DiP: Learning Discriminative Implicit Parts for Person Re-Identification

In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods extract part features in an explicit manner, by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, a set of attributes and auxiliary losses are introduced to further improve the learning of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available

Promoting hydrogen-evolution activity and stability of perovskite oxides via effectively lattice doping of molybdenum

Electrocatalysts are the most compelling objectives in realizing highly efficient renewable energy conversion and storage applications. Rational doping is an effective strategy for the development of cost-effective perovskite oxides with high electrochemical performance. In this study, we report facilely prepared molybdenum (Mo)-doped SrCo0.70Fe0.30O3-δ perovskites such as SrCo0.7Fe0.25Mo0.05O3-δ (SCFM0.05) and SrCo0.7Fe0.20Mo0.10O3-δ (SCFM0.10) for boosting the hydrogen evolution reaction (HER) activity and stability. Among them, SCFM0.05 delivers a promising overpotential of ∼323 mV at the current density of 10 mA cmdisk^-2 and keeps almost stable for 5 h and after accelerated 1000 cycles. The promoted HER activity of SCFM0.05 regarding the decreased overpotential, increased catalytic current density, and improved charge transfer kinetics, might originate from the combined effects of distortion of octahedral coordination, low oxygen vacancy/high oxidation state of Co, abundant lattice oxygen and highly oxidative oxygen species, long B–O length, and strong OH− adsorption compared to the un-doped counterpart. We ascribe the enhanced operational stability to the formation of a low concentration of oxygen vacancy that stabilizes the crystal structure of Mo-doped SrCo0.7Fe0.3O3-δ and prevents the surface from Sr leaching/surface amorphization. These findings suggest that tuning perovskite oxide using a redox-inactive dopant featured with high valence state may provide further avenues to HER optimization.This research is supported by the National Natural Science Foundation of China (No. 51702125 & No. 21808080), Pearl River S&T Nova Program of Guangzhou (No. 201806010054), Fundamental Research Funds for the Central Universities (No. 21616301), and the China Postdoctoral Science Foundation (No. 2017M620401)

Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics

This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (SSC), Acidity and pulp percentage (PP) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the SSC, Acidity and PP in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the SSC (R2 = 0.9240, RMSE = 0.2595) and the PP (R2 = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting Acidity (R2 = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit

Study on Thin Lamination of Carbon Fiber Based on Mechanical Broadening

Carbon fiber has excellent mechanical properties and plays an important role in modern industry. However, due to the complexity of the carbon fiber widening process, the industrial application of carbon fiber is limited. By designing the carbon fiber widening equipment of automaton, the relationship between the widening width of carbon fiber and the process parameters is studied, and the optimum developing process parameters are obtained, to improve the performance of carbon fiber composites to a certain extent. In this study, the widening process of carbon fiber was studied based on the mechanical broadening method. Firstly, an automatic broadening equipment was designed, and the effects of the initial tension, the number of straight rods, the number of convex rods, and the drawing speed on the widened width during the broadening process were discussed. The widening effect was evaluated by SEM imaging and mechanical testing. At the same time, the factors affecting the broadening width and broadening defects during the broadening process were analyzed, and the optimal broadening process parameters were obtained. The results showed that within a specific range, a higher initial tension, a greater number of convex rods, and an appropriate speed resulted in relatively smaller damage to the broadening of carbon fibers. Through the design of automatic broadening, this experiment explores optimal broadening process parameters, provides a reference for the improvement of the carbon fiber broadening process and further promotes large-scale industrial applications of carbon fiber

Surprisingly High Activity for Oxygen Reduction Reaction of Selected Oxides Lacking Long Oxygen-Ion Diffusion Paths at Intermediate Temperatures: A Case Study of Cobalt-Free BaFeO_3‑δ

The widespread application of solid oxide fuel cell technology requires the development of innovative electrodes with high activity for oxygen reduction reaction (ORR) at intermediate temperatures. Here, we demonstrate that a cobalt-free parent oxide BaFeO_3‑δ (BF), which lacks long-range oxygen-ion diffusion paths, has surprisingly high electrocatalytic activity for ORR. Both in situ high-temperature X-ray diffraction analysis on room-temperature powder and transmission electron microscopy on quenched powder are applied to investigate the crystal structure of BF. Despite the lack of long oxygen-ion diffusion paths, the easy redox of iron cations as demonstrated by thermal gravimetric analysis (TGA) and oxygen temperature-programmed desorption and the high oxygen vacancy concentration as supported by iodometric titration and TGA benefit the reduction of oxygen to oxygen ions. Moreover, the electrical conductivity relaxation technique in conjunction with a transient thermogravimetric study reveals very high surface exchange kinetics of BF oxide. At 700 °C, the area specific resistance of BF cathode, as expressed by a symmetrical cell configuration, is only ∼0.021 Ω cm², and the derived single fuel cell achieves high power output with a peak power density of 870 mW cm^–2. It suggests that an undoped BF parent oxide can be used as a high-efficiency catalyst for ORR

Highly Active and Stable Cobalt-Free Hafnium-doped SrFe_0.9Hf_0.1O_3−δ Perovskite Cathode for Solid Oxide Fuel Cells

Sluggish oxygen reduction reaction (ORR) kinetics and chemical instability of cathode materials hinder the practical application of solid oxide fuel cells (SOFCs). Here we report a Co-free Hf-doped SrFe_0.9Hf_0.1O_3−δ (SFHf) perovskite oxide as a potential cathode focusing on enhancing the ORR activity and chemical stability. We find that SFHf exhibits a high ORR activity, stable cubic crystal structure, and improved chemical stability toward CO₂ poisoning compared to undoped SrFeO_3−δ. The SFHf cathode has a polarization area-specific resistance as low as 0.193 Ω cm² at 600 °C in a SFHf|Sm_0.2Ce_0.8O_1.9 (SDC)|SFHf symmetrical cell and has a maximum power density as high as 1.94 W cm^–2 at 700 °C in an anode-supported fuel cell (Ni+(ZrO₂)_0.92(Y₂O₃)_0.08 (YSZ)|YSZ|SDC|SFHf). The ORR activity maintains stable for a period of 120 h in air and in CO₂-containing atmosphere. The attractive ORR activity is attributed to the moderate concentration of oxygen vacancy and electrical conductivity, as well as the fast oxygen kinetics at the operation temperature. The improved chemical stability is related to the doping of the redox-inactive Hf cation in the Fe site of SrFeO_3−δ by decreasing oxygen vacancy concentration and increasing metal–oxygen bond energy. This work proposes an effective strategy in the design of highly active and stable cathodes for SOFCs

Highly Active and Stable Cobalt-Free Hafnium-doped SrFe_0.9Hf_0.1O_3−δ Perovskite Cathode for Solid Oxide Fuel Cells

Sluggish oxygen reduction reaction (ORR) kinetics and chemical instability of cathode materials hinder the practical application of solid oxide fuel cells (SOFCs). Here we report a Co-free Hf-doped SrFe_0.9Hf_0.1O_3−δ (SFHf) perovskite oxide as a potential cathode focusing on enhancing the ORR activity and chemical stability. We find that SFHf exhibits a high ORR activity, stable cubic crystal structure, and improved chemical stability toward CO₂ poisoning compared to undoped SrFeO_3−δ. The SFHf cathode has a polarization area-specific resistance as low as 0.193 Ω cm² at 600 °C in a SFHf|Sm_0.2Ce_0.8O_1.9 (SDC)|SFHf symmetrical cell and has a maximum power density as high as 1.94 W cm^–2 at 700 °C in an anode-supported fuel cell (Ni+(ZrO₂)_0.92(Y₂O₃)_0.08 (YSZ)|YSZ|SDC|SFHf). The ORR activity maintains stable for a period of 120 h in air and in CO₂-containing atmosphere. The attractive ORR activity is attributed to the moderate concentration of oxygen vacancy and electrical conductivity, as well as the fast oxygen kinetics at the operation temperature. The improved chemical stability is related to the doping of the redox-inactive Hf cation in the Fe site of SrFeO_3−δ by decreasing oxygen vacancy concentration and increasing metal–oxygen bond energy. This work proposes an effective strategy in the design of highly active and stable cathodes for SOFCs