome ranges. Genome-wide DNA methylation profiling of the XJB blood was investigated through whole-genome bisulfite sequencing (WGBS). Many differentially methylated regions (DMRs) obtained by comparing the cases and controls groups were found within the CG, CHG, and CHH (where H is A, T, or C) sequences (16,765, 7502, and 2656, respectively), encompassing 4334 differentially methylated genes (DMGs). Furthermore, GO/KEGG analyses showed that some DMGs were involved within immune response pathways. Combining WGBS-Seq data and existing RNA-Seq data, we identified 71 significantly differentially methylated (DMGs) and expressed (DEGs) genes (p < 0.05). Next, complementary analyses identified nine DMGs (LTA, STAT3, IKBKG, IRAK1, NOD2, TLR2, TNFRSF1A, and IKBKB) that might be involved in the immune response of XJB cattle infected with respiratory diseases. Although further investigations are needed to confirm their exact implication in the involved immune processes, these genes could potentially be used for a marker-assisted selection of animals resistant to BRD. This study also provides new knowledge regarding epigenetic control for the bovine respiratory immune process. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we aimed to improve the culture system for chicken embryonic-derived pluripotent stem cells (PSCs). These cells have immense potential in various fields, such as growth and development studies, vaccine production, and preserving genetic resources. However, establishing a stable and efficient culture system for chicken PSCs remains a challenge. We investigated the effects of different signaling pathways and feeder layers on the derivation and maintenance of chicken PSCs. Our results show that using STO cells as feeder layers, along with specific signaling pathway inhibitors, allows for the efficient derivation of chicken PSC-like cells. These cells exhibit characteristics of pluripotency and can differentiate into various cell types. This research enhances our understanding of chicken PSC culture conditions and lays the groundwork for their biomedical and biotechnological applications. The utilization of chicken embryonic-derived pluripotent stem cell (PSC) lines is crucial in various fields, including growth and development, vaccine and protein production, and germplasm resource protection. However, the research foundation for chicken PSCs is relatively weak, and there are still challenges in establishing a stable and efficient PSC culture system. Therefore, this study aims to investigate the effects of the FGF2/ERK and WNT/β-catenin signaling pathways, as well as different feeder layers, on the derivation and maintenance of chicken embryonic-derived PSCs. The results of this study demonstrate that the use of STO cells as feeder layers, along with the addition of FGF2, IWR-1, and XAV-939 (FIX), allows for the efficient derivation of chicken PSC-like cells. Under the FIX culture conditions, chicken PSCs express key pluripotency genes, such as POUV, SOX2, and NANOG, as well as specific proteins SSEA-1, C-KIT, and SOX2, indicating their pluripotent nature. Additionally, the embryoid body experiment confirms that these PSC-like cells can differentiate into cells of three germ layers in vitro, highlighting their potential for multilineage differentiation. Furthermore, this study reveals that chicken Eyal–Giladi and Kochav stage X blastodermal cells express genes related to the primed state of PSCs, and the FIX culture system established in this research maintains the expression of these genes in vitro. These findings contribute significantly to the understanding and optimization of chicken PSC culture conditions and provide a foundation for further exploration of the biomedical research and biotechnological applications of chicken PSCs. [ABSTRACT FROM AUTHOR]

Nanoparticles (NPs) have been surfacing as a pivotal platform for vaccine development. In our previous work, we developed a cholera toxin B subunit (CTB)-based self-assembled nanoparticle (CNP) and produced highly promising bioconjugate nanovaccines by loading bacterial polysaccharide (OPS) in vivo. In particular, the Klebsiella pneumoniae O2 serotype vaccine showcased a potent immune response and protection against infection. However, extremely low yields limited its further application. In this study, we prepared an efficient Klebsiella pneumoniae bioconjugate nanovaccine in Escherichia coli with a very high yield. By modifying the 33rd glycine (G) in the CNP to aspartate (D), we were able to observe a dramatically increased expression of glycoprotein. Subsequently, through a series of mutations, we determined that G33D was essential to increasing production. In addition, this increase only occurred in engineered E. coli but not in the natural host K. pneumoniae strain 355 (Kp355) expressing OPSKpO2. Next, T-cell epitopes were fused at the end of the CNP(G33D), and animal experiments showed that fusion of the M51 peptide induced high antibody titers, consistent with the levels of the original nanovaccine, CNP-OPSKpO2. Hence, we provide an effective approach for the high-yield production of K. pneumoniae bioconjugate nanovaccines and guidance for uncovering glycosylation mechanisms and refining glycosylation systems. [ABSTRACT FROM AUTHOR]

Existing studies have made a great endeavor in predicting users' potential interests in items by modeling user preferences and item characteristics. As an important indicator of users' satisfaction and loyalty, repeat purchase behavior is a promising perspective to extract insightful information for community e-commerce. However, the repeated purchase behaviors of users have not yet been thoroughly studied. To fill in this research gap from the perspective of repeated purchase behavior and improve the process of generation of candidate recommended items this research proposed a novel approach called ReRec (Repeat purchase Recommender) for real-life applications. Specifically, the proposed ReRec approach comprises two components: the first is to model the repeat purchase behaviors of different types of users and the second is to recommend items to users based on their repeat purchase behaviors of different types. The extensive experiments are conducted on a real dataset collected from a community e-commerce platform, and the performance of our model has improved at least about 13.6% compared with the state-of-the-art techniques in recommending online items (measured by F-measure). Specifically, for active users, with w = 1 and N U A ∈ 5 , 25 , the results of ReRec show a significant improvement (at least 50%) in recommendation. With α and σ as 0.75 and 0.2284, respectively, the proposed ReRec for unactive users is also superior to (at least 13.6%) the evaluation indicators of traditional Item CF when N U B ∈ 6 ,   25 . To the best of our knowledge, this paper is the first to study recommendations in community e-commerce. [ABSTRACT FROM AUTHOR]

A low-voltage and low-power true single-phase flip-flop that minimum the total transistor count by using the pass transistor logic circuit scheme is proposed in this paper. Optimization measures lead to a new flip-flop design with better various performances such as speed, power, energy, and layout area. Based on post-layout simulation results using the TSMC CMOS 180 nm and 90 nm technologies, the proposed design achieves the conventional transmission-gate-based flip-flop design with a 53.6% reduction in power consumption and a 63.2% reduction in energy, with 12.5% input data switching activity. In order to further the performance parameters of the proposed design, a shift-register design has been realized. Experimental measurements at 0.5 V/0.5 MHz show that this proposed design reduces power consumption by 47.3% while achieving a layout area reduction of 30.5% compared to the conventional design. [ABSTRACT FROM AUTHOR]

The type-B authentic response regulators (type-B ARRs) are positive regulators of cytokinin signaling and involved in plant growth and stress responses. In this study, we used bioinformatics, RNA-seq, and qPCR to study the phylogenetic and expression pattern of 35 type-B ARRs in Brassica napus. The BnARRs experienced gene expansion and loss during genome polyploidization and were classified into seven groups. Whole-genome duplication (WGD) and segmental duplication were the main forces driving type-B ARR expansion in B. napus. Several BnARRs with specific expression patterns during rapeseed development were identified, including BnARR12/14/18/23/33. Moreover, we found the type-B BnARRs were involved in rapeseed development and stress responses, through participating in cytokinin and ABA signaling pathways. This study revealed the origin, evolutionary history, and expression pattern of type-B ARRs in B. napus and will be helpful to the functional characterization of BnARRs. [ABSTRACT FROM AUTHOR]

Diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with practical applications. The diversified top-k weight clique search problem aims to search k maximal cliques that can cover the maximum weight in a vertex weighted graph. In this work, we propose a novel local search algorithm called TOPKWCLQ for the DTKWC search problem which mainly includes two strategies. First, a restart strategy is adopted, which repeated the construction and updating processes of the maximal weight clique set. Second, a scoring heuristic is designed by giving different priorities for maximal weight cliques in candidate set. Meanwhile, a constraint model of the DTKWC search problem is constructed such that the research concerns can be evaluated. Experimental results show that the proposed algorithm TOPKWCLQ outperforms than the comparison algorithm on large-scale real-world graphs. [ABSTRACT FROM AUTHOR]

China has become the major stakeholder in global photovoltaic (PV) technology. However, the existing mechanistic interpretation of "what promotes the technological progress of the Chinese PV industry" is controversial. This paper takes China's A-share listed PV enterprises from 1999 to 2019 as the research sample and uses a panel fixed-effect regression model to empirically test the impact of research and development (R&D) investment and government subsidies on the technological progress of PV enterprises. The results show that there is an "N"-shaped nexus between R&D investment and technological progress, and most PV enterprises are in the climbing stage of the N-shaped curve. With the development of the PV industry, the nexus will undergo a transformation from inverted U-shaped to N-shaped, indicating that R&D investment is a key driver of PV technological progress. Yet, government subsidies are a "double-edged sword". They have a significant positive direct effect on PV technological progress but also a negative moderating effect. Tax returns play a positive incentivizing role, while financial subsidies play a negative moderating role. This study provides a policy basis for the timely reduction of financial subsidies and increased R&D investment to promote technological progress in China's PV industry. [ABSTRACT FROM AUTHOR]

Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with fault events being rare and thus failure samples are very limited. To solve the problem above, a novel wide residual relation network (WRRN) is proposed for intelligent fault diagnosis of the RMs. Specifically, the WRRN is trained by performing a series of learning tasks in RMs with sufficient samples to obtain knowledge about how to diagnose, and then it is directly transferred to realize fault task of the RM with small samples. In this method, a wide residual network-based feature extraction module is used to generate representative fault features from input samples, and a relation module is designed to calculate the relation score between the sample pairs so as to determine their categories. Extensive experiments are conducted on two RMs to validate the WRRN method. The results demonstrate that the WRRN can accurately identify the fault types of the RMs with only small samples or even one sample. The WRRN significantly outperforms the existing popular methods in diagnostic performance. [ABSTRACT FROM AUTHOR]

Improved taxonomies and phylogenies are essential for understanding the evolution of organisms, the development of conservation plans, and the allocation of funds and resources, especially for threatened species with uncertain identities. Pears are an economically and nutritionally important fruit, and wild pear species are highly valued and protected because of their utility for the development of cultivars. Pyrus hopeiensis is an endangered species endemic to North China, which is sympatric with and difficult to distinguish from the widely distributed and morphologically similar species P. ussuriensis. To clarify its taxonomic identity, principal coordinate analysis was performed using 14 quantitative and qualitative characters from P. hopeiensis, P. ussuriensis, and P. phaeocarpa, and phylogenomic analysis was performed based on whole-genome resequencing and whole plastome data. Pyrus hopeiensis was synonymized with P. ussuriensis based on morphological and phylogenetic evidence, as well as our long-term field studies. Pyrus hopeiensis is proposed to be excluded from the list of local key protected wild plants. Given that the holotype of P. ussuriensis was not designated, a lectotype was designated in this work. Integrative evidence-based taxonomic study including museomics is suggested for organisms with uncertain identities, which will contribute to biodiversity conservation. [ABSTRACT FROM AUTHOR]

PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated with experts in the development of an air monitoring pilot study to measure PM2.5 concentrations to which employees and local residents are exposed during factory operating hours. To detect PM2.5, participants wore government-validated AtmoTube Pro personal air monitoring devices during three separate workdays (5 AM–1:30 PM) in August 2021. Results demonstrated a mean PM2.5 level inside the facility of 112.3 µg/m3, nearly seven-times greater than outdoors (17.3 µg/m3). Of the eight workers who wore personal indoor sampling devices, five showed measurements over 100 μg/m3. Welding-related activity inside the facility resulted in the greatest PM2.5 concentrations. This study demonstrates the utility of using low-cost air quality sensors combined with employee knowledge and participation for the investigation of workplace air pollution exposure as well as facilitation of greater health-related awareness, education, and empowerment among workers and community members. Results also underscore the need for basic measures of indoor air pollution control paired with ongoing air monitoring within the Santa Ana facility, and the importance of future air monitoring studies aimed at industrial facilities. [ABSTRACT FROM AUTHOR]

The issue of students' career choice is the common concern of students themselves, parents, and educators. However, students' behavioral data have not been thoroughly studied for understanding their career choice. In this study, we used eXtreme Gradient Boosting (XGBoost), a machine learning (ML) technique, to predict the career choice of college students using a real-world dataset collected in a specific college. Specifically, the data include information on the education and career choice of 18,000 graduates during their college years. In addition, SHAP (Shapley Additive exPlanation) was employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can predict students' career choice robustly with a precision, recall rate, and an F1 value of 89.1%, 85.4%, and 0.872, respectively. Furthermore, the interaction of features among four different choices of students (i.e., choose to study in China, choose to work, difficulty in finding a job, and choose to study aboard) were also explored. Several educational features, especially differences in grade point average (GPA) during their college studying, are found to have relatively larger impact on the final choice of career. These results can be of help in the planning, design, and implementation of higher educational institutions' (HEIs) events. [ABSTRACT FROM AUTHOR]

Chemotherapy for human solid tumors in clinical practice is far from satisfactory. Despite the discovery and synthesis of hundreds of thousands of anticancer compounds targeting various crucial units in cancer cell proliferation and metabolism, the fundamental problem is the lack of targeting delivery of these compounds selectively into solid tumor tissue to maintain an effective concentration level for a certain length of time for drug-tumor interaction to execute anticancer activities. The enhanced permeability and retention effect (EPR effect) describes a universal pathophysiological phenomenon and mechanism in which macromolecular compounds such as albumin and other polymer-conjugated drugs beyond certain sizes (above 40 kDa) can progressively accumulate in the tumor vascularized area and thus achieve targeting delivery and retention of anticancer compounds into solid tumor tissue. Targeting therapy via the EPR effect in clinical practice is not always successful since the strength of the EPR effect varies depending on the type and location of tumors, status of blood perfusion in tumors, and the physical-chemical properties of macromolecular anticancer agents. This review highlights the significance of the concept and mechanism of the EPR effect and discusses methods for better utilizing the EPR effect in developing smarter macromolecular nanomedicine to achieve a satisfactory outcome in clinical applications. [ABSTRACT FROM AUTHOR]

With the rising popularity of the Advanced Driver Assistance System (ADAS), there is an increasing demand for more human-like car-following performance. In this paper, we consider the role of heterogeneity in car-following behavior within car-following modeling. We incorporate car-following heterogeneity factors into the model features. We employ the eXtreme Gradient Boosting (XGBoost) method to build the car-following model. The results show that our model achieves optimal performance with a mean squared error of 0.002181, surpassing the model that disregards heterogeneity factors. Furthermore, utilizing model importance analysis, we determined that the cumulative importance score of heterogeneity factors in the model is 0.7262. The results demonstrate the significant impact of heterogeneity factors on car-following behavior prediction and highlight the importance of incorporating heterogeneity factors into car-following models. [ABSTRACT FROM AUTHOR]

A novel absolute positioning sensor for high-speed maglev train based on eddy current effect is studied in this paper. The sensor is designed with photoelectric switch and four groups of unilateral coplanar code-reading detection coil combination. The photoelectric switch realizes the positioning of the marker plate, and the four groups of detection coils read the mileage code of the mileage sign plate to obtain the absolute mileage information of the vehicle, which effectively reduces the quality and volume of the sensor, and reduces the impact of ice and snow. At the same time, the code-reading reliability and speed adaptability index are proposed. The code-reading reliability of the sensor is analyzed and tested under the fluctuation of levitation guidance, and the positioning error under the speed range of 0–600 km/h is calculated and analyzed. The results show that the novel sensor has the advantages of simple and compact structure. It still satisfies the system's requirements for absolute vehicle mileage information under the conditions of vehicle operating attitude fluctuations and changes in the full operating speed range. [ABSTRACT FROM AUTHOR]

Jatropha curcas is a natural non-food resource with high oil-content seeds, that has attracted worldwide attention as it is an ideal renewable resource for the production of biofuels. With the increasing use of vegetable insulating oil in related industries, it is valuable to develop the vegetable insulating oils from Jatropha curcas seed oil. This study explores how to use Jatropha curcas seed oil to prepare high-quality natural vegetable insulating oil. A six-step process is first established according to the optimization results of alkali refining, activated clay treatment and alumina treatment of Jatropha curcas seed oil, combined with cold treatment, water washing and high temperature decompression treatment. Physicochemical and electrical performance tests show that most of the properties of the prepared vegetable insulating oil are significantly improved compared with the original seed oil, and meet the standard requirements for vegetable insulating oil, especially with no sulfur corrosion, a breakdown voltage of 72 kV and an acid value (KOH, potassium hydroxide) of 0.012 mg/g. [ABSTRACT FROM AUTHOR]

An adaptive method for quantum state fidelity estimation in bipartite higher dimensional systems is established. This method employs state verifier operators which are constructed by local POVM operators and adapted to the measurement statistics in the computational basis. Employing this method, the state verifier operators that stabilize Bell-type entangled states are constructed explicitly. Together with an error operator in the computational basis, one can estimate the lower and upper bounds on the state fidelity for Bell-type entangled states in few measurement configurations. These bounds can be tighter than the fidelity bounds derived in [Bavaresco et al., Nature Physics (2018), 14, 1032–1037], if one constructs more than one local POVM measurements additional to the measurement in the computational basis. [ABSTRACT FROM AUTHOR]

Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies. [ABSTRACT FROM AUTHOR]

The low-altitude quasi-periodic (LQP) E-region echoes observed with the upgraded Hainan COherent scatter Phased Array Radar (HCOPAR) located in Hainan Island of China (19.5°N, 109.1°E) are presented. With the interferometry technique, interferometry equations were developed to investigate the structures of the irregularities. The results show that the plasma structures of the LQP echoes with a negative slope mainly drifted southwestward almost horizontally, but they also drifted southeastward in some striations. According to the movement of the plasma structures, we found strong wind shear in the region in which the LQP echoes were produced. Meanwhile, the periodicity of the LQP echoes may have been related to the strength of the wind shear. It is proposed that the plasma structures were most likely produced by the Kelvin–Helmholtz instability (KHI). [ABSTRACT FROM AUTHOR]

Proteins encoded by the G-box regulating factor (GRF, also called 14-3-3) gene family are involved in protein–protein interactions and mediate signaling transduction, which play important roles in plant growth, development, and stress responses. However, there were no detailed investigations of the GRF gene family in pear at present. In this study, we identified 25 GRF family members in the pear genome. Based on a phylogenetic analysis, the 25 GRF genes were clustered into two groups; the ε group and the non-ε group. Analyses of the exon–intron structures and motifs showed that the gene structures were conserved within each of the ε and non-ε groups. Gene duplication analysis indicated that most of the PbGRF gene expansion that occurred in both groups was due to WGD/segmental duplication. Phosphorylation sites analysis showed that the main phosphorylation sites of PbGRF proteins were serine residues. For gene expression, five PbGRF genes (PbGRF7, PbGRF11, PbGRF16, PbGRF21, and PbGRF23) were highly expressed in fruits, and PbGRF18 was highly expressed in all tissues. Further analysis revealed that eight PbGRF genes were significantly differentially expressed after treatment with different sugars; the expression of PbGRF7, PbGRF8, and PbGRF11 significantly increased, implying the involvement of these genes in sugar signaling. In addition, subcellular localization studies showed that the tested GRF proteins localize to the plasma membrane, and transgenic analysis showed that PbGRF18 can increase the sugar content in tomato leaves and fruit. The results of our research establish a foundation for functional determination of PbGRF proteins, and will help to promote a further understanding of the regulatory network in pear fruit development. [ABSTRACT FROM AUTHOR]

Piezoelectric sensors with high performance and low-to-zero power consumption meet the growing demand in the flexible microelectronic system with small size and low power consumption, which are promising in robotics and prosthetics, wearable devices and electronic skin. In this review, the development process, application scenarios and typical cases are discussed. In addition, several strategies to improve the performance of piezoelectric sensors are summed up: (1) material innovation: from piezoelectric semiconductor materials, inorganic piezoceramic materials, organic piezoelectric polymer, nanocomposite materials, to emerging and promising molecular ferroelectric materials. (2) designing microstructures on the surface of the piezoelectric materials to enlarge the contact area of piezoelectric materials under the applied force. (3) addition of dopants such as chemical elements and graphene in conventional piezoelectric materials. (4) developing piezoelectric transistors based on piezotronic effect. In addition, the principle, advantages, disadvantages and challenges of every strategy are discussed. Apart from that, the prospects and directions of piezoelectric sensors are predicted. In the future, the electronic sensors need to be embedded in the microelectronic systems to play the full part. Therefore, a strategy based on peripheral circuits to improve the performance of piezoelectric sensors is proposed in the final part of this review. [ABSTRACT FROM AUTHOR]

The polyetherimide diaphragm, sodium copper chlorophyllin (SCC), and copper ion coating composite used on earphones were observed to improve the high-frequency (10k–14k Hz) performance. This reinforcement phenomenon was expected to make the sound experience brighter and more diverse. By SEM observation, the mixed coating of SCC/Cu2+ on the polyethylenimine (PEI) diaphragm exhibited a planar blocky structure and was tightly bonded to the surface of the PEI polymer without the aid of colloids. The endothermic process of SCC and metal ion complexation was analyzed by isothermal titration calorimetry. The association ratios of SCC/Cu2+ and SCC/Ni2+ were 4/1 and 6/1, respectively, and the SCC/Cu2+ association yielded a stronger binding constant and more free energy. It was expected that the SCC/Cu2+(4/1) mixed liquid would be immobilized on the PEI polymer by multivalent interaction, including hydrogen-bonding networks between carboxyl groups of SCC and amine groups of PEI, and cross-linking of bridging copper ions. We used dimethylethylenediamine (DME) monomer instead of PEI polymer to analyze this multivalent interaction and observed a two-stage exothermic association of SCC/Cu2+(4/1) and DME with a total Gibbs free energy of 15.15 kcal/mol. We observed that the binding energy could be used to explain that the SCC/Cu2+ mixed formulation could be fixed on the surface of the PEI polymer and could enhance the strength of the PEI film. Compared with graphene films, which can continuously improve the performance of high and ultrasonic frequencies, this study was devoted to and was initiated for the purpose of applying porphyrin compounds to improve music performance. [ABSTRACT FROM AUTHOR]

Urban agglomerations are regions where the economy and population are highly concentrated, which are also spatial units with more concentrated carbon emissions. A detailed decomposition of driving factors based on changes in carbon emissions of urban agglomerations can provide a reference for better carbon reduction policies. In this paper, we establish an evaluation framework of carbon emission drivers of urban agglomeration from the perspective of CO2 generation and removal using a system dynamics method. The key influencing factors and optimal emission reduction measures of carbon emissions in urban agglomerations are explored. The results are as follows: (1) The industrial structure is the key influencing factor of carbon emissions; (2) compared with no implementation of any policies, the total carbon emissions and carbon emission intensity of integrated policies all significantly decrease, with a decrease of 43.68% and 53.32%, respectively in 2035; (3) energy structure adjustment has a significant effect in reducing carbon emissions and carbon emission intensity; (4) the role of increasing investment in technological innovation in ensuring achievement of "carbon peak" should not be ignored. It is found that integrated policies often exhibit a better emission reduction effect, but this effect is not a simple summation of the effects of each single policy. [ABSTRACT FROM AUTHOR]

The Flowering locus T (FT) gene encodes the florigen protein, which primarily regulates the flowering time in plants. Recent studies have shown that FT genes also significantly affect plant growth and development. The FT gene overexpression in plants promotes flowering and suppresses leaf and stem development. This study aimed to conduct a transcriptome analysis to investigate the multiple effects of Jatropha curcas L. homolog (JcFT) overexpression on leaf growth in tobacco plants. The findings revealed that JcFT overexpression affected various biological processes during leaf development, including plant hormone levels and signal transduction, lipid oxidation metabolism, terpenoid metabolism, and the jasmonic-acid-mediated signaling pathway. These results suggested that the effects of FT overexpression in plants were complex and multifaceted, and the combination of these factors might contribute to a reduction in the leaf size. This study comprehensively analyzed the effects of JcFT on leaf development at the transcriptome level and provided new insights into the function of FT and its homologous genes. [ABSTRACT FROM AUTHOR]

As a vital organelle in eukaryotic cells, the Golgi apparatus is responsible for processing and transporting proteins in cells. Precisely monitoring the status of the Golgi apparatus with targeted fluorescence imaging technology is of enormous importance but remains a dramatically challenging task. In this study, we demonstrate the construction of the first Golgi apparatus-targeted near-infrared (NIR) fluorescent nanoprobe, termed Golgi-Pdots. As a starting point of our investigation, hydrophobic carbon nanodots (CNDs) with bright NIR fluorescence at 674 nm (fluorescence quantum yield: 12.18%), a narrow emission band of 23 nm, and excellent stability were easily prepared from Magnolia Denudata flowers using an ultrasonic method. Incorporating the CNDs into a polymer matrix modified with Golgi-targeting molecules allowed for the production of the water-soluble Golgi-Pdots, which showed high colloidal stability and similar optical properties compared with pristine CNDs. Further studies revealed that the Golgi-Pdots showed good biocompatibility and Golgi apparatus-targeting capability. Based on these fascinating merits, utilizing Golgi-Pdots for the long-term tracking of the Golgi apparatus inside live cells was immensely successful. [ABSTRACT FROM AUTHOR]