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Magnetic compensation is an important means to suppress interference and improve signal-to-noise ratio in magnetic signal measurement. In the process of geomagnetic signal measurement, magnetic compensation plays a crucial role. At present, the commonly used method for magnetic compensation is the Tolles-Lawson (T-L) model, which can effectively correct magnetic measurement data. The T-L model is used to compensate for carrier attitude-related noise. In general, magnetic compensation systems, direction cosine is an indispensable parameter, usually measured by the fluxgate magnetometer. The T-L model assumes a rigid connection between the tail boom and the carrier. This ignores the swing noise generated by the tail boom swings, which leads to errors in the measured carrier attitude information and affects the magnetic compensation. Building upon the T-L model, this article analyzes the reasons behind swing noise affecting magnetic measurement data and derives the relationship between swing noise and magnetic compensation results. In this article, attitude sensors are used to obtain the relative angle offset between the carrier coordinate system and the sensor coordinate system. By combining the angle offset with the fluxgate magnetometer data, the calculation of direction cosine is corrected and an improved compensation method is proposed. The experiment shows that compared to the classic T-L model, this method improves the magnetic compensation quality by 20.3%–24.6%. This indicates that the method proposed in this article has a better magnetic compensation effect.

The study aimed to investigate the predictive value of clinical characteristics, major computed tomography angiography (CTA) indexes of carotid AS (carotid lumen stenosis and plaque burden), and inflammatory pericarotid adipose tissue for acute stroke risk in patients with a moderate or higher degree of carotid stenosis. In all, 119 patients with unilateral carotid stenosis who underwent head and neck computed tomography angiography were included and assigned to the stroke group or non-stroke group according to magnetic resonance imaging. Pericarotid adipose tissue attenuation value, net enhancement value in the base phase and the enhancement phase, and atherosclerotic features (plaque burden and lumen stenosis) were recorded. Multivariate logistic regression analysis and the operating characteristic curve (ROC) were performed to establish a predictive model for the presence of acute ischemic stroke. ROC analysis showed that pericarotid adipose tissue attenuation value and lumen stenosis were predictive factors for stroke. The AUC of pericarotid adipose tissue attenuation (PCAT) attenuation, lumen stenosis, the novel prediction model independently constructed based on PCAT attenuation, and lumen stenosis resulted in 0.838 (95% CI 0.759–0.899), 0.700 (95% CI 0.826–0.944), and 0.942 (95% CI 0.884–0.977), respectively. The model had a sensitivity and specificity of 0.909 and 0.893, respectively, when the cutoff value was 0.388. We found that the risk model combining pericarotid adipose tissue attenuation value and lumen stenosis has significant predictive values for the presence of symptomatic stroke among patients with a moderate or higher degree of carotid stenosis.

In this paper, a novel electric arc electrochemical machining (EAECM) technology is proposed which is a combination of electric arc machining (EAM) and electrochemical machining (ECM). EAECM utilizes the combined thermal energy of EAM and electrochemical dissolution characteristics of ECM simultaneously to remove material and level surface without compromising efficiency and quality in the macro-machining of cemented alloys. A gap electric field model and a gas-liquid-solid three-phase flow model are established, the causes and hazards of secondary discharge in electric arc electrochemical machining are revealed in combination with experiment. The flushing efficiency for debris removal and electrochemical mass transfer rate are improved by optimizing flow field form and enhancing flushing pressure, so that the proposed hybrid machining can obtain smooth surfaces at higher machining speed. Finally, the process potential of electric arc electrochemical machining is demonstrated by milling patterns.

This study introduces novel electrode-assisted hydrodynamic arc-breaking methods for the efficient side-cutting of Ti2AlNb, a material widely used in the aerospace industry but notorious for its machining difficulties. The research aims to overcome the limitations of traditional arc machining techniques, with a particular emphasis on deep-sidewall machining. The paper begins with an overview of electrode manufacturing processes used in electrode-assisted hydrodynamic arc-breaking methods. Following that, Fluent simulations are used to analyze particle behavior during the side-cutting process. The feasibility of these novel electrodes is then demonstrated using extensive single-factor experiments. The optimization of process parameters using orthogonal experiments, which significantly improves machining efficiency, is a crucial aspect of this study. The results showed that electrode-assisted arc breaking and hydrodynamic arc breaking for Ti2AlNb side-cutting processing increased the material removal rate by 65 % compared to conventional electrode materials, reduced relative electrode wear rate by 54 %, and reduced surface roughness by 85 %. Therefore, electrode-assisted hydrodynamic arc-breaking methods for side-cutting processing have a promising future in difficult-to-machine materials in the aerospace industry.

Modern cognitive radars, armed with knowledge-aided waveforms, exhibit considerable effectiveness in detecting low-speed, small radar cross-section (RCS) targets, while demonstrating resilience in electronic countermeasure scenarios. In this article, we introduce a novel technique named particle filter-based recurrent waveform optimization (ALTERATION) to design unimodular waveforms with tailored range-Doppler profiles and low probability of intercept (LPI), thereby enhancing radar performance in complex environments. ALTERATION leverages the power of particle filters (PFs) in conjunction with iterative optimization methods. Our approach begins with linear frequency modulation (LFM) waveforms regularized by chaotic-phase terms serving as the initial particles. During each PF iteration, these particles are evolved via an alternating optimization method dovetailed with fast Fourier transform (FFT) operations. A specially proposed perturbation scheme corresponding to the deployed optimization method is then applied to weigh particles according to their auto-correlation levels, facilitating resampling to produce the new particle set for the next PF iteration. Moreover, ALTERATION is designed to readily integrate low-resolution phase quantization, thereby further mitigating hardware costs while maintaining near-optimal waveforms. Numerical comparisons illustrate that our ALTERATION approach outperforms existing state-of-the-art alternatives, indicating its potential for application in cognitive radar systems.

The use of an argon–water medium rather than air–water medium is proposed to increase the material removal rate (MRR) and decrease surface oxide production for short electric arc machining. To provide a more stable discharge state during high-temperature processing, this approach uses argon to reduce the production of highly resistant refractory materials (e.g. titanium oxide). For short-arc machining of Ti6Al4V titanium alloy, the effects of the voltage, milling depth, electrode speed and gas flow rate on the MRR, relative electrode loss rate and surface roughness were compared for air–water and argon–water mixtures. It was discovered that the milling depth and voltage had the greatest impact on the MRR, voltage and electrode rotational speed had the greatest impact on the relative tool electrode wear rate (REWR), and voltage had the greatest impact on the surface roughness. In contrast to the air–water mixture, the MRR with the argon–water working medium was as high as 9391 mm3/min, with a 13% improvement in MRR, as well as a smooth and flat surface of the machined workpiece. Nevertheless, the REWR increased to 2.46% and the surface roughness was at least 432 μm with the argon–water medium, increasing by 59 μm compared with the air–water medium. Moreover, the addition of argon reduced the types of surface compound on the workpiece and decreased the residual stress defects, including the recast layer, microcracks and holes.

The use of hand pose for dexterous manipulator teleoperation is an attractive method to the control of the multifingered manipulators. Furthermore, the advancement of the deep learning and depth sensors has encouraged the development of the 3-D hand pose estimation. However, developing a 3-D hand pose estimation method with an accurate and real-time performance is still a difficult task in computer vision. In this article, a lightweight depth-based network named the biomechanical structure information cascade network (BaSICNet) is proposed by considering the global and local structure of human hands to improve the performance through a cascade network and a bone-constraint loss function. In addition, the BaSICNet is applied to a five-fingered dexterous manipulator platform to achieve visual hand-based teleoperation. Extensive evaluations on two public data sets show that the BaSICNet can produce accurate and fast 3-D hand poses (9.15 and 7.59-mm mean errors on NYU and MSRA data sets with 114.7 fps), and can achieve superior 3-D hand pose estimation balance of accuracy and speed when compared with state-of-the-art (SOAT) methods. Experiment results on the dexterous manipulator platform also show that the BaSICNet can be applied well for teleoperation.

Image reconstruction remains a challenging task in image processing. In this letter, a compressive sensing total-variation primal-dual (CTPD) algorithm is developed for image reconstruction. The proposed CTPD algorithm involves minimization of a multi-term cost function with sparse and total-variation composite regularization. Instead of finding its optimal solution directly, this minimization problem is first cast into a convex-concave saddle point optimization problem, which can be solved by using a proximal primal-dual iteration algorithm with several simple sub-steps, each producing low complexity solvers. Moreover, in order to speed-up convergence, an accelerated version of the CTPD (A-CTPD) algorithm is also presented. In the A-CTPD algorithm, the primal-dual iteration is reformulated as a fixed-point iteration, which allows the use of an Anderson acceleration technique for faster convergence without sacrificing image reconstruction accuracy. Several numerical experiments demonstrate the efficiency of our proposed algorithms in comparison with other algorithms such as least squares (LS), the least absolute shrinkage and selection operator (LASSO), total variation (TV) image reconstruction, the alternating direction method of multipliers (ADMM), or fast iterative shrinkage-thresholding algorithm (FISTA).

In the presence of noise interference, direction of arrival (DOA) estimation becomes challenging. Particularly, when radar signals are entirely submerged in noise, the spatial information within the signals becomes obscured, thereby affecting the precision of DOA estimation. To address the issue, a symplectic geometry mode decomposition (SGMD) method based on Gaussian process space angle (GPSA) has been introduced to mitigate the impact of noise on target DOA estimation. This method operates within nonconstant curvature spaces, constructing subspaces for signals with different signal-to-noise ratios (SNRs). The subspace with the highest SNR is selected as the reference subspace. By quantifying the differences between the various signal subspaces and the reference subspace, a trajectory matrix that aligns with the dynamic characteristics of the signals is formed. Subsequently, the trajectory matrix is used to reconstruct the signals in the symplectic space, preserving the spatial information while reducing the disturbance caused by noise components on DOA estimation. Simulation and field experiments demonstrate the effectiveness of this approach in diminishing noise components within received signals, enhancing the precision of Music and Capon algorithms for DOA estimation under low SNR conditions.

In distributed coherent aperture radar (DCAR), the fundamental problem in ensuring the full coherence of the radar system is how to realize the precise positioning of the subarray. The subarray positioning scheme is designed based on the multi-domain extended extreme learning machine (ELM)-direction of arrival (DOA) estimation for the mobile DCAR system. The multi-domain partition scheme improves the ELM model, making the algorithm effective and low-complex in the changing electromagnetic environment. Firstly, the array covariance matrix is constrained by L2 regularization to decrease the influence of different feature measurement scales on the model. An airspace extension ELM (AEELM) method, which reduces the original matrix by the spatial partition layer and the sequential process, is proposed. This method diminishes the size of the model's solving matrix and enhances the DOA estimation accuracy while increasing or refining the samples. Secondly, an extended ELM method based on partition projection (PPEELM) is proposed to unravel the problems of high dimensions, small samples, and complex electromagnetic environments in radar array features. The radar covariance matrix is analyzed by partitioned multi-space projection according to the changes in the scene's electromagnetic environment. The operation can ameliorate the model's ability to solve nonlinear problems in complex environments. Therefore, the fully coherent system's position estimation accuracy and signal-to-noise ratio (SNR) gain are ameliorated. Numerical simulations show that AEELM can effectively augment the estimation accuracy and curtail the maximum matrix dimension of a single training. The PPEELM method can ensure the system coherence performance is more excellent than 95% when the subarray signal's SNR is greater than 0 dB in small scenarios.

Signal or noise power is an important performance parameter or indicator used for many applications in signal processing and wireless communications. This article investigates the power estimation of random signals on all sensor network nodes by using measurements taken over a subset of nodes, where the power values are assumed to be smooth on the network. This power-estimation problem is formulated as a regularized optimization problem involving the minimization of a sum of nonnegative log-likelihood and the total variation. To find its optimal solution, a primal–dual hybrid gradient (PDHG) algorithm is developed. Moreover, due to the strong demand for low hardware cost and bandwidth/power consumption in wireless communication systems, an extended algorithm, called low-resolution few-bit PDHG (LBit-PDHG), is also developed, which implements the power estimation with low-resolution few-bit quantized measurements. The LBit-PDHG algorithm could achieve competitive estimation performance when increasing the quantization resolution to 2 or 3 b while the energy consumption and implementation costs are still much lower in comparison to the high-resolution sampling implementations. Theoretical analyses prove the convergence of the proposed algorithms. Simulation studies and an application to acoustic source localization further validate the performance of the proposed algorithms.

Visual hand-based robot teleoperation provides a powerful guarantee for robots to complete complex tasks. However, detection and distinction of dual hands on images are difficult because of the small differences between left and right hands. To solve this problem, a parallel dual-hand detection and distinction method that combines the features of hands with the relationship features between the dual hands and body pose is proposed to achieve robust and accurate dual-hand detection. This parallel dual-hand detection method includes a hand detection module, a body pose estimation module, and a fusion module. In the hand detection module, a hand detector that realizes fast and accurate hand detection by detecting the center and corner points of hands is designed. In the body pose estimation module, a body pose estimator with dual-hand positions is proposed. The fusion module is designed to fuse hand detection and dual-hand estimation results to achieve distinction between left and right hands. Finally, the parallel dual-hand detection method is applied to a bimanual robot teleoperation system by using a designed dual-hand teleoperation framework. The proposed parallel dual-hand detection method can achieve 98.54% mAP of hand detection with 18 frames per second on a custom dual-hand detection dataset, and the bimanual robot teleoperation method can achieve 95.4% average accuracy for teleoperation tasks. Experimental results show the high accuracy and speed of our proposed parallel dual-hand detection method and its practicability in bimanual robot teleoperation.

Objectives:This study evaluates the cost–effectiveness of adding prolonged-release (PR)-fampridine to best supportive care (BSC) versus BSC alone in adult multiple sclerosis patients with walking disability in China. Materials & methods:A hybrid decision tree and Markov model from both the societal and healthcare perspectives were constructed. Parameters were derived from clinical trials of PR-fampridine, published sources and clinical expert interviews. Results:Over a 10-year time horizon, adding PR-fampridine to BSC led to 0.15 quality-adjusted life year (QALY) gain and lower costs, with incremental cost–effectiveness ratios of -238,806 Chinese Yuan/QALY and -113,488 Chinese Yuan/QALY from the societal and healthcare perspectives, respectively. Conclusion:Compared with BSC alone, PR-fampridine plus BSC is considered an economically dominant strategy for the treatment of multiple sclerosis-related walking disability in China.

Because of the complexity of cancer ecosystems, the efficacy of single-agent chemotherapy is limited. Herein, we report the use of cationic nanoparticles (designated PPCNs) generated from a chemically modified form of the chemotherapeutic agent podophyllotoxin (PPT) to deliver both microRNA-424 (miR-424) and PPT to tumor cells, thus combining chemotherapy and gene therapy. We evaluated the optimal loading ratio of miR-424─which targets programmed cell death ligand 1 (PD-L1) mRNA and reduces PD-L1 production, thus promoting the attack of tumor cells by T cells─for effective delivery of miR-424 and PPCNs into nonsmall-cell lung cancer cells (H460). Because miR-424 can reverse chemotherapy resistance, treatment of the tumor cells with the combination of miR-424 and PPT enhanced their sensitivity to PPT. Because miR-424 and the PPCNs regulated PD-L1 production in different ways, the miR-424@PPCN complexes were significantly more efficacious than either miR-424 or PPCNs alone. We also demonstrated that treatment of tumor-bearing mice with these complexes significantly inhibited tumor growth and extended survival. Moreover, additional in vitro experiments revealed that the complexes could remodel the tumor immune microenvironment, relieve immunosuppression, and achieve immune normalization. This novel system for delivering a combination of PPT and miR-424 shows great potential for the multimodal treatment of lung cancer.

Circulation planning for electric multiple units (EMUs) is regarded as one of the key operation issues for a high-speed railway transportation system. The EMU circulation plan consists of determining the connections of trains while accomplishing the passengers’ demands. EMUs need regular maintenance at a certain interval of kilometers or minutes for safety reasons. Consequently, the circulation plan must ensure that EMU trains can reach the maintenance depots in time for their required maintenance. This paper proposes a 0-1 integer programming model for the EMU circulation plan with the aim of minimizing the total costs of the mileage losses of the EMUs, which is incurred when they undergo a maintenance check before the corresponding travel mileage reaches the limit of the cycle. Given that the accumulated travel mileage of EMUs is allowed to be 10% above the standard mileage cycle in practice, an ingenious fuzzy maintenance constraint is presented to tackle the mileage cycle constraint with a certain elasticity. The exterior penalty function is employed to deal with the fuzzy constraint and a simulated annealing (SA) heuristic is employed to solve the model. The modeling and solving approach are applied to a practical instance in the context of China’s high-speed railway system. Compared with the average travel mileage of EMU trains using the manual solution, with the SA solution an increase of 293 km was observed. It can be then concluded that the optimization method presented in this paper can effectively improve the quality of the EMU circulation plan.

This research presents bending responses of hybrid laminated nanocomposite reinforced axisymmetric circular/annular plates (HLNRACP/ HLNRAAP) within the framework of non-polynomial under mechanical loading and various type of initially stresses via the three-dimensional elasticity theory. The current structure is on the Pasternak type of elastic foundation and torsional interaction. The state-space approach and differential quadrature method (SS-DQM) are studied to present the bending characteristics of the current structure by considering various boundary conditions. To predict the material properties of the bulk, the role of mixture and Halpin–Tsai equations are studied. For modeling the circular plate, a singular point is studied. Finally, a parametric study investigates the impacts of various types of distribution of laminated layers, stacking sequence on the stress/strain information of the HLNRACP/ HLNRAAP. Results reveal that the system's static stability and bending behavior improve due to increasing the value of Winkler and Pasternak factors, and the stress distribution becomes more uniform.

Low drug delivery efficiency elevates the cost of medication, lowers the therapeutic efficacy, and appears as a leading reason for unmet needs in anticancer therapies. Herein, we report the development of self-assembled podophyllotoxin-loaded lipid bilayer nanoparticles that inhibit the production of programmed cell death ligand 1 in lung cancer cells and promote tumor-specific immune responses, thus offering a strategy for regulating the immunosuppressive microenvironment of tumors. In addition, encapsulation of podophyllotoxin in the nanoparticles reduced its systemic toxicity, enhanced its penetration into tumors, and increased its antitumor efficacy. Systemic injection of the nanoparticles into tumor-bearing mice not only prevented tumor immune escape but also significantly inhibited tumor growth and extended survival. In general, the podophyllotoxin-loaded nanoparticles exhibited both immunological effects and antitumor effects in addition to having better targeting activity and fewer side effects than free podophyllotoxin. We expect our findings to facilitate the development of therapies for lung cancer.

The combination of photothermal therapy (PTT) and gene therapy (GT) shows great potential to achieve synergistic anti-tumor activity. However, the lack of a controlled release of genes from carriers remains a severe hindrance. Herein, peptide lipid (PL) and sucrose laurate (SL) were used to coat single-walled carbon nanotubes (SCNTs) and multi-walled carbon nanotubes (MCNTs) to form bifunctional delivery systems (denoted SCNT-PS and MCNT-PS, respectively) with excellent temperature-sensitivity and photothermal performance. CNT/siRNA suppressed tumor growth by silencing survivin expression while exhibiting photothermal effects under near-infrared (NIR) light. SCNT-PS/siRNA showed very high anti-tumor activity, resulting in the complete inhibition of some tumors. It was highly efficient for systemic delivery to tumor sites and to facilitate siRNA release owing to the phase transition of the temperature-sensitive lipids, due to PL and SL coating. Thus, SCNT-PS/siRNA is a promising anti-tumor nanocarrier for combined PTT and GT.

Silencing the inhibitor of apoptosis (IAP) by RNAi is a promising method for tumor therapy. One of the major challenges lies in how to sequentially overcome the system barriers in the course of the tumor targeting delivery, especially in the tumor accumulation and penetration. Herein we developed a novel stimuli-responsive polysaccharide enveloped liposome carrier, which was constructed by layer-by-layer depositing redox-sensitive amphiphilic chitosan (CS) and hyaluronic acid (HA) onto the liposome and then loading IAP inhibitor survivin-shRNA gene and permeation promoter hyaluronidase (HAase) sequentially. The as-prepared HA/HAase/CS/liposome/shRNA (HCLR) nanocarrier was verified to be stable in blood circulation due to the negative charged HA shield. The tumor targeting recognition and the enhanced tumor accumulation of HCLR were visualized by fluorescence resonance energy transfer (FRET) and in vivofluorescence biodistribution. The deshielding of HA and the protonizing of CS in slightly acidic tumor extracellular pH environment (pHe, 6.8–6.5) were demonstrated by ζ potential change from −23.1 to 29.9 mV. The pHe-responsive HAase release was confirmed in the tumor extracellular mimicking environments, and the intratumoral biodistribution showed that the tumor penetration of HCLR was improved. The cell uptake of HCLR in pHeenvironment was significantly enhanced compared with that in physiological pH environment. The increased shRNA release of HCLR was approved in 10 mM glutathione (GSH) and tumor cells. Surprisingly, HCLR suppressed the tumor growth markedly through survivin silencing and meanwhile maintained low toxicity to mice. This study indicates that the novel polysaccharide enveloped HCLR is promising in clinical translation, thanks to the stimuli-triggered tumor accumulation, tumor penetration, cell uptake, and intracellular gene release.

Objective Post-transplantation diabetes mellitus (PTDM) is a frequent complication after heart transplantation. We investigated the specific predictors of PTDM in Chinese heart transplant recipients and the prognostic value of these predictors.Methods We retrospectively analyzed 122 adult patients who underwent heart transplantation. Comparisons were made between patients with PTDM (n = 44) and those without PTDM (n = 78).Results During the median follow-up of 44 months, the cumulative incidence of PTDM was 19.7% at 1 year after transplantation and 36.1% at the endpoint. PTDM was associated with a significantly higher preoperative body mass index (BMI) (odds ratio [OR] = 1.349), fasting plasma glucose (FPG) concentration (OR = 2.538), and serum uric acid concentration (OR = 1.005) after transplantation. The area under the receiver operating characteristic curve was 0.708 and 0.763 for the BMI and FPG concentration, respectively. The incidence of acute rejection and infection were higher and the all-cause mortality rate was considerably greater in patients with than without PTDM.Conclusions A higher preoperative BMI (>23 kg/m2), FPG concentration (>5.2 mmol/L), and uric acid concentration could potentially predict PTDM in Chinese heart transplant recipients. PTDM influences long-term survival after heart transplantation.

Metastable molybdenum carbide (α-MoC), as a catalyst and an excellent support for metal catalysts, has been widely used in thermo/electro-catalytic reactions. However, the selective synthesis of α-MoC remains a great challenge. Herein, a simple one-pot synthetic strategy for the selective preparation of metastable α-MoC is proposed by electrochemical co-reduction of CO2and MoO3in a low-temperature eutectic molten carbonate. The synthesized α-MoC shows a reed flower-like morphology. By controlling the electrolysis time and monitoring the phase and morphology of the obtained products, the growth process of α-MoC is revealed, where the carbon matrix is deposited first followed by the growth of α-MoC from the carbon matrix. Moreover, by analyzing the composition of the electrolytic products, the formation mechanism for α-MoC is proposed. In addition, through this one-pot synthetic strategy, S-doped α-MoC is successfully synthesized. Density functional theory (DFT) calculations reveal that S doping enhanced the HER performance of α-MoC by facilitating water absorption and dissociation and weakening the bond energy of Mo-H to accelerate H desorption. The present work not only highlights the valuable utilization of CO2but also offers a new perspective on the design and controllable synthesis of metal carbides and their derivatives.

Light-chain amyloidosis and transthyretin-related amyloidosis (wild-type and mutated) are three main types of systemic amyloidosis associated with a clinically relevant cardiac involvement. In this study, we compared prognosis in patients with different categories of cardiac amyloidosis using meta-analysis and present a systematic review.

The current study sought to determine the prevalence and correlates of depressive symptoms among older adults with physical functional limitations in China. A total of 2,410 older adults (age ≥60 years) were enrolled. Depressive symptoms and physical function were assessed with the Patient Health Questionnaire-9 (PHQ-9) and Barthel Index of Activities of Daily Living, respectively. Univariate logistic regression and stepwise multiple logistic regression analyses were performed. The mean score for depressive symptoms was 5.45 (The current study sought to determine the prevalence and correlates of depressive symptoms among older adults with physical functional limitations in China. A total of 2,410 older adults (age ≥60 years) were enrolled. Depressive symptoms and physical function were assessed with the Patient Health Questionnaire-9 (PHQ-9) and Barthel Index of Activities of Daily Living, respectively. Univariate logistic regression and stepwise multiple logistic regression analyses were performed. The mean score for depressive symptoms was 5.45 (SD= 5.49), with a prevalence of 27% (95% confidence interval [25.2, 28.8]). Risk factors for depressive symptoms included female gender, rural residence, being a farmer before age 60, history of chronic disease, self-reported poor health status, complete dependence, stressful life events over the past 3 months, insufficient emotional and material support, and low social participation over the past 1 month and feeling depressed about it. However, individuals older than 80 with high educational levels and hobbies showed a relatively low prevalence of depressive symptoms compared to their counterparts. This study confirms the necessity to screen for depressive symptoms in older adults, particularly those with physical functional limitations. The information can be used to identify and reduce related risk factors of depressive symptoms for vulnerable older adults.[Res Gerontol Nurs. 2019; 12(3):133–146.]

It is of great importance but full of challenge to construct intelligent materials with self-powered, durable and high fire-safety to meet the requirements of the intelligent fire protection towards Internet of Things (IoT). In this work, phosphorous-containing ionic liquid (PIL)-based ionogels (PILGs) were fabricated with acrylamide, low melting point polyanionic polystyrene sulfonate sodium and polycationic polydiallyl dimethyl ammonium chloride via ultraviolet initiated polymerization. PILGs showed good ionic conductivity (1.33 S·m−1) and high ionic thermoelectric effect (1.31 mV·K−1), which endowed flammable materials with accurate temperature-sensing, sensitive fire-warning performance and outstanding flame retardancy. When being burned, PILGs triggered the fire-warning system at about 1 s and rapidly self-extinguished. Besides, due to the good thermostability and low volatility of PIL, PILGs displayed high durability in extreme environments. More importantly, PILGs were designed as thermoelectric capacitors and high output power galvanic cells, which could power fire-warning circuits and electronics. Our work provides new insights for the development and application of self-powered intelligent fire protection materials.

The contribution of wind power to the availability of generating capacity becomes more important with increasing wind penetration. This paper presents a multi-objective evaluation of the acceptable capacity of wind power. A partitioning method which can adaptively divide the forecast power range into various regions according to its historical data is firstly proposed. Then, the multi-objective optimisation model of power system integrated with wind is constructed with objectives of minimum generating cost, minimum pollutant emission and maximum wind power acceptable capacity. By amending the NDX (normal distribution crossover) operator into the binary crossover operator of NSGA2, the improved NSGA2 (named INSGA2) is tested by a benchmark for typical high-dimensional functions, and the simulation results confirmed the feasibility of the developed algorithm. Finally, a case study of IEEE 118 bus system is carried out to verify that the approach is beneficial to deal with the problems of evaluation of stochastic wind acceptance.

As effective non-viral vectors of gene therapy, cationic lipids still have the problem of toxicity, which has become one of the main bottlenecks for their applications. The toxicity of cationic lipids is strongly connected to the headgroup structures. In this article, we studied the cytotoxicity of two cationic lipids with a quaternary ammonium headgroup (CDA14) and a tri-peptide headgroup (CDO14), respectively, and with the same linker bond and hydrophobic domain. The IC50values of CDA14 and CDO14 against NCI-H460 cells were 109.4 μg mL−1and 340.5 μg mL−1, respectively. To determine the effects of headgroup structures of cationic lipids on cytotoxicity, apoptosis related pathways were investigated. As the lipids with a quaternary ammonium headgroup could induce more apoptotic cells than the ones with a peptide headgroup, the enzymatic activity of caspase-9 and caspase-3 increased obviously, whereas the mitochondrial membrane potential (MMP) decreased. At the same time, the reactive oxygen species (ROS) levels also increased and the cell cycle was arrested at the S phase. The results showed that the toxicity of the cationic lipid had a close relationship with its headgroup structures, and the cytotoxic mechanism was mainly viathe caspase activation dependent signaling pathway and mitochondrial dysfunction. Through this study, we hope to provide the scientific basis for exploiting safer and more efficient cationic lipids for gene delivery.

Chenopodiaceae is one of the most important families in arid and saline environments. Several studies have observed the mycorrhizal structure in Chenopodiaceae plants (i.e., chenopods), but the mycorrhizal colonization status of chenopods in saline habitats and the influencing factors are still not well understood. The mycorrhizal colonization of twenty chenopod species in three different saline habitats (a saline alkaline meadow in the Songnen Plain of northeastern China, a saline desert in the Junggar Basin of northwestern China, and a saline alpine meadow in the Tibetan Plateau of western China) and the chenopod-associated environmental factors (including soil moisture, soil available phosphorous (P) concentration, pH, and salt content) were analyzed. Our results showed that approximately 60% of the studied chenopods were colonized by arbuscular mycorrhizal (AM) fungi with a colonization percentage ranging from 5% to 33%. Structural analysis of mycorrhizal association indicated that vesicles were quite common, while arbuscules and hyphal coils were relatively rare. In addition, a positive correlation between mycorrhizal colonization rate and soil electrical conductivity (r=0.920, P<0.01) and two negative correlations of mycorrhizal colonization rates with soil moisture (r=–0.818, P<0.01) and the soil available P concentration (r=–0.876, P<0.01) confirmed that mycorrhizal colonization rate in the roots of chenopods was environment-dependent.

The oxygen reduction reaction (ORR), an important process in Zn-air batteries (ZABs), shows sluggish reaction kinetics, which significantly impairs the further improvement of battery performance. Thus, rationally designing cathodic catalysts for ZABs has drawn sufficient attention. We herein synthesize and characterize Fe/N/F-tridoped CNTs (FeNFCs) by annealing the postsynthesized trifluoroacetic anhydride-modified Fe-MIL-88B-NH2nanocrystals with melamine at high temperature in a N2atmosphere. Benefiting from the Fe/N/F element doping, high specific surface area, and CNT structure, the FeNFC800 catalyst prepared at 800 °C exhibits a preferable half-wave potential of 0.829 V vs. RHE. The Zn-air battery equipped with FeNFC800 shows a high open-circuit voltage of 1.47 V, a gratifying peak power density of 196 mW/cm2, and extraordinary long-term stability, outperforming the benchmark 20% Pt/C.

The Fano line shape at about 185 cm−1is observed in temperature-dependent Raman spectra of BaTiO3nanoparticles. The dip structure appears at the low-energy side, indicating a positive asymmetric parameter q, which is assigned to the interference between a central peak from the quasi-elastic scattering mode and the A(TO) vibrational mode around 180 cm−1. Furthermore, the relation between the phase change of BaTiO3nanoparticles and the Fano line shape is discussed. This work deepens the understanding of the mechanism underlying the Fano line shape in Raman spectral and provides a powerful strategy to characterize phase change of the nanostructured BaTiO3.