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The present study was undertaken to understand the effect of annealing on the microstructural features of the melt-pool structure and the associated multiscale mechanical properties of the Al–2.5%Fe–2%Cu alloy manufactured by laser powder bed fusion (LPBF). Microstructural characterizations and tensile tests were conducted for the LPBF-built specimen and those subsequently annealed at various temperatures ranging from 200 to 500 °C. Nanoindentation hardness mapping was used to evaluate the mechanical inhomogeneity of the melt-pool structure and its changes by annealing at different temperatures. The LPBF-manufactured sample exhibited a melt-pool structure containing numerous particles of the Al23CuFe4 phase (oC28, orthorhombic structure) formed because of local melting and rapid solidification during the LPBF process. The relatively coarsened cellular structure localized along the melt-pool boundary resulted in local soft regions. The local vulnerability contributed to the direction dependence of the tensile ductility. A slight variation was observed in the inhomogeneous microstructure of the samples annealed at 200 or 300 °C. The formation of numerous Al23CuFe4 nanoprecipitates in the α-Al supersaturated solid solution prevented strength loss after post-heat treatments. In addition, considerable coarsening of the intermetallic phase after annealing at 500 °C eliminated the melt-pool structure. The tensile performance of the specimens demonstrated a ductile fracture mode, wherein ductile fracture occurred in the α-Al matrix with low hardness while the harder θ-Al13Fe4 stable phase was embedded within it. The anisotropy in the mechanical properties was less pronounced owing to the significant microstructural changes.

AbstractMedical security support for rehabilitation therapy in China is different from that in other countries. We investigated whether the discharge plan to continue rehabilitation therapy in tertiary hospitals for patients after traumatic spinal cord injury (TSCI) was influenced by payment sources or other conditions. This was a cross-sectional, observational study. Information was collected on the general condition, caregiver, types of payment sources for continued rehabilitation, American Spinal Injury Association Impairment Scale (AIS) scores, and discharge plans. In total, 135 patients with TSCI (107 male, mean age 41.00 ± 13.73 years, mean spinal cord injury duration 238.43 ± 345.54 days) were enrolled. Medical insurance (43%) and out-of-pocket payments (27.4%) were the primary payment sources. Although most patients were beyond the acute phase, 40% continued rehabilitation therapy at other tertiary hospitals. The caregiver, payment sources, injury level, AIS level, and complete urinary tract infection (UTI) were different due to discharge plans (p > .05). Patients seemingly consider a higher AIS level and co-UTI as the requirement for tertiary hospital therapy. In non-medical insurance payment source patients, the discharge plan also differed due to the AIS level and co-UTI (p > .05). However, in medical insurance patients, the discharge plan differed only in terms of TSCI duration (p > .05). The restricted duration of medical coverage restricted the continuation of rehabilitation therapy and influenced the discharge plan of most patients with TSCI.

In this article, a game theoretic non‐cooperative dynamic target tracking algorithm that empowers defensive unmanned aerial vehicles (UAVs), with directional sensing capabilities to track and collect information on intrusive UAVs, is proposed. Specifically, defenders aim to maximize the collection of identity information from intruders possessing anti‐tracking and evading capabilities, while simultaneously preventing their entry into protected areas. Game theory is employed to determine the optimal confrontation paths for defenders against the intruders. The probability perception model is utilized for evaluating the dynamic target tracking capability and designing a tracking merit function to assess tracking performance, taking into account both the target's position and the perception relative angle. Furthermore, considering the dynamic interactive behaviors between intruders and defenders, the iterative linear quadratic game (ILQG) algorithm is employed to solve the Nash equilibrium of the non‐cooperative target tracking game. Through simulation experiments, the effectiveness of the proposed algorithm in accomplishing multi‐agent dynamic target tracking tasks is demonstrated and the performance of the algorithm under varying parameters in the intruder's cost function is evaluated, which represent different intrusion intentions.

Abstract Myocardial ischemia/reperfusion injury (MIRI) is the leading cause of irreversible myocardial damage. A pivotal pathogenic factor is ischemia/reperfusion (I/R)‐induced cardiomyocyte ferroptosis, marked by iron overload and lipid peroxidation. However, the impact of lipid droplet (LD) changes on I/R‐induced cardiomyocyte ferroptosis is unclear. In this study, an aggregation‐induced emission probe, TPABTBP is developed that is used for imaging dynamic changes in LD during myocardial I/R‐induced ferroptosis. TPABTBP exhibits excellent LD‐specificity, superior capability for monitoring lipophagy, and remarkable photostability. Molecular dynamics (MD) simulation and super‐resolution fluorescence imaging demonstrate that the TPABTBP is specifically localized to the phospholipid monolayer membrane of LDs. Imaging LDs in cardiomyocytes and myocardial tissue in model mice with MIRI reveals that the LD accumulation level increase in the early reperfusion stage (0–9 h) but decrease in the late reperfusion stage (>24 h) via lipophagy. The inhibition of LD breakdown significantly reduces the lipid peroxidation level in cardiomyocytes. Furthermore, it is demonstrated that chloroquine (CQ), an FDA‐approved autophagy modulator, can inhibit ferroptosis, thereby attenuating MIRI in mice. This study describes the dynamic changes in LD during myocardial ischemia injury and suggests a potential therapeutic target for early MIRI intervention.

A theoretical approach is presented to significantly enhance the spectral sensitivity of prism-based SPR sensors. The spectral sensitivity of prism-based SPR sensors is derived based on the coupling conditions of SPR and might exceed 105 nm/RIU for analytes with large RI values when other sensor parameters are carefully considered, including the RI of the prism, the angle of incidence, and the SPR active material. The spectral sensitivity could be markedly enhanced, reaching up to 10,000 nm/RIU by fine-tuning the effective RI of the incident light to be slightly larger, specifically 0.01~0.02 RIU, than the RI of the analyte, which is attributed to the large dielectric permittivity of the SPR active material, the key factor for achieving high sensitivity. The dynamic range is 0.040 RIU in the case of high sensitivity, which is sufficient in most applications. Moreover, the spectral sensitivity could be pushed even higher, into the range of 106~108 nm/RIU, by positioning the effective RI of the incident light closer to that of the analyte. However, it requires a careful balance between optimizing the sensitivity and maintaining an acceptable dynamic range.

Spatial scale is a key factor, which affects the accuracy of spatial expression and further influences the spatial planning of a research area. In order to help improve the efficiency and accuracy of optimal scale selection for all sizes of research areas, a universal two-layer theoretical framework for optimal scale selection was proposed in this paper. Port area was taken as an example to systematically clarify the application of the proposed framework, and the scale selection model for port spatial expression was established. Least-squares-based mean change point analysis was introduced into the model, and the concept of a comprehensive change point was proposed to form the criterion for optimal port scale selection. First, an appropriate scale domain was preliminarily determined by the upper scale selection model. Then, the lower scale selection model determined the final appropriate scale domain and took the corresponding scale of a minimum comprehensive change point as the final optimal scale for port spatial expression. Finally, a port area in Qingdao in eastern China was taken to verify the feasibility of the proposed model, and the optimal scale was suggested to be 14 m. The proposed framework in this paper helps ensure the accuracy of spatial expression and reduce spatial data redundancy, and it can provide the methodological references for planners to better spatialize a research area, which will guarantee the subsequent spatial planning work.

BackgroundNeutrophil-to-high-density lipoprotein cholesterol ratio (NHR), monocyte-to-high-density lipoprotein cholesterol ratio (MHR), lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), platelet-to-high-density lipoprotein cholesterol ratio (PHR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI) have been identified as immune-inflammatory biomarkers associated with the prognosis of cardiovascular diseases. However, the relationship of these biomarkers with the prognosis of myocardial infarction with non-obstructive coronary arteries (MINOCA) remains unclear.MethodPatients with MINOCA who underwent coronary angiography at the 920th Hospital of Joint Logistics Support Force were included in our study. Clinical baseline characteristics and laboratory testing data were collected from the hospital record system. The patients were divided into two groups on the basis of major adverse cardiovascular events (MACE) occurrence. Multiple logistic regression analysis was conducted to assess the relationship between NHR, MHR, LHR, PHR, SII, SIRI, AISI, and MACE. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive value of NHR, MHR, LHR, PHR, SII, SIRI, and AISI for MACE in patients with MINOCA. The accuracy of the prediction was indicated by the area under the curve (AUC) value.ResultsThe study included 335 patients with MINOCA. (81 in the MACE group and 254 in the No-MACE group). The MACE group had higher levels of NHR, MHR, LHR, PHR, SII, SIRI, and AISI than the No-MACE group. Multiple logistic regression analysis adjusted for confounding factors indicated that the higher levels of NHR, MHR, PHR, SII, SIRI, and AISI were associated with the occurrence of MACE in patients with MINOCA (P

Timely and accurate detection of diseases in vegetables is crucial for effective management and mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools for automated disease detection in crops due to their ability to learn intricate patterns from large-scale image datasets and make predictions of samples that are given. The use of CNN algorithms for disease detection in important vegetable crops like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, and cauliflower is critically examined in this review paper. This review examines the most recent state-of-the-art techniques, datasets, and difficulties related to these crops’ CNN-based disease detection systems. Firstly, we present a summary of CNN architecture and its applicability to classify tasks based on images. Subsequently, we explore CNN applications in the identification of diseases in vegetable crops, emphasizing relevant research, datasets, and performance measures. Also, the benefits and drawbacks of CNN-based methods, covering problems with computational complexity, model generalization, and dataset size, are discussed. This review concludes by highlighting the revolutionary potential of CNN algorithms in transforming crop disease diagnosis and management strategies. Finally, this study provides insights into the current limitations regarding the usage of computer algorithms in the field of vegetable disease detection.

The flexibility of handling equipment in dry bulk ports is poor, and frequent equipment fault induced by the high-load and high-power working conditions greatly impacts the overall port handling operations, making accurate fault detection play an important role in improving the efficiency and stability of dry bulk port operations. However, as we know, most fault detection methods for port handling equipment depend heavily on monitoring sensor data, which is not applicable in the dry bulk port due to high configuration and maintenance cost, as well as the high false alarm rate of monitoring sensors caused by strong background noise. To solve the problem, this study proposes a High-Level Feature Fusion Deep Learning Model, which uses different deep learning sub-models to extract features of structured and unstructured data. It fuses the extracted feature vectors to achieve fault detection in the handling equipment, establishing the mapping relationship between the fault (e.g., waiting for the pre-loading process, equipment fault, and others) and multi-source heterogeneous operation and maintenance data for the handling equipment, including reclaimers, belt conveyors, dumpers, and ship loaders. To verify the effectiveness of the proposed method, the actual data of a coal port in Northern China is employed as an example. The results show the deep learning model can achieve high prediction accuracy (over 86%) with high efficiency (0.5 s for each sample), which provides decision support for the fault detection in dry bulk port handling equipment.

In this study, the hydrophobic modification of coal gasification fly ash (FA) was investigated given the adverse effects of surface hydrophilic structures on the material field. The surface of FA was modified using stearic acid (SA), which successfully altered its hydrophilic structure. When the contact angle of S-FA increased from 23.4° to 127.2°, the activation index increased from 0 to 0.98, the oil absorption decreased from 0.564 g/g to 0.510 g/g, and the BET-specific surface area decreased from 13.973 m2/g to 3.218 m2/g. The failure temperature of SA on the surface of S-FA was 210 °C. The adsorption mechanism of FA was analyzed using density functional theory (DFT) and molecular dynamics (MD). The adsorption of water molecules by FA involved both chemical and physical adsorption, with active adsorption sites for Al, Fe, and Si. The adsorbed water molecules on the surface of FA formed hydrogen bonds with a bond length of 1.5–2.5 Å, leading to agglomeration. In addition, the long alkyl chain in SA mainly relied on the central carbon atom in the (-CH3) structure to obtain electrons in different directions from the H atoms in space, increasing the Coulomb repulsion with the O atoms in the water molecule and thereby achieving the hydrophobic effect. In the temperature range of 298 K to 358 K, the combination of FA and SA became stronger as the temperature increased.

Recently, an affine-projection-like M-estimate (APLM) algorithm has gained popularity for its ability to effectively handle impulsive background disturbances. Nevertheless, the APLM algorithm’s performance is negatively affected by steady-state misalignment. To address this issue while maintaining equivalent computational complexity, a robust cost function based on the Andrew’s sine estimator (ASE) is introduced and a corresponding affine-projection Andrew’s sine estimator (APASE) algorithm is proposed in this paper. To further enhance the tracking capability and accelerate the convergence rate, we develop the combined-step-size APASE (CSS-APASE) algorithm using a combination of two different step sizes. A series of simulation studies are conducted in system identification and echo cancellation scenarios, which confirms that the proposed algorithms can attain reduced misalignment compared to other currently available algorithms in cases of impulsive noise. Meanwhile, we also establish a bound on the learning rate to ensure the stability of the proposed algorithms.

As important nodes of the global coal supply chain, coal export terminals bear the tasks of coal storage, processing, and handling, whose efficiency and stability are of great importance with the growing coal shipping market in recent years. However, poor working conditions of the handling equipment in the coal export terminal, together with its relatively fixed layout and poor flexibility, allow frequent equipment failures to seriously affect the ship-loading operations. To solve the problem, this paper constructs a scheduling optimization model for ship-loading operation systems considering equipment maintenance and proposes an opportunity-maintenance-based two-layer algorithm to solve the model. The upper layer aims to optimize the scheduling scheme of the ship-loading operation system under a certain maintenance plan. The lower layer of the algorithm, an opportunity-maintenance-based “equipment-level–flow-level” maintenance optimization method, determines the best equipment maintenance plan. A coal export terminal in China is employed as the case study to verify the effectiveness of the proposed method. The results show that the proposed method can reduce the average dwell time of ships at the terminal by 15.8% and save total scheduling and maintenance costs by 10.3%. This paper shows how to make full use of equipment failure historical data and integrate equipment maintenance schemes into the scheduling problem of the ship-loading operation system, which can effectively reduce the impact of equipment failures on ship-loading operations and provide decision support for coal export terminal management.

Measurement-device-independent quantum key distribution (MDI-QKD) closes all the security loopholes in the detection system and is a promising solution for secret key sharing. Polarization encoding is the most common QKD encoding scheme, as it is straightforward to prepare and measure. However, implementing polarization encoding in MDI QKD imposes extra challenges, as polarization alignment must be maintained over both mutually unbiased bases and be maintained for both paths (Alice-Charlie and Bob-Charlie). Polarization alignment is usually done by interrupting the QKD process (reducing overall key generation rates) or using additional classical laser sources multiplexed with quantum channels for polarization alignment. Since low key rates and cost are the two most pressing challenges preventing wide adoption of QKD systems, using additional resources or reducing key rates runs contrary to making QKD commercially viable. Therefore, we propose and implement a novel polarization compensation scheme in the MDI-QKD system that avoids the aforementioned drawbacks by recycling part of discarded detection events. Our scheme evaluates the polarization drift in real-time based on single measurements corresponding to decoy intensities. Our fully automated experimental demonstration maintains the average polarization drift below 0.13 rad over 40 km of spooled fibre (without an insulating jacket) for at least four hours. The average quantum bit error rate is 3.8$\%$, and we achieved an average key rate of $7.45\times 10^{-6}$ bits per pulse.

Background Right ventricular longitudinal shortening fraction (RVLSF) is a 2‐dimensional speckle tracking echocardiography parameter based on tricuspid annular displacement analysis that could be used to assess right ventricular (RV) systolic function. The value of RVLSF in the assessment of RV systolic function in recipients of heart transplantation (HT) and whether RVLSF can replace strain parameters remains unknown. Methods and Results A total of 153 adult patients who underwent HT were consecutively enrolled in this prospective longitudinal study. All subjects were examined by conventional transthoracic 2‐dimensional echocardiography and 2‐dimensional speckle tracking echocardiography to evaluate the RV end‐diastolic basal diameter, RV end‐diastolic area, fractional area change, peak systolic velocity of tricuspid annulus, tricuspid annular plane systolic excursion, RV free wall strain, and RVLSF. Cox proportional hazards regression was used to test if the parameters of interest had independent prognostic value for adverse outcome prediction in patients who underwent HT. A significant positive correlation was found between the measurements of RVLSF and RV free wall strain (r=0.927, P

Abstract Objective Our study was conducted aimed at investigating the potential correlation between cerebral microangiopathy and autonomic nervous dysfunction. Methods We initially included 164 hospitalized patients with cerebral microangiopathy at our hospital from November 2019 to January 2021. Based on the inclusion and exclusion criteria, a final total of 162 patients with cerebral microangiopathy were selected. According to the patient's Autonomic Symptom Profile (ASP) score, patients with a score greater than 22 were categorized into a group with concomitant autonomic dysfunction (71 cases, combined group), while those with a score below 22 were categorized into a group of isolated cerebral microangiopathy (83 cases, cerebral microangiopathy group). The general data and laboratory examination results of the two groups were analyzed, and Pearson correlation analysis was performed to evaluate the correlation between cerebral microangiopathy and autonomic dysfunction, as well as the influencing factors of cerebral microangiopathy patients combined with autonomic dysfunction. Results There were no significant differences between the two groups in terms of sex, BMI, smoking, drinking, family dementia history, diabetes, hypothyroidism, carotid atherosclerosis, obstructive sleep apnea hypopnea syndrome, hyperuricemia, hyperlipidemia, chronic obstructive pulmonary disease, Hamilton Anxiety Scale score, Hamilton Depression Scale score, 24‐h mean systolic blood pressure (SBP), 24‐h mean diastolic blood pressure DBP, daytime mean systolic blood pressure (dSBP), daytime mean diastolic blood pressure, nighttime mean systolic blood pressure (nSBP), nighttime mean diastolic blood pressure, 24‐h systolic blood pressure standard deviation (SBPSD), 24‐h diastolic blood pressure standard deviation, daytime diastolic blood pressure standard deviation, nighttime diastolic blood pressure standard deviation (nDBPSD), nDBPSD (p > .05). However, significant differences were observed between the two groups regarding age, history of coronary heart disease, hypertension, leukoaraiosis, cognitive function, ASP score, SSR, 24‐h SBPSD, daytime systolic blood pressure standard deviation (dSBPSD), nighttime systolic blood pressure standard deviation (nSBPSD), standard deviation of RR interval (SDNN), root mean square value of successive RR interval difference (RMSSD), high‐frequency component (HF), and low‐frequency component (LF) (p .05), but there were significant differences in blood uric acid and homocysteine (Hcy) levels (p

Laser-beam powder bed fusion (PBF-LB) technique was used to produce an Al–2.5 %Fe–2 %Cu ternary alloy, featuring a two-phase eutectic composition of α-Al/Al23CuFe4 in non-equilibrium solidification, as determined by thermodynamic calculations. The specimen manufactured by PBF-LB exhibited a high tensile strength exceeding 350 MPa and a low thermal conductivity of approximately 140 W m−1 K−1. Subsequent annealing at 300 °C improved the thermal conductivity to 175 W m−1 K−1 without compromising the strength. This improvement was attributable to forming numerous Al23CuFe4 nanoprecipitates, which consumed solute elements. By appropriately managing the factors contributing to strengthening, a superior strength–conductivity balance can be achieved by implementing post-heat treatments.

To characterize volatile organic compounds (VOCs) and aromatic sensory properties in Yun cigar, 27 samples from four origins were analyzed using SPME–HS–GC/MS and sensory analysis. The investigation results were analyzed using principal component analysis (PCA), Fisher linear discriminant analysis (LDA), and Pearson correlation analysis. In Yunnan cigars, the content of nicotine and neophytadiene accounted for over 90% of the total VOC content. Nicotine was significantly positively correlated with neophytadiene and phytol. The cigars from four origins were clearly classified by the PCA of VOCs. Four region discrimination functions were established through the LDA of 14 compounds, and the validation accuracy was 100%. The sensory descriptors with the highest geometric mean were woody, roasted, fresh-sweet, bean, and scorched. Acetophenone, megastigmatrienone A, and thunbergene were positively correlated with multiple aroma descriptors, while nicotine was negatively correlated with multiple aroma descriptors.

Heart transplantation (HT) is the mainstream therapy for end-stage heart disease. However, the cardiac graft function can be affected by several factors. It is important to monitor HT patients for signs of graft dysfunction. Transthoracic echocardiography is a simple, first-line, and non-invasive method for the assessment of cardiac function. The emerging speckle-tracking echocardiography (STE) could quickly and easily provide additive information over traditional echocardiography. STE longitudinal deformation parameters are markers of early impairment of ventricular function. Although once called the “forgotten ventricle”, right ventricular (RV) assessment has gained attention in recent years. This review highlights the potentially favorable role of STE in assessing RV systolic function in clinically well HT patients.

Mine tailings have shown viability as the fine–grained layer in a capillary barrier structure for controlling acid mine drainage in a circular economy. Their saturated hydraulic conductivities (ksat) under wetting–drying cycles and freeze–thaw cycles remain unexplored. In this study, modified tailings with a weight ratio of 95:5 (tailings/hydrodesulfurization (HDS) clay from waste–water treatment) and an initial water content of 12% were used. The ksat of specimens was measured after up to 15 wetting–drying cycles, each lasting 24 h, with a drying temperature of 105 °C. The ksat for wetting–drying cycles decreased from 3.9 × 10−6 m/s to 9.5 × 10−7 m/s in the first three cycles and then stabilized in the subsequent wetting–drying cycles (i.e., 5.7 × 10−7 m/s–6.3 × 10−7 m/s). Increased fine particles due to particle breakage are the primary mechanism for the ksat trend. In addition, the migration of fines and their preferential deposition near the pore throat area may also promote this decreasing trend through the shrinking and potentially clogging–up of pore throats. This could be explained by the movement of the meniscus, increased salinity, and, subsequently, the shrinkage of the electrical diffuse layer during the drying cycle. Similar specimens were tested to measure ksat under up to 15 freeze–thaw cycles with temperatures circling between −20 °C and 20 °C at 12 h intervals. Compared to the untreated specimen (i.e., 3.8 × 10−6 m/s), the ksat after three freeze–thaw cycles decreased by 77.6% (i.e., 8.5 × 10−7 m/s) and then remained almost unchanged (i.e., 5.6 × 10−7 m/s–8.9 × 10−7 m/s) in subsequent freeze–thaw cycles. The increased fine grain content (i.e., 3.1%) can be used to explain the decreased ksat trend. Moreover, the migration of fines toward the pore throat area, driven by the advancing and receding of ice lens fronts and subsequent deposition at the pore throat, may also contribute to this trend.

With the increase in the use of high-frequency power electronic devices, the harmonics injected into the power grid show a trend of high-frequency development. The continuous rise of the supraharmonic emission level in the distribution network has become one of the power quality problems that needs to be solved urgently in the power grid. In this paper, an algorithm based on the Interpolation of the Self-convolutional Window All-phase Compressive Sampling Matching Pursuit (ISWApCoSaMP) is proposed. Firstly, the self-convolution operation is used for the maximum sidelobe decay (MSD) window, and then the compressed sampling matching pursuit model based on the All-phase is constructed, leading to the All-phase Compressive Sampling Matching Pursuit (ApCoSaMP). Finally, the four-spectrum-line interpolation is combined to utilize spectrum line information to improve the accuracy of signal parameter detection in the frequency domain. The introduced All-phase greatly improves the phase measurement accuracy because the initial phase of the supraharmonic signal is selected for phase estimation. In addition, the self-convolutional window and four-spectrum-line interpolation make full use of the information in the time and frequency domains, thus optimizing the measurement results of amplitude and frequency. The algorithm achieves high accuracy in the measurement results of simulated signals and accurately measures supraharmonics.

Red mud (RM), a bauxite residue, contains hazardous radioactive wastes and alkaline material and poses severe surface water and groundwater contamination risks, necessitating recycling. Pretreated RM can be used to make adsorbents for water treatment. However, its performance is affected by many factors, resulting in a nonlinear correlation and coupling relationship. This study aimed to identify the best formula for an RM adsorbent using a mathematical model that examines the relationship between 11 formulation types (e.g., pore-assisting agent, component modifier, and external binder) and 9 properties (e.g., specific surface area, wetting angle, and Zeta potential). This model was built using a back-propagation neural network (BP) based on single-factor experimental data and orthogonal experimental data. The model trained and predicted the established network structure to obtain the optimal adsorbent formula. The RM particle adsorbents had a pH of 10.16, specific surface area (BET) of 48.92 m2·g−1, pore volume of 2.10 cm3·g−1, compressive strength (ST) of 1.12 KPa, and 24 h immersion pulverization rate (ηm) of 3.72%. In the removal of total phosphorus in flotation tailings backwater, it exhibited a good adsorption capacity (Q) and total phosphorous removal rate (η) of 48.63 mg·g−1 and 95.13%, respectively.

With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

Chelating agents are commonly employed in microelectronic processes to prevent metal ion contamination. The ligand fragments of a chelating agent largely determine its binding strength to metal ions. Identification of ligands with suitable characteristics will facilitate the design of chelating agents to enhance the capture and removal of metal ions from the substrate in microelectronic processes. This study employed quantum chemical calculations to simulate the binding process between eleven ligands and the hydrated forms of Ni2+, Cu2+, Al3+, and Fe3+ ions. The binding strength between the metal ions and ligands was quantified using binding energy and binding enthalpy. Additionally, we explored the binding interaction mechanisms and explained the differences in binding abilities of the eleven ligands using frontier molecular orbitals, nucleophilic indexes, electrostatic potentials, and energy decomposition calculations based on molecular force fields. Based on our computational results, promising chelating agent structures are proposed, aiming to guide the design of new chelating agents to address metal ion contamination issues in integrated circuit processes.

BackgroundThe most prevalent cancer and the second-leading cause of cancer-related mortality in women is breast cancer. Growing interest has been shown in recent years in learning more about the processes behind the development of breast cancer. It has been shown that persistent inflammation may play a significant role in the advancement of breast cancer. However, a comprehensive and objective analysis on the state of inflammation in breast cancer research is still lacking. This study was aim to undertake a bibliometric analysis of breast cancer research associated with inflammation between 2013 and 2022 in order to identify the trends, dynamics, and scientific outputs in the field.MethodsFrom 2013 to 2022, original and review publications on breast cancer and inflammation-associated research were retrieved from the Web of Science Core Collection (WOSCC) database. To examine the position of yearly publications, journals, nations, institutions, and authors, we employed two bibliometric tools (CiteSpace and VOSviewer). After that, by examining keyword visualization and keyword bursts, we determined the hot research fields related to inflammation in breast cancer.Resultswe discovered 6902 publications regarding inflammation in breast cancer by using our retrieval approach. In terms of the number of publications, The United States ranked first in the global study, followed by China and Italy. In terms of institutions, the University of Texas System, UT MD Anderson Cancer Center, and University of California System are in the top 3 for the quantity of publications published. The most popular journal for this field research is “CANCERS.” Ueno NT, Woodward WA, Cristofanilli M, and others have made significant contributions to the understanding of inflammation in breast cancer. In the end, we conducted a biclustering analysis on keywords and discovered three clusters that represent research hotspots.ConclusionAccording to the global trend, the research output of inflammation in breast cancer is increasing. The information provided in this article, including the cooperation network information of authors, nations, journals, and institutions, may help researchers to better understand hotspots and developing patterns in this discipline. At present, the focus of study gradually shifts from “phenotype study” to “therapeutic research”. It is recommended to pay attention to the latest hot spots, such as targeted therapy, antimicrobial activity and nanoparticle.

Aiming at the problem of excessive vertical vibration caused by seismic primary waves in jacket structures located in seismic zones in China, a new type of shear plate metal damper with upper and lower elliptical openings (SPA) is proposed in this paper. The seismic model test is carried out to compare the shear plate metal damper with three traditional opening forms, central elliptical opening, single row opening, and double row opening, and the hysteretic curve, skeleton curve, and cyclic hysteretic energy dissipation capacity are analyzed. The results show that the energy dissipation performance of the new shear plate metal damper with upper and lower elliptical openings proposed in this paper is better than that of the other three types of dampers.

Abstract Braided neurovascular flow diverters (FDs) have revolutionized the treatment of aneurysms. However, dual antiplatelet therapy is mandatory for patients with FDs, which increases the risk of adverse side effects like bleeding complications. Surface modification would be of critical relevance to improve the biocompatibility and therefore clinical performance of FDs. Herein, a titanium dioxide (TiO2) covalently immobilized citric acid (CA) nanohybrid coating is fabricated on nitinol (NiTi) braided FDs by liquid phase deposition and dip‐coating, to inhibit thrombosis and promote re‐endothelialization. The CA molecules are covalently bound onto the pre‐deposited TiO2 nanoparticulate coating. The coating has a unique homogenously nanostructured morphology as well as super‐hydrophilicity. Both in vitro and in vivo results verify that the coated samples inhibit platelets and fibrinogen adhesion, delay coagulation time, and concomitantly promote re‐endothelialization. Such appealing properties are ascribed to the nature of CA and TiO2 per se as well as the nanostructured morphology. The present strategy may not only provide a new avenue to surface‐modify braided neurovascular FDs but also shed light on advanced nanohybrid materials for biomedical applications among others.

In recent years, modular multilevel converters (MMCs) have been increasingly used in the field of electric vehicle charging and discharging due to their unique performance advantages. However, the unique cascade structure of MMCs raises the problem of the circulating current. Due to chemical processes, aging effects, etc., the capacitance parameters of the upper and lower bridge arms will be asymmetric, which will introduce odd harmonics into the circulating current, increasing system losses and threatening the system reliability. To address this phenomenon, this paper proposes a circulating current suppression strategy with additional virtual impedance (VI) based on a repetitive controller (RC). The corresponding simulation models were built for the comparative study of different circulating current suppression strategies. The results show that the VI-RC circulating current suppression strategy can significantly reduce the odd and even harmonics in the circulating current under asymmetric conditions, and the total harmonic distortion (THD) of the bridge arm current is only 0.98%, which verifies the effectiveness of the proposed strategy.

Based on the human structure as the prototype， a humanoid robot with a lower limb and 7 degrees of freedom is designed based on bionics. The motion coordinates of human body during normal walking are collected through the three-dimensional optical motion capture system， and the joint angle and the variation rule of the moment of each lower extremity joint during walking are obtained by analyzing the data. A 7-degree-of-freedom kinematics model of the lower extremity is established and inverse kinematics analysis is carried out to solve the variation of the lower extremity joint angles. According to the gait programming， the rotation angles of the lower extremity joints during the humanoid robot walking are solved. In the lower extremity in UG environment， the 3D model of 7 DOF humanoid robot is established， and the dynamics simulation is carried out by using multi-body dynamics software Adams， combining with the gait motion planning from the angle of the changing situation to set the simulation parameters， through virtual prototype humanoid robot gait， and the simulation data and the data results from the motion capture experimental are compared. Research results show， the humanoid robot can achieve stable walking gait， the overall accuracy of the modeling ideas， analysis of kinematics and dynamics simulation is confirmed. Based on the results of the experiment simulation for hip and knee torque value compared with the experimental value of relative error of 6.7% and 9.6% respectively in a reasonable scope， the basis for the humanoid robot structure design and motor selection is provided.

Abstract Background Achondroplasia (ACH) is the most common genetic form of dwarfism and belongs to dominant monogenic disorder caused by a gain-of-function point mutation in the transmembrane region of FGFR3. There are no effective treatments for ACH. Stem cells and gene-editing technology provide us with effective methods and ideas for ACH research and treatment. Methods We generated non-integrated iPSCs from an ACH girl’s skin and an ACH boy’s urine by Sendai virus. The mutation of ACH iPSCs was precisely corrected by CRISPR-Cas9. Results Chondrogenic differentiation ability of ACH iPSCs was confined compared with that of healthy iPSCs. Chondrogenic differentiation ability of corrected ACH iPSCs could be restored. These corrected iPSCs displayed pluripotency, maintained normal karyotype, and demonstrated none of off-target indels. Conclusions This study may provide an important theoretical and experimental basis for the ACH research and treatment.

Background: A balloon dilatation catheter is a vital tool in percutaneous transluminal angioplasty. Various factors, including the material used, influence the ability of different types of balloons to navigate through lesions during delivery. Objective: Thus far, numerical simulation studies comparing the impacts of different materials on the trackability of balloon catheters has been limited. This project seeks to unveil the underlying patterns more effectively by utilizing a highly realistic balloon-folding simulation method to compare the trackability of balloons made from different materials. Methods: Two materials, nylon-12 and Pebax, were examined for their insertion forces via a bench test and a numerical simulation. The simulation built a model identical to the bench test’s groove and simulated the balloon’s folding process prior to insertion to better replicate the experimental conditions. Results: In the bench test, nylon-12 demonstrated the highest insertion force, peaking at 0.866 N, significantly outstripping the 0.156 N force exhibited by the Pebax balloon. In the simulation, nylon-12 experienced a higher level of stress after folding, while Pebax had demonstrated a higher effective strain and surface energy density. In terms of insertion force, nylon-12 was higher than Pebax in specific areas. Conclusion: nylon-12 exerts greater pressure on the vessel wall in curved pathways when compared to Pebax. The simulated insertion forces of nylon-12 align with the experimental results. However, when using the same friction coefficient, the difference in insertion forces between the two materials is minimal. The numerical simulation method used in this study can be used for relevant research. This method can assess the performance of balloons made from diverse materials navigating curved paths and can yield more precise and detailed data feedback compared to benchtop experiments.

Abstract Twin-field (TF) quantum key distribution (QKD) is highly attractive because it can beat the fundamental limit of secret key rate for point-to-point QKD without quantum repeaters. Many theoretical and experimental studies have shown the superiority of TFQKD in long-distance communication. All previous experimental implementations of TFQKD have been done over optical channels with symmetric losses. But in reality, especially in a network setting, the distances between users and the middle node could be very different. In this paper, we perform a proof-of-principle experimental demonstration of TFQKD over optical channels with asymmetric losses. We compare two compensation strategies, that are (1) applying asymmetric signal intensities and (2) adding extra losses, and verify that strategy (1) provides much better key rate. Moreover, the higher the loss, the more key rate enhancement it can achieve. By applying asymmetric signal intensities, TFQKD with asymmetric channel losses not only surpasses the fundamental limit of key rate of point-to-point QKD for 50 dB overall loss, but also has key rate as high as 2.918 × 10−6 for 56 dB overall loss. Whereas no keys are obtained with strategy (2) for 56 dB loss. The increased key rate and enlarged distance coverage of TFQKD with asymmetric channel losses guarantee its superiority in long-distance quantum networks.

FK506, a first-line immunosuppressant, is routinely administered orally and intravenously to inhibit activation and proliferation of T cells after heart transplantation (HT). Current administration route is not conducive enough to exert its efficacy in lymphatic system. Herein, we proposed that subcutaneous (SC) administration of FK506-loaded nanoparticles (PLGA-FK506-NPs) would be valuable for treating acute rejection after HT. The biodistribution and pharmacokinetic study revealed that it could effectively deliver FK506 to the lymph nodes (LNs) due to their suitable particle size, especially in inguinal LNs. Subsequently, the therapeutic efficacy of PLGA-FK506-NPs on the HT model was evaluated using intravenous (IV), intragastric (IG), or SC injection. Histopathological analysis revealed that 80% of allografts exhibited only grade 1R rejection with negligible lymphocyte infiltration in the SC group. The IV group exhibited 40% 1R rejection with mild lymphocyte infiltration and 20% grade 3R that require further intervention, and the IG group exhibited grades 40% 3R rejection with more lymphocyte infiltration. Moreover, the infiltration of T cells and the secretion of IL-2 and IFN-γ were significantly reduced in the SC group compared with the IG or IV group. The mean survival time (MST) further revealed that 50% of grafts treated with PLGA-FK506-NPs via SC injection survived longer than IG and IV groups. Moreover, the MST of single-dose SC injection of PLGA-FK506-NPs demonstrated that it would effectively reduce the required dose for a similar therapeutic effect. Overall, these results indicate that SC administration of PLGA-FK506-NPs is a more effective route for chronic FK506 treatment.

The primary aim of this paper was to identifying the geographical origin of tobacco leaves based on stable isotopic and mineral elemental fingerprint. We collected eighty-one tobacco leaf samples from Argentina, Brazil, Zimbabwe, the U.S., Zambia, and China. And nine mineral element contents and four strontium and lead isotope ratios of the tobacco leaves were determined by thermal ionization mass spectrometry (TIMS) and inductively coupled plasma mass spectrometry (ICP-MS). After variance and stepwise discriminant analysis, the discriminant functions of the tobacco leaf’s geographical origin were established. The results indicate that: (1) the contents of six mineral elements including Cu, Zn, Cr, Ni, Cd, and Pb, together with four strontium and lead isotope ratios containing 87Sr/86Sr, 208Pb/204Pb, 207Pb/204Pb, and 206Pb/204Pb, were significantly different among six countries. (2) Different countries presented some characteristic mineral elemental and isotopic fingerprint. The even contents of mineral elements from Zambian tobacco leaf were much lower than the other countries, among which four elements consisting of Zn, Cr, As, and Cd were not detected. The three average lead isotope ratios including 208Pb/204Pb, 207Pb/204Pb, and 206Pb/204Pb from Zimbabwe tobacco leaves were far higher than the other countries, and the range of which was unoverlapped. (3) The effective identification of the geographical origin of tobacco leaf was accomplished by Fisher stepwise discriminant analysis and the characteristic tracing elements consisted of Cu, Zn, Cr, Ni, Cd, Pb, 87Sr/86Sr, 207Pb/204Pb, and 206Pb/204Pb. Based on the established discriminant functions, the original and cross-validation accuracy towards different geographical origins of tobacco leaves were 98.8% and 95.1%, respectively. The study shows that the strontium and lead isotopic with mineral elemental fingerprints is a potential effective method to identify the geographical origin of tobacco leaves from different countries.

In recent years, with the rise of the electric vehicle industry, there has been significant research on charging and power supply vehicle technologies for electric vehicles. In terms of the corresponding converter usage, modular multi-level converters (MMCs) are also increasingly used in the field of electric vehicle power supply research because of their unique advantages. However, the circulating current problem of MMCs has not been effectively addressed in existing domestic and international studies. In this paper, we propose a proportional-integral resonant (PIR) control method combined with virtual impedance for the optimal suppression of the MMC internal circulating current problem based on the comparison and generalization of the existing methods. Based on the analysis of the working principle of MMCs, this paper proposes and adopts the control strategy of combining virtual impedance and proportional-integral resonance to suppress the circulating current and builds a simulation model in MATLAB to verify that the control strategy proposed in this paper is feasible.

In this work, we incorporate decoy-state analysis into a well-established numerical framework for key rate calculation, and we apply the numerical framework to decoy-state BB84 and measurement-device-independent (MDI) QKD protocols as examples. Additionally, we combine with these decoy-state protocols what is called “fine-grained statistics,” which is a variation of existing QKD protocols that makes use of originally discarded data to get a better key rate. We show that such variations can grant protocols resilience against any unknown and slowly changing rotation along one axis, similar to reference-frame-independent QKD, but without the need for encoding physically in an additional rotation-invariant basis. Such an analysis can easily be applied to existing systems, or even data already recorded in previous experiments, to gain a significantly higher key rate when considerable misalignment is present, extending the maximum distance for BB84 and MDI-QKD and reducing the need for manual alignment in an experiment.

While solid oxide electrolysis presents an approach to remove CO2 from high-temperature emission streams, it is challenging to engineer stable yet active interfaces. Here, authors show in situ exsolution of nanoscale metal-metal oxide interfaces that improve cathode activities and durabilities.

Iris segmentation algorithms are of great significance in complete iris recognition systems, and directly affect the iris verification and recognition results. However, the conventional iris segmentation algorithms have poor adaptability and are not sufficiently robust when applied to noisy iris databases captured under unconstrained conditions. In addition, there are currently no large iris databases; thus, the iris segmentation algorithms cannot maximize the benefits of convolutional neural networks (CNNs). The main work of this paper is as follows: first, we propose an architecture based on CNNs combined with dense blocks for iris segmentation, referred to as a dense-fully convolutional network (DFCN), and adopt some popular optimizer methods, such as batch normalization (BN) and dropout. Second, because the public ground-truth masks of the CASIA-Interval-v4 and IITD iris databases do not include the labeled eyelash regions, we label these regions that occlude the iris regions using the Labelme software package. Finally, the promising results of experiments based on the CASIA-Interval-v4, IITD, and UBIRIS.V2 iris databases captured under different conditions reveal that the iris segmentation network proposed in this paper outperforms all of the conventional and most of the CNN-based iris segmentation algorithms with which we compared our algorithm's results in terms of various metrics, including the accuracy, precision, recall, f1 score, and nice1 and nice2 error scores, reflecting the robustness of our proposed network.

Voltage-dependent anion channels (VDACs) are the most abundant proteins in the mitochondrial outer membranes of all eukaryotic cells. They participate in mitochondrial energy metabolism, mitochondria-mediated apoptosis, and cell growth and reproduction. Here, the chromosomal localizations, gene structure, conserved domains, and phylogenetic relationships were analyzed. The amino acid sequences of VDACs were found to be highly conserved. The tissue-specific transcript analysis from transcriptome data and qRT-PCR demonstrated that grapevine VDACs might play an important role in plant growth and development. It was also speculated that VDAC3 might be a regulator of modulated leaf and berry development as the expression patterns during these developmental stages are up-regulated. Further, we screened the role of all grape VDACs’ response to pathogen stress and found that VDAC3 from downy mildew Plasmopara viticola-resistant Chinese wild grapevine species Vitis piasezkii “Liuba-8” had a higher expression than the downy mildew susceptible species Vitis vinifera cv. “Thompson Seedless” after inoculation with P. viticola. Overexpression of VpVDAC3 resulted in increased resistance to pathogens, which was found to prevent VpVDAC3 protein accumulation through protein post-transcriptional regulation. Taken together, these data indicate that VpVDAC3 plays a role in P. viticola defense and provides the evidence with which to understand the mechanism of grape response to pathogen stress.

Unlike mammals, zebrafish can regenerate injured hearts even in the adult stage. Cardiac regeneration requires the coordination of cardiomyocyte (CM) proliferation and migration. The TGF-β/Smad3 signaling pathway has been implicated in cardiac regeneration, but the molecular mechanisms by which this pathway regulates CM proliferation and migration have not been fully illustrated. Here, we investigated the function of TGF-β/Smad3 signaling in a zebrafish model of ventricular ablation. Multiple components of this pathway were upregulated/activated after injury. Utilizing a specific inhibitor of Smad3, we detected an increased ratio of unrecovered hearts. Transcriptomic analysis suggested that the TGF-β/Smad3 signaling pathway could affect CM proliferation and migration. Further analysis demonstrated that the CM cell cycle was disrupted and the epithelial–mesenchymal transition (EMT)-like response was impaired, which limited cardiac regeneration. Altogether, our study reveals an important function of TGF-β/Smad3 signaling in CM cell cycle progression and EMT process during zebrafish ventricle regeneration.

The quadrilateral anterior cervical plate (ACP) is used extensively in anterior cervical discectomy and fusion (ACDF) to reconstruct the stability of the cervical spine and prevent cage subsidence. However, there have been no comparison studies on the biomechanical performance of quadrilateral ACP and triangular ACP. The objective of this study is to investigate the functional outcomes of quadrilateral ACP and triangular ACP usage in ACDF surgery. In this study, a finite element model of intact C1-C7 segments was established and verified. Additionally, two implant systems were built; one using triangle anterior cervical plates (TACP) and another using quadrilateral orion anterior cervical plate (QACP). Both models were then compared in terms of their postoperative biomechanical performance, under normal and excessive motion. Compared to QACP, the peak stress of the TACP screws and plates occurred at 359.2 MPa and 97.2 MPa respectively and were the highest during over extension exercises. Alternately, compared to TACP, the endplate peak stress and the cage displacement of QACP were the largest at over extension, with values of 7.5 MPa and 1.2 mm, respectively. Finally, the average stress ratio of bone grafts in TACP was relatively high at 31.6%. In terms of biomechanical performance, TACP can share the load more flexibly and reduce the risks of cage subsidence and slippage but the screws have high peak stress value, thereby increasing the risk of screw slippage and fracture. This disadvantage must be considered when designing a TACP based implant for a potential patient.

The ancient Zhenggang landslide (47.5 million m3) represents a potential threat to the construction and safe operation of the proposed Gushui Hydropower Project and to the people living downstream. The landslide was caused by continuous rainfall from October 20 to November 5, 2008, indicating that groundwater aggravated sliding and deformation, and it can be divided into two distinct zones: zone I and zone II. Investigations of the Zhenggang landslide deposits have been conducted for 10 years, but the evolution of the landslide deformation is yet to be revealed. Geological surveys and stability analysis have revealed that the Zhenggang landslide is currently relatively stable and is not sliding. The deformational data for the deep soil layers show that subslide zone I is a translational slide, while subslide zone II is an uplift slide, and zone II is slightly more stable than zone I. Obvious interior deformational failure occurred and was observed in the exploratory adits. The numerical results show that rainfall infiltration is the main factor to induce the revival of ancient landslide, and it is necessary to strengthen the landslide risk assessment and reliability of Zhenggang landslide under the condition of rainfall infiltration. Finally, in order to reduce landslide risks, the local residents living near the landslide mass should be relocated, and measures should be taken to increase slope stability.

Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting millions of elder people due to the degeneration of dopamine neurons in the striatum and substantia nigra. The clinical manifestations of PD include tremor, rigidity, bradykinesia and postural instability. Studying PD is challenging due to two obstacles: 1) disease models such as primary neurons or animal models usually couldn’t recapitulate the disease phenotype, and 2) accessibility of human autopsied brain samples is very limited if not impossible. Induced pluripotent stem cells (iPSCs)-derived neuronal cells from patients emerge as an ideal in vitro model for disease modeling and drug development. Here we describe a cell density-dependent method for preparing functional hiPSC-derived dopamine neurons (iDAs) with ~90% purity (TH-positive cells). iDAs derived from PD patient exhibit the disease-related phenotypes, for example, slowed morphogenesis, reduced dopamine release, impaired mitochondrial function, and α-synuclein accumulation as early as 35 days after induction. Furthermore, we found that the effects of cell density are different between iDA development stages, whereas high cell density increases stress for early neural progenitor cells (NPCs), but are neural-protective for mature iDAs, high density also favors morphogenesis. Hence, using stage and density-dependent strategies we can obtain high quality iDAs, which are critical for disease modeling, drug development and cell replacement therapy.

Vesicular glutamate transporter type 2 (VGLUT2) is known to play an important role in mediating heat hyperalgesia induced by inflammation. However, the underlying mechanism for this activity is poorly understood. Cyclin-dependent kinase 5 (Cdk5), serving as a key regulator in modulating release of glutamate, acted a key player in the formation of heat hyperalgesia of inflammatory pain. However, it remains unknown whether there is a bridge between Cdk5 and VGLUT2 for mediating inflammatory pain. Therefore, we designed the experiment to determine whether VGLUT2 signaling pathway is involved in inflammatory pain mediated by Cdk5 in the inflammatory pain model induced by complete Freund’s adjuvant (CFA). Our results showed that the coexpression of Cdk5/VGLUT2 in small- and medium-sized neuronal cells of the dorsal root ganglion (DRG) and spinal cord between days 1 and 3 following subcutaneous injection of CFA was significantly increased. Moreover, our study revealed that the expression of VGLUT2 protein in the DRG and spinal cord was remarkably increased between days 1 and 3 following CFA injection and was significantly reduced by roscovitine, a selective antagonist of Cdk5. Additionally, p25 but not p35, an activator of Cdk5, protein was significantly increased by CFA and reduced by roscovitine. Our findings suggested that VGLUT2/Cdk5 signaling pathway contributes to inflammatory pain mediated by Cdk5/p25.

This paper considers the problem of adaptive estimation of graph signals under the impulsive noise environment. The existing least mean squares (LMS) approach suffers from severe performance degradation under an impulsive environment that widely occurs in various practical applications. We present a novel adaptive estimation over graphs based on Welsch loss (WL-G) to handle the problems related to impulsive interference. The proposed WL-G algorithm can efficiently reconstruct graph signals from the observations with impulsive noises by formulating the reconstruction problem as an optimization based on Welsch loss. An analysis on the performance of the WL-G is presented to develop effective sampling strategies for graph signals. A novel graph sampling approach is also proposed and used in conjunction with the WL-G to tackle the time-varying case. The performance advantages of the proposed WL-G over the existing LMS regarding graph signal reconstruction under impulsive noise environment are demonstrated.

This paper presents a systematic analysis of DC voltage stability of a multi-terminal VSC-HVDC (MTDC) system, with the emphasis on a comparative study of the most ubiquitous droop control configurations. The paper introduces a general framework for the analysis of various droop control configurations employed in MTDC systems. This framework is then used to compare leading droop control configurations in terms of their impact on the relative stability, performance and robustness of the overall MTDC system. A generalized analytical MTDC model that contains detailed models of AC and DC system components is derived. Limitations imposed by DC power flow, DC inductor, cable modeling and AC network impedance on DC system stability are identified. Classical and multivariable frequency response analysis and eigenvalue analysis are applied to open-loop and closed-loop models to compare the stability and robustness of five leading droop controllers, with the focus on feedback signal selection and controller parameterization. This paper also proposes an active stabilizing controller, which takes the form of a modified constant power control, to enhance the controllability and robustness of the DC voltage control.

Abstract Background The current study determined the expression and clinical value of lncRNA AC010761.9 in human gastric adenocarcinoma (GA). Methods Real-time quantitative reverse transcription (qRT)-PCR was used to detect the level of lncRNA expression in 145 GA tissues and three GA cell lines, and the correlation between its level and clinicopathologic characteristics and potential corresponding mRNA of TNF receptor-associated factor 4 gene (TRAF4) was then evaluated. Results Elevated lncRNA AC010761.9 was detected in all 6 GA tissues by previous lncRNA expression profile microarray assay. LncRNA AC010761.9 was over-expressed in 99 of 145 GA tissues (68.3%) with an elevated fold change of up to 35.14 compared to matched paracancerous tissues (p

To acquire precise channel state information in the forthcoming 5G frequency division duplexing (FDD) communication system, we study the virtual angular domain channel common sparsity in the massive multiple-input multiple-output orthogonal frequency division multiplexing downlink transmission system. Here, we propose an off-line learning algorithm, which is based on expectation maximization and generalized approximate message passing (GAMP) algorithms. Besides, the learning algorithm utilizes the common sparsity to obtain the support information of the sparse channel vectors. This process is defined as zero partition. With this learned information, the sparse channel vectors are estimated using GAMP again. We prove that when the learned support information is correct, the zero partition-enhanced GAMP algorithm will achieve better sparsity and indeterminacy trade-off performance when compared with the original GAMP algorithm. Based on these results, our proposed channel estimation (CE) solution has two stages. In the first stage, because of the practical propagation characteristics, the zero partition learning algorithm is amended to ensure the correctness of the learned result. In the second stage, channel vectors on the virtual angular domain are estimated with the proposed zero partition-enhanced GAMP algorithm. Analysis and numerical results prove that our CE solution will consume less pilots to ensure the estimation accuracy, when compared with other existed FDD downlink CE solution.

Millimeter-wave (mm-wave) communication is a practicable scheme for big data communication, such as next-generation cellular communication. However, because mm-wave frequencies have an extremely large path loss, in order to mitigate the mm-wave path loss, a large number of antennas are packed for beamforming. Conventional multiple-input multiple-output (MIMO) beamforming uses digital processing, which leads to needing extremely giant energy. Hybrid analog/digital beamforming could serve as an awesome technique to reduce the cost. It is essential for achieving high beamfroming gain that accurate mm-wave channel information should be attained at the transmitter. This paper concentrates on the channel state information (CSI) acquirement problem in mm-wave communication systems with massive MIMO arrays. Because of the CSI acquirement, a method of significant overhead, we consider an accurate channel estimation scheme with low overhead. Not only do we in this paper propose using support information extracted at Sub-6GHz to aid the mm-wave CSI acquirement but also introduce a strategy used in the mm-wave channel estimation. We, in particular, formulate mm-wave CSI acquirement as a compressive sensing problem and obtain the CSI by using Bernoulli-Gaussian generalized approximate message passing (BG-GAMP) algorithm. We also extend the BG-GAMP algorithm with support distribution information from sub-6-GHz channel. Furthermore, based on the K nearest neighbor idea, we redesign the BG-GAMP algorithm depending on sub-6-GHz support distribution information. Details of BG-GAMP with the nearest neighbor learning algorithm that is built on support distribution information from sub-6-GHz channel would also be revealed. Simulation results show the out-of-band information aided mm-wave channel estimation is capable of reducing the pilot overhead greatly and channel estimation accuracy improved as well.

As one of the signed variants of the diffusion least mean square (DLMS) algorithm over networks, the diffusion sign error algorithm has been presented in previous reference. In this paper, we propose two novel signed variants of the DLMS algorithm, i.e., the diffusion signed regressor algorithm and the diffusion sign-sign algorithm. Moreover, this paper analyzes the performance of these three signed variants of the DLMS algorithm for cyclostationary white Gaussian inputs which have periodically time-varying variances. It is assumed that the distributed algorithms are in non-stationary environments. Specifically, the unknown parameter to be identified is time-varying according to the standard random walk model. The analysis models in terms of mean weight behavior and mean square performance are provided, in which, we can find some interesting results. Finally, simulations are carried out to verify the correctness of the proposed analysis model.