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Pressure-induced surface-enhanced Raman spectroscopy (PI-SERS) has garnered significant attention as a subfield of SERS detection due to its capacity to regulate the band gap between molecules and substrates through pressure modulation. Currently, SERS detection primarily focuses on single molecules at atmospheric pressure with limited investigations conducted under high pressure conditions. Herein, we employed rose-shaped MoS2nanoflowers as the SERS substrate and realized selective PI-SERS enhancement of R6G molecules in the binary (MV+R6G) and ternary (MV+R6G+RhB) systems. The MoS2demonstrated an exceptionally low SERS detection limit of 5 × 10–6M in binary and ternary systems with equimolar amounts of molecules. High-pressure experimental results indicate that MoS2displays selective enhancement for R6G molecules, as evidenced by the comparison of the PI-SERS peak intensity ratio between MoS2and the probe molecules. The proposed enhancement mechanism in binary and ternary SERS systems under high pressure involves pressure-induced changes in both the band structures of the MoS2substrate and molecules, thereby influencing their charge transfer dynamics. Consequently, this approach holds great promise for practical applications in complex SERS systems operating under extreme conditions.

Conventional LLC synchronous rectifier (SR) schemes use detection circuits to sense the signals of voltage or current. But it typically operates below 500 kHz, which is unsuitable for high-frequency applications as it may lose large SR duty cycle caused by high dv/dt and GaN devices usage. To address the serious challenge, a digital SR control using extended harmonics impedance model is proposed for GaN LLC converters. By building the impedance model, the SR on-time is calculated accurately in the microcontroller. It not only minimizes the on-time of SR body diode with high precision, but also owns high immunity. A 650-kHz 1.5-kW GaN LLC converter was built. With the output of 28 V/53.5 A, the efficiency is as high as 98.1% at 1.5 kW by using the proposed SR. The power density is also up to 524 W/in3. Compared to the conventional SR, the efficiency improves 1.6% at full load.

This study delves into the ion recognition capabilities of a novel host molecule, emphasizing the role of conformational locking in dictating ion selectivity. By employing the Buchwald–Hartwig cross-coupling reaction, we have notably shifted the ion receptor’s selectivity from K+to Na+. The findings are supported by computational simulations that reveal differences in binding energies and molecular strain impacting ion recognition. This innovative structural modification broadens the scope for alterations at the calix[4]arene’s lower rim, paving the way for new methods and strategies in modulating ion recognition selectivity.

A fall is an abnormal behavior that rarely occurs, however, once it occurs in a crowded scenario, it is prone to cause local disturbance, density-velocity fluctuations, and crowd instability even leading to a stampede accident. Currently, research on fall behavior mainly depends on single-point detection approach but lacks investigation on disturbing mechanism in a crowd flow. To address this issue, this paper first proposed a pressure dynamics model based on limb-level contact to analyze pedestrian behavioral characteristics. Then, based on the random Brownian motion, the disturbance distribution of fall behavior was determined theoretically. Using two parameters (a pressure coefficient and disturbance intensity), the pressure term of the fluid dynamics Aw-Rascle model was improved, and the internal disturbance propagation model (DPM) of fall behavior was established, as a primary contribution of this study. Further, to eliminate the disturbance propagation in a crowd, damping motion theory was employed. The disturbance elimination mechanism of abnormal behavior was discussed to suppress disturbance propagation caused by a pedestrian fall behavior. To validate the proposed model, both field experiment and numerical simulation were conducted respectively. The stability performance of the proposed DPM was evaluated with standard deviation (less than 0.1631) based on 50 repetitive experiments. Results showed that this study could help discover the disturbance propagation dynamics and elimination mechanisms of pedestrian fall behavior in a crowded scenario.

Maximizing the sustainable recycling of spent lithium-ion batteries (LIBs) shows economic and environmental significance. This study recovered lithium cobaltate (LiCoO2, LCO) from spent LIBs cathode powder. The recovered LCO was then combined with NaHSO3to remove refractory organic pollutants of carbamazepine (CBZ) in water. The degradation of CBZ reached 80.0% within 60 min, by 1O2, SO4−⋅, ⋅OHand O2−⋅generated in the LCO/NaHSO3reaction. The electron transfer between Co (III) and Co (II) was beneficial to the generation of free radicals. The LCO/NaHSO3degraded CBZ effectively in both secondary outlet water and tap water. However, high concentrations of inorganic ions (Cl−, HCO3−, HPO42−, SO42−, NO3−) and natural organic matter (humic acid, HA) could inhibit the degradation of CBZ. After three cycles, the stability of the LCO/NaHSO3system was demonstrated by the maintained high efficiency in the degradation of CBZ. The obtained data indicate that the LCO/NaHSO3system holds great application potential in the field of advanced oxidation degradation of pollutants.

Pneumonia is one of the largest causes of death in the world. Deep learning techniques can assist doctors to detect the areas of pneumonia in the chest X-rays images. However, existing methods lack sufficient consideration for the large variation scale and the blurred boundary of the pneumonia area. Here, we present a deep learning method based on Retinanet for pneumonia detection. First, we introduce Res2Net into Retinanet to get the multi-scale feature of pneumonia. Then, we proposed a novel predicted boxes fusion algorithm, named Fuzzy Non-Maximum Suppression (FNMS), which gets a more robust predicted box by fusing the overlapping detection boxes. Finally, we get the performance outperforms than existing methods by integrating two models with different backbones. We report the experimental result in the single model case and the model ensemble case. In the single model case, RetinaNet with FNMS algorithm and Res2Net backbone is better than RetinaNet and other models. In the model ensemble case, the final score of predicted boxes that fused by the FNMS algorithm is better than NMS, Soft-NMS, and weighted boxes fusion. Experimental results on the pneumonia detection dataset verify the superiority of the FNMS algorithm and the proposed method in the pneumonia detection task.

Dynamic threshold voltage shifts (DTVS) in silicon carbide (SiC) MOSFETs are investigated using an ultrafast characterization method that incorporates nanosecond-range switching. A positive gate bias causes a positive DTVS and a negative gate bias causes a negative DTVS, which is observed within a timescale range of 40 ns–1 s. At a gate bias of −10 V, the negative DTVS can reach approximately −4 V, which changes the threshold voltage from positive to negative. A mechanism involving carrier trapping at/near the SiO2 /SiC interface is proposed. Then, the impact of DTVS on dynamic behavior is evaluated by a double-pulse test (DPT) under different off-state gate biases ( ${V} _{\text {GS,OFF}}$ ). A negative DTVS causes the channel turn-on earlier, increasing the current overshoot ( ${I} _{\text {OS}}$ ) during the turn-on process. As ${V} _{\text {GS,OFF}}$ decreases from 0 to −10 V, the turn-on loss ( ${E} _{\text {ON}}$ ) increases by 11.2% at a load current of 10 A. Moreover, the ${E} _{\text {ON}}$ changes from an increase to a decrease when the turn-on gate resistance ( ${R} _{\text {G,ON}}$ ) increases from 10 to $50~\Omega $ . Therefore, for a specific gate driving condition, an optimized ${R} _{\text {G,ON}}$ can be chosen to compromise the DTVS-induced ${E} _{\text {ON}}$ change.

With the escalating demand for fast simulation of large-scale multi-input multi-output (MIMO) RCS circuits formulated as second-order differential systems, the need arises for more effective decentralized second-order model order reduction (MOR) methods, while providing a desired approximation of the original system. Dynamic relative gain array (DRGA) that takes into account both the steady-state and dynamic system information has shown promising efficacy in measuring the degree of each loop interaction, which is crucial for decoupling a MIMO system into several multi-input single-output (MISO) subsystems. Although several decentralized MOR methods have been introduced for dimension reduction to linear MIMO networks, hardly has any research explored second-order decentralized MOR methods with regard to MIMO RCS circuits. Besides, the existing DRGA method based on first-order state feedback predictive control greatly increases the computational complexity when directly applying to second-order RCS systems. Hence, we develop a second-order block Arnoldi method based on DRGA, termed DRGA-SOBAR, which enables the extension of the SOAR method and the second-order DRGA method to MIMO scenarios. Experimental results on RCS networks show that most input-output interactions are negligible in terms of the magnitude-wise insignificance, and our proposed DRGA-SOBAR based reduced systems perform with higher accuracy compared to the PRIMA and the generalized block SOAR (SOBAR) methods, and higher efficiency compared to the decentralized SOBAR algorithm based on RGA method as well.

Phase separation inside mammalian cells regulates the formation of the biomolecular condensates that are related to gene expression, signalling, development and disease. However, a large population of endogenous condensates and their candidate phase-separating proteins have yet to be discovered in a quantitative and high-throughput manner. Here we demonstrate that endogenously expressed biomolecular condensates can be identified across a cell’s proteome by sorting proteins across varying oligomeric states. We employ volumetric compression to modulate the concentrations of intracellular proteins and the degree of crowdedness, which are physical regulators of cellular biomolecular condensates. The changes in degree of the partition of proteins into condensates or phase separation led to varying oligomeric states of the proteins, which can be detected by coupling density gradient ultracentrifugation and quantitative mass spectrometry. In total, we identified 1,518 endogenous condensate proteins, of which 538 have not been reported before. Furthermore, we demonstrate that our strategy can identify condensate proteins that respond to specific biological processes.

Vienna rectifier with simple structure, which has gained popularity in recent years for ac to dc power conversion for many industrial applications such as electric vehicle charging systems and uninterrupted power system (UPS) systems, accomplishes the operation of unit power factor. However, the distortion of the input current near the zero-crossing point is a puzzle that will increase the current harmonic content. In this article, the cause of the zero-crossing distortion is analyzed, and an improved space vector pulsewidth modulation modulation strategy is proposed. Moreover, the sliding mode control for the outer voltage loop and the phase compensation multiresonance control for the inner current loop is proposed to improve the dynamic response performance of the dc side and achieve zero-error control of the ac side. Simulation and experimental results verify the effectiveness of the proposed modulation scheme and control strategy.

With the increasing number of video surveillance cameras in public buildings, it has become challenging, yet significant to detect abnormal pedestrian behaviors in crowd management, to prevent crowd accidents. Although current advancements in human action recognition based on computer vision can help detect abnormal behaviors after their incidence, majority of them lack the ability to detect potential characteristics prior to the occurrence of real abnormal behaviors. Hence, in this study, we addressed this issue by proposing a novel dynamic centroid model (DCM) of a human body, and rebuilding pedestrian joint sub-segments from human skeleton key nodes obtained in camera images. We built a weighted centroid-combined force model based on Newton’s second law, considering acceleration, mass inertial of human body sub-segments, and internal constraints. Thereafter, pedestrian kinematic and dynamic parameters were analyzed, such as speed, trajectory, force. Furthermore, abnormal behavior detection criteria were constructed for typical abnormal-behavior cases: U-turn and fall-down. Comparative experiments between the proposed DCM and the state-of-the-art methods were conducted. The experimental results showed that the model was capable of detecting abnormal behaviors, with mean values of lead time of 277 ms in U-turn behavior, and 562 ms in fall-down behavior, prior to the captured occurrence of these two abnormal behaviors. Finally, a de-occlusion algorithm was designed and jointly used with DCM, validated by a fall-down detecting experiment including partial occlusion. Therefore, this study holds significant value for the prevention of abnormal pedestrian behaviors in public places.

AbstractWhether a stock market should matter or not when monetary policy is concerned seems to be a controversial issue. The purpose of this study is to indicate whether the central bank should use monetary policy to help the stock market or not. Based on macroeconomic data such as interest rate and the stock market, we adopt a novel Bayesian time-varying regression model and determine that the impact of interest rate changes on stock returns varies over time in China, after controlling various macroeconomic factors. Although on average interest rates negatively impact stock price returns, they tend to have an abnormal positive effect at market high points, following a time-varying dynamic pattern. Surprisingly, during periods of overheated economic development, an increase in interest rates cannot suppress the rise in stock prices. Therefore, policymakers need to pay attention when accelerating the marketisation of interest rates and initiating the preventive role of timely and strategic adjustment of interest rates.

Modern electromagnetic (EM) absorbing materials (EAMs) are experiencing a revolution triggered by advanced information technology. Simultaneously, the diverse harsh EM application scenarios entail a more stringent appeal of practicability to EAMs, especially under high-temperature conditions. Therefore, exploring EAMs with both excellent absorbing performance and practicability at elevated temperatures is necessary. Herein, a novel 3D porous carbon foam/carbon nanotubes@Si3N4(CF/CNTs@Si3N4) heterostructure was constructed by the chemical vapor infiltration process. The optimally grown 1D CNTs embedded in 3D CF/Si3N4are utilized to provide abundant nanointerface coupling effects to compensate for the excessive increase in the conductive loss during rising temperature to realize a self-adjustment in response to high temperature. A high-efficiency EM absorption over a wide temperature range from 25 to 480 °C was achieved (with a ≥90% absorbing ratio covering the whole X-band). In addition, the Si3N4coating can improve the thermal stability of the carbon matrix and maintain the tailored inner structure. Multiple investigations into other environmental adaptabilities also exhibited the application perspective of such a heterostructure. This work points out a new strategy for preparing designable, efficient, and high-temperature applicable EAMs, promoting the diverse development of electronic devices.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ajog.2006.01.022 Byline: Wenjie Zhu (a), Xuemei Li (a), Xiumin Chen (a), Zhan Lin (b), Li Zhang (c) Abstract: This study was undertaken to assess the effectiveness of transvaginal ultrasound-guided ovarian interstitial laser-coagulation treatment in anovulatory women with polycystic ovary syndrome (PCOS). Author Affiliation: (a) Departments of Reproductive Health (b) Gynecology (c) Central Laboratory, ShenZhen Maternity and Child Healthcare Hospital, ShenZhen, China Article History: Received 1 December 2005; Revised 25 December 2005; Accepted 10 January 2006 Article Note: (footnote) Supported by a grant for scientific research from the ShenZhen Bureau of Science and Technology (No. 200304167). Reprints not available from the authors.

To investigate the safety and feasibility of topical injection of bone marrow derived mesenchymal stem cells (BM-MSCs) in acute severe ocular burns.

A difluorocarbene-mediated cascade cyclization reaction for rapid access to gem-difluorinated 3-coumaranone derivatives was developed. The difluorocarbene acts as a bipolar CF2building block, which enables a homologation cyclization process via sequentially reacting with the phenolate and the ester group on the same substrate. The potential application of this synthetic approach is demonstrated by a late-stage modification of diethylstilbestrol. Mechanistic studies revealed the multiple crucial roles played by the Ruppert–Prakash reagent.

Wide inductance variation of powder core inductors used in the Vienna rectifier challenges the system stability and limits the bandwidth of the current loop, which leads to more harmonics. In this article, a current control method with extended bandwidth is proposed for a three-phase Vienna rectifier to improve the system stability and reduce harmonics. The averaged model of three-phase Vienna rectifier considering the inductance variation is derived in the natural a-b-c frame. The impact of inductance variation on control system is analyzed. To extend the control bandwidth, the instantaneous inductance is obtained based on a piecewise linearization method and is embedded into a classical current control loop. Since the control method is easy to implement and occupies a little time of digital controller, the sampling frequency can be increased to twice the sampling frequency, which makes the bandwidth further extended. The proposed method keeps a wide and fixed bandwidth of the current loop, which improves the harmonics reduction ability and dynamic performance compared with classical control methods. Finally, the effectiveness of the proposed method is verified by a three-phase Vienna rectifier prototype.

Esophageal squamous cell carcinoma (ESCC) is a poor-prognosis cancer type with limited understanding of its molecular etiology. Using 508 ESCC genomes, we identified five novel significantly mutated genes and uncovered mutational signature clusters associated with metastasis and patients’ outcomes. Several functional assays implicated that NFE2L2may act as a tumor suppressor in ESCC and that mutations in NFE2L2probably impaired its tumor-suppressive function, or even conferred oncogenic activities. Additionally, we found that the NFE2L2mutations were significantly associated with worse prognosis of ESCC. We also identified potential noncoding driver mutations including hotspot mutations in the promoter region of SLC35E2that were correlated with worse survival. Approximately 5.9% and 15.2% of patients had high tumor mutation burden or actionable mutations, respectively, and may benefit from immunotherapy or targeted therapies. We found clinically relevant coding and noncoding genomic alterations and revealed three major subtypes that robustly predicted patients’ outcomes. Collectively, we report the largest dataset of genomic profiling of ESCC useful for developing ESCC-specific biomarkers for diagnosis and treatment.

This study presents a bioinspired ion host featuring continuous binding sites arranged in a tunnel-like structure, closely resembling the selectivity filter of natural ion channels. Our investigation reveals that ions traverse these sites in a controlled, sequential manner due to the structural constraints, effectively mimicking the ion translocation process observed in natural channels. Unlike systems with open binding sites, our model facilitates sequential ion recognition state transitions, enabled by the deliberate design of the tunnel. Notably, we observe dual ion release kinetics, highlighting the system’s capacity to maintain ion balance in complex environments and adapt to changing conditions. Additionally, we demonstrate selective binding of two different ions─a challenging task for systems lacking structured tunnels.

Mesoporous polydopamine/titanium dioxide (PDA/TiO2) composite nanospheres with different polydopamine content were successfully synthesized by a facile organic–inorganic self-assembly method. The synthesized composite materials were characterized by N2adsorption–desorption isotherm, X-ray photoelectron spectroscopy, X-ray diffraction, transmission electron microscopy (TEM), energy dispersive spectrometry, field emission scanning electron microscopy, Fourier transform infrared spectroscopy,  ζ--potential, and cyclic voltammetry. The results showed that the composite material with the most exposure active site exhibits a spherical morphology. The composite material could also be used as a reducing agent, and spheres of the composite material were used as adsorbents for removing hexavalent chromium from aqueous solutions by reducing Cr(VI) to Cr(III). The influence of factors, including the initial solution pH, contact time, initial ion concentration, temperature, coexisting ions, and dose of adsorbent, was also studied. The hexavalent chromium adsorption data fit well to a Langmuir isotherm model, and the maximum adsorption capacity was shown to be 244.5 mg/g. The kinetics data follow a pseudo-second-order kinetic model. The study of thermodynamics indicated that the adsorption on the composite nanospheres was a spontaneous and endothermic process. Moreover, a mechanism of adsorption and reduction on the composite spheres was proposed.

Understanding the effects of panic on crowd dynamics in emergency situations has long been considered necessary for pedestrian evacuation control. In the case of disasters, stampedes caused by panic behaviors occur with high possibility, and pedestrians are crushed or trampled, leading to enormous casualties. To eliminate the computational errors accumulated in the traditional macromodel, a macro-microconversion model based on the SF (social force) model and the AR (Aw-Rascle) model is proposed in this paper. The purpose is to use the crowd parameters of the microscopic model as the input part of the macroscopic model and to combine the advantages of the two models to ensure accuracy and improve calculation performance. The concept of the “pressure term” is defined to measure the panic level of the crowd. In addition, a flowchart of the numerical simulation is designed based on the road network conditions at the trampling site. To validate the conversion model, a numerical simulation is conducted in a case study of the Mecca Hajj stampede in 2015. The simulation results display the whole process of crowd marching and meeting with the dynamic variations of the “pressure term.” The simulation results are compared with the traditional simulation results based on a Gaussian distribution, which verifies that the simulation results obtained by the proposed method are closer to the real situation. Moreover, in this study, a new micromacro transformation method for crowd evaluation dynamics, which can enhance computing speed and execution efficiency, is provided.

Abstract

Graph data are widely available in complex network systems. Numerous community detection algorithms have been investigated to study graph problems wherein the network topology offers abundant behavioral and functional information. In real scenarios, networks are extremely susceptible to external perturbations, primarily because of sparse topological information. Consequently, maintaining robust community detection performance when confronted with intricate network attacks or perturbations is challenging. Previous anti-graph perturbation methods have typically been adjusted to specific perturbation types, leading to the degradation or failure of these defences when confronted with other types of attack. Accordingly, a unified robust framework that leverages extreme adversarial attacks is proposed in this paper. Specifically, a novel graph perturbation module is introduced into the model to generate extreme undirected or directed perturbations through dynamic updating; this ensures that the model can fit the attacked network well. The proposed framework derives a unified perturbation formulation capable of simultaneously attacking symmetric and asymmetric networks. Furthermore, this framework can be applied to many existing non-negative matrix factorization-based community detection methods. Extensive experiments on artificial and real-world networks demonstrate that the proposed framework significantly improves the robustness of detection tasks, particularly in noisy networks. • A unified robust network embedding framework is proposed for CD to enhance the robustness against structural perturbations. • Two versions of the proposed NMF-based model are proposed for the directed and undirected graph data. • An efficient optimization algorithm is devised to construct network perturbation matrices for the extreme global attacks. [ABSTRACT FROM AUTHOR]

This work investigated the normal spectral emissivity characteristics of Steel 304 over a temperature range from 800 to 1100 K and a wavelength range from 1.4 to 2.1 μm. In the experiments, specimens were heated in air for 6 h at a certain temperature. Two platinum–rhodium thermocouples were symmetrically welded onto the front surface of the specimens near the measuring area for accurate monitoring of the surface temperature. The temperatures measured by the two thermocouples had an uncertainty of 1 K. The average of their readings was regarded as the temperature of the specimen surface. The radiance stemming from the specimens were measured at temperatures from 800 to 1100 K in increments of 20 K by multispectral radiation thermometry. Variation in the normal spectral emissivity with respect to wavelength and temperature was studied for different heating times. The effect of surface oxidization on the accurate prediction of the specimen surface temperature was discussed. Similar variation in the normal spectral emissivity for different heating times showed that the emissivity followed a certain rule. Nine emissivity models were examined for accuracy in the temperature prediction. The results showed that the four-parameter log-linear wavelength and line wavelength emissivity models generated best overall temperature prediction. We concluded that the effect of surface oxidization on the emissivity models of Steel 304 could be dismissed; and that the same models could be used to predict its surface temperature from 800 to 1100 K.

AbstractRemoval of Co(II) and Mn(II) ions from the pure terephthalic acid (PTA) wastewater onto Na-bentonite has been investigated in this work. Batch experiments were performed to elucidate the effects of various parameters, such as dosage of adsorbent, temperature, and initial pH, on the adsorption process. It has been found that the simulated adsorption process of the pseudo-second-order kinetic model was in better agreement with experimental results than that of the pseudo-first-order and intra-particle diffusion kinetic models. Moreover, the adsorption experiments were conducted at four temperatures, which was 298.15, 308.15, 318.15, and 328.15 K, respectively, with the initial metal concentrations ranging from 30 to 120 mg L−1, so as to study the adsorption thermodynamics. Compared with the Freundlich and Dubinin–Radushkevich models, the Langmuir isotherm model agreed better with experimental data. The Gibb’s free energy change (ΔG), enthalpy change (ΔH), and entropy change (ΔS) of the adsorption process were calculated at four temperatures. The results indicated that the adsorption process was endothermic and spontaneous. When the adsorbent concentration was 3 g L−1, the adsorption percentages of Co(II) and Mn(II) can be attained about 95.09 and 95.03%, respectively. Therefore, it can be concluded that Na-bentonite could be used as an efficient adsorbent to remove heavy metal ions from the PTA wastewater.

AbstractMCM-41 is demonstrated to be an efficient adsorbent for the removal of two representative cationic dyes, namely Basic Violet 5BN (BV) and Basic Green (BG). Characterization of the adsorbent was studied by FTIR, X-ray diffraction, and Brunauer-Emmett-Teller. And various parameters including the solution pH, adsorbent dosage, contact time, initial cationic dye concentration, and temperature were systematically analyzed. It was found that the adsorption was pH independent and the maximum removal percentage of 86.81% BV occurred at pH 5.0 and 25°C using 0.3-g MCM-41, whereas 94.79% BG under the same experimental conditions. Besides, both the adsorptive removal of BV and BG by MCM-41 increased with the adsorbent dosage and contact time, but decreased with the initial dye concentration and temperature. In single component systems, equilibrium data were well presented by the Langmuir isotherm, suggesting the adsorption to be monolayer. And the Evalues (<8 kJ/mol) resulted from the D–R isotherm fitting showed the adsorption was physical in nature. The adsorption kinetics fitted better with the Lagergren pseudo-second-order model, and rate-controlling steps were both the external diffusion and intraparticle diffusion. Thermodynamic parameters (ΔG°, ΔH°, and ΔS° < 0) indicated that the adsorption process was feasible, spontaneous, and exothermic. Furthermore, competitive adsorption existed between the mixed dyes, and the removal efficiency and adsorption capacity of the dyes in binary component systems were lower than those in single systems. The adsorption isotherm and kinetic data of the binary component systems could also be well described by the Langmuir and Lagergren pseudo-second-order models. High recovery percentage of BV and BG by 0.1-M NaOH solution allowed excellent desorption and regeneration of the cationic dyes in practical applications.

•A novel and efficient mesoporous Fe–Al bimetallic As(V) adsorbent was developed.•The influence of ferric additive on As(V) uptake was investigated firstly.•Equilibrium time for As(V) removal by Fe–MA was far shorter than MA.•The adsorption was not dependent on solution pH.•Formation of Fe2(SO4)3and Al2(SO4)3accelerated the uptake of As(V).

The aim of this study was to evaluate the benefits of the addition of platinum agents for the treatment of patients with triple-negative breast cancer on the basis of randomized-controlled trials (RCTs). A fully recursive literature search was performed in the Cochrane Controlled Trials Register Databases, Medline, EMBASE, and Chinese Biomedical Literature Database in any language. RCTs were considered for inclusion. Eight randomized-controlled trials totaling 1142 patients were included. The objective response rate was reported in six RCTs, which were divided into two subgroups: palliative chemotherapy for a metastatic setting and neoadjuvant chemotherapy. Using the fixed-effects model, the difference between the platinum-based group and the non-platinum-based group was found to be statistically significant in the overall study [relative risk (RR)=1.36, P<0.00001], the subgroup of palliative chemotherapy (RR=2.42, P<0.00001), and the subgroup of neoadjuvant (RR=1.15, P=0.01). Pathological complete response rates were based on five studies, and the results between the platinum-based group and the non-platinum-based group also reached statistical significance both in the fixed-effects model (RR=1.43, P<0.0001) and in the random-effects model (RR=1.47, P=0.01). The results seemed to yield a better response rate and pathological complete response rate for platinum-based therapy in triple-negative breast cancer. However, because of the heterogeneous nature of primary trial outcomes, caution should be exercised in coming to this conclusion and further research is necessary to support these findings.

Based on the anti-jamming performance of differential frequency hopping (DFH) systems in Additive White Gaussian Noise (AWGN) channel, Fountain code is introduced to the DFH systems as the outer error correcting code in this paper to investigate the improvements against partial-band jamming over AWGN channel. The performance of Fountain coded DFH is theoretically analyzed and numerically simulated. The total frequency of hopping in the simulation is 16, and results show that, on one hand, when exact jamming state information (JSI) is available, and the number of jamming frequency is n=16, the bit error rate (BER) of 10−3 is achieved with the signal to interference ratio (SIR) approximately 7.5 dB over AWGN channel, and the performance improves about 1–1.5dB compared with the no-coded system. When the number of jamming frequency is n=2, the performance increases 15–17dB. On the other hand, when JSI is unavailable, a joint JSI estimation and decoding algorithm is proposed. The BER of 10−3 is achieved with jamming-frequency n=16, SIR=8dB and signal noise ratio (SNR) 10dB over AWGN channel. It's proved that this algorithm provides robust anti-jamming performance even without JSI. The anti-jamming performance of Fountain coded DFH systems is obviously superior to no-coded DFH systems.

The CoFe/ZSM-5 catalyst was successfully synthesized through the impregnation method and used for peroxymonosulfate (PMS) activation to degrade sulfamethoxazole (SMX). The CoFe/ZSM-5 sample showed more excellent catalytic performance than that of Co/ZSM-5 and Fe/ZSM-5 due to the synergistic effect between Co and Fe, and the removal efficiency of SMX reached to 93% within 60 min. Total organic carbon (TOC) removal rate was 85% in the CoFe/ZSM-5/PMS reaction system. In addition, the CoFe/ZSM-5/PMS system showed excellent catalytic ability on SMX degradation in the pH range of 6.0–9.0. The CoFe/ZSM-5 sample showed excellent stability and reusability, and the degradation efficiency of SMX still reached to 90% in the fifth run. The quenching experiment proved that SO4-·and 1O2active species were produced, and 1O2played a dominant role during the degradation process. This study provides an efficient strategy for the preparation of heterogeneous catalysts used as the PMS activator to degrade organic pollutants in wastewater.

Exploration of two-dimensional (2D) nonlayered materials is a significant work for developing the 2D materials family, owing to their intriguing physicochemical properties and promising applications in various fields. Herein, we present the controllable chemical vapor deposition (CVD) synthesis of nonlayered β-Bi2Te4O11with a 2D morphology. It was revealed that the 2D growth of β-Bi2Te4O11depends on the proper precursor. Moreover, the corresponding mechanism was demonstrated by density functional theory calculations. Moreover, the growth conditions of 2D β-Bi2Te4O11were optimized by a machine learning (ML) strategy. The resultant 2D β-Bi2Te4O11flakes with a large domain size (∼120 μm) and ultrathin thickness (∼9.5 nm) indicate promising applications of ML in guiding the CVD synthesis of 2D nonlayered materials. Compared with the bulk β-Bi2Te4O11with an antiglass structure, the as-grown 2D β-Bi2Te4O11shows a unique cation-ordering superstructure. In addition, the 2D β-Bi2Te4O11-based photodetector shows a prominent responsivity of 79.5 A W–1at 375 nm. This work shows an efficient strategy for preparing 2D nonlayered materials and provides a brand-new platform to study the properties and applications of 2D nonlayered materials.

The kinetics of a detailed chemically reacting system can potentially be very complex. Although the chemist may be interested in only a few species, the reaction model almost always involves a much larger number of species. Some of those species are radicals, which are very reactive species and can be important intermediaries in the reaction scheme. A large number of elementary reactions can occur among the species; some of these reactions are fast and some are slow. The aim of simplified kinetics modeling is to derive the simplest reaction system which retains the essential features of the full system. An optimization‐based method for reduction of the number of species and reactions in chemical kinetics models is described. Numerical results for several reaction mechanisms illustrate the potential of this approach.

Cryoprotectants are crucial factors in cell cryopreservation. Trehalose (Tre), a nontoxic, nonreducing, and natural disaccharide, has the potential to protect cells as a cryoprotectant. As an inducer of autophagy, Tre can influence the development of many diseases and may also have an effect on cell cryopreservation through this mechanism. In this study, human aortic endothelial cells were preserved in different cryopreservation fluids with or without dimethyl sulfoxide and Tre was added. Subsequently, the expression of the main autophagy-related genes LC3, BECN, and P62, cell death and apoptosis, and the proliferation rate were measured in different groups after cryopreservation. Our data showed that Tre can improve the expression of the autophagy-related genes LC3 and BECN and reduce the expression of P62. Dead/alive staining and flow cytometry showed that cell death and cell apoptosis were reduced during cryopreservation with Tre. In addition, the cell proliferation rate after thawing was increased in the Tre group when compared with others. These results all indicated that there might be a connection between Tre-triggered autophagy and the protective role of Tre in cell cryopreservation. Furthermore, strategies to regulate autophagy to reduce apoptosis in this process should be investigated in future research.

Amino-functionalized mesoporous silica MCM-41 materials have been prepared to develop efficient adsorbents of Cr(VI) in wastewater, using silica fume as silica source. Functionalization with amino groups has been carried out by using grafting method. The materials have been characterized by means of X-ray diffraction (XRD), nitrogen (N2) adsorption-desorption, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). Adsorption potential of the material for Cr(VI) removal from aqueous solution was investigated by varying experimental conditions such as pH, initial metal concentration, and contact time. The equilibrium data were analyzed using the Langmuir and Freundlich isotherm by linear regression analysis, and the results show that the adsorption equilibrium data obeyed the Langmuir model. In addition, the kinetics analysis revealed that the overall adsorption process was successfully fitted with the pseudo-second-order kinetic model.

Most community detection algorithms require the global information of the networks. However, for large scale complex networks, the global information is often expensive and even impossible to obtain. Therefore, local community detection is of tremendous significance. In this paper, a new local community detection algorithm based on NGC nodes, named LCDNN, is proposed. For any node, its NGC node refers to the nearest node with greater centrality. In the LCDNN, local community C initially consists of the given node, v . Then, the remaining nodes are added to the local community one by one, and the added node should satisfy: 1) its NGC node is in C , or it is the NGC node of the center node of C ; and 2) the fuzzy relation between the node and its NGC node is the largest; 3) the fuzzy relation is no less than half of the average fuzzy relation of the current local network. The experimental results on ten real-world and synthetic networks demonstrate that LCDNN is effective and highly competitive. Concurrently, LCDNN can also be extended for multiscale local community detection, and experimental results are provided to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]