Recent Progresses in Oxygen Reduction Reaction Electrocatalysts for Electrochemical Energy Applications

Abstract Electrochemical energy storage systems such as fuel cells and metal–air batteries can be used as clean power sources for electric vehicles. In these systems, one necessary reaction at the cathode is the catalysis of oxygen reduction reaction (ORR), which is the rate-determining factor affecting overall system performance. Therefore, to increase the rate of ORR for enhanced system performances, efficient electrocatalysts are essential. And although ORR electrocatalysts have been intensively explored and developed, significant breakthroughs have yet been achieved in terms of catalytic activity, stability, cost and associated electrochemical system performance. Based on this, this review will comprehensively present the recent progresses of ORR electrocatalysts, including precious metal catalysts, non-precious metal catalysts, single-atom catalysts and metal-free catalysts. In addition, major technical challenges are analyzed and possible future research directions to overcome these challenges are proposed to facilitate further research and development toward practical application. Graphic Abstrac

Effect of pre-slaughter fasting time on carcass yield, blood parameters and meat quality in broilers

Objective The aim of this study was to evaluate the effect of pre-slaughter fasting time on carcass yield, meat quality, blood parameters and glucose metabolism in broilers. Methods Four hundred and fifty Arbor Acres (AA) broilers at 42 days of age were divided into 5 groups with 6 replicates in each group and 15 chickens as one replicate. Following this period, broilers from each group were distributed among five groups according to pre-slaughter fasting period as 4, 8, 12, 16, or 20 h. Results With increasing fasting time, the carcass yield (p0.10), while the increase of fasting time resulted in a linear decrease of the blood glucose (p = 0.021) and, more specifically, the glycogen content of the liver and leg muscles (p<0.001). With increasing fasting time, the aspartate transaminase (p<0.01), uric acid (p<0.01), and triglycerides (p<0.01) in serum linearly downregulated, while the alanine aminotransferase was linearly upregulated. Conclusion The results of this study show a significant influence of fasting time on carcass yield and meat quality in broilers. Moderate fasting (8 to 12 h) before slaughter can reduce the weight loss of broilers. Prolonged fasting (≥16 h) increased body weight loss, decreased slaughtering performance and fluctuating blood indexes of broilers

Metal-free photo-induced sulfidation of aryl iodide and other chalcogenation

A photo-induced C-S radical cross-coupling of aryl iodides and disulfides under transition-metal and external photosensitizer free conditions for the synthesis of aryl sulfides at room temperature has been presented, which features mild reaction conditions, broad substrate scope, high efficiency, and good functional group compatibility. The developed methodology could be readily applied to forge C-S bond in the field of pharmaceutical and material science

Double-Observer-Based Bumpless Transfer Control of Switched Positive Systems

This paper investigates the bumpless transfer control of linear switched positive systems based on state and disturbance observers. First, state and disturbance observers are designed for linear switched positive systems to estimate the state and the disturbance. By combining the designed state observer, the disturbance observer, and the output, a new controller is constructed for the systems. All gain matrices are described in the form of linear programming. By using co-positive Lyapunov functions, the positivity and stability of the closed-loop system can be ensured. In order to achieve the bumpless transfer property, some additional sufficient conditions are imposed on the control conditions. The novelties of this paper lie in that (i) a novel framework is presented for positive disturbance observer, (ii) double observers are constructed for linear switched positive systems, and (iii) a bumpless transfer controller is proposed in terms of linear programming. Finally, two examples are given to illustrate the effectiveness of the proposed results

Capsule networks embedded with prior known support information for image reconstruction

Compressed sensing (CS) has been successfully applied to realize image reconstruction. Neural networks have been introduced to the CS of images to exploit the prior known support information, which can improve the reconstruction quality. Capsule Network (Caps Net) is the latest achievement in neural networks, and can well represent the instantiation parameters of a specific type of entity or part of an object. This study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS framework. The output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of interest. To lead the dynamic routing to the most likely index, a group of prediction vectors is designed determined by the information. Furthermore, the results of experiments on imaging signals are taken for a comparation of the performances among different algorithms. It is concluded that the proposed capsule network (Caps Net) creates higher reconstruction quality at nearly the same time with traditional Caps Net

Logging Pattern Detection by Multispectral Remote Sensing Imagery in North Subtropical Plantation Forests

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698&ndash;713 nm, B5 in Sentinel-2), short-wave infrared band (2100&ndash;2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands

A Nomogram to Predict the Risk for MACCE within 1 Year after Discharge of Patients with NVAF and HFpEF: A Multicenter Retrospective Study

Background: To develop and validate a nomogram prediction model for assessing the risk of major adverse cardiovascular and cerebrovascular events (MACCE) in patients with nonvalvular atrial fibrillation (NVAF) and heart failure with preserved ejection fraction (HFpEF) within one year of discharge. Methods: We enrolled 828 patients with NVAF and HFpEF from May 2017 to March 2022 in Zhongda Hospital as the training cohort, and 564 patients with NVAF and HFpEF in Taizhou People’s Hospital between August 2018 and March 2022 as the validation cohort. A total of 35 clinical features, including baseline characteristics, past medical records, and detection index, were used to create a prediction model for MACCE risk. The optimized model was verified in the validation cohort. Calibration plots, the Hosmer-Lemeshow test, and decision curve analyses (DCA) were utilized to assess the accuracy and clinical efficacy of the nomogram. Results: MACCE occurred in 23.1% of all patients within one year of discharge. The nomogram identified several independent risk factors for MACCE, including atrial fibrillation duration ≥6 years, poor medication compliance, serum creatinine level, hyperthyroidism, serum N-terminal pro-brain natriuretic peptide level, and circumferential end-diastolic stress. The DCA demonstrated the excellent efficacy of the prediction model for the MACCE end-point, with a wide range of high-risk threshold probabilities in both cohorts. The Hosmer-Lemeshow test confirmed that momogram predictions fit for both the training (p = 0.573) and validation (p = 0.628) cohorts. Conclusions: This nomogram prediction model may offer a quantitative tool for estimating the risk of MACCE in patients with NVAF and HFpEF within one year of discharge

Logging Pattern Detection by Multispectral Remote Sensing Imagery in North Subtropical Plantation Forests

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands

Strong Structural Controllability Analysis of Structured Networks with Identical Nodes

In this note, necessary and sufficient rank conditions are proposed for checking the strong structural controllability of structured networks with identical nodes. More specifically, for a network with multi-input multi-output (MIMO) nodes, under an assumption called the strong invertibility for the nodal systems, the strong structural controllability of a given structured network is shown to be solely determined by its own topology. For the single-input single-output (SISO) case, such an assumption is allowed to be dropped by incorporating certain controllability and observability conditions for the nodal systems. Additionally, we utilize a recently developed graph-theoretical method for structured systems to verify the proposed rank conditions

Experimental investigation on the residual axial capacity of close-in blast damaged CFDST columns

Concrete-filled double-skin steel tubes (CFDSTs) are comprised of two concentrically placed steel tubes and concrete core filled in the annulus between the two tubes. A number of studies have demonstrated the excellent structural performance of CFDSTs as load-bearing columns under various loading conditions, which have attracted lots of interests of engineers and researchers. Despite the increasing popularity of using CFDSTs as load-bearing structural columns, there is limited knowledge on blast resistant performance of CFDST columns particular under close-in explosion. In this paper, an experimental study was conducted to investigate the residual axial load-carrying capacity of CFDST columns after being subjected to close-in explosion. Field blast tests were firstly carried out on CFDST columns. Then, the blast-damaged columns were subjected to axial compression till failure under quasi-static condition in the laboratory to examine their residual axial load-carrying capacities. Seven half-scale CFDST column specimens were constructed and tested, of which one column serves as a reference column that was directly tested under axial compression to check the ultimate axial load-carrying capacity of CFDST columns without blast damage. The influences of design factors including charge weight, stand-off distance, charge orientation, and section hollow ratio on blast resistant performance of CFDST columns were quantitatively assessed based on the residual axial capacity index. It was found that under close-in blast loading CFDST columns showed localized cross-section denting with slight global deformation. The damaged columns can maintain up to 60% of its ultimate axial load-carrying capacity and exhibited good strength and ductility