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Hydrogen energy has been recognized as the most promising secondary energy source due to high energy density, abundance, and environmental friendliness. Among hydrogen production techniques, water electrolysis has emerged as a key research focus, owing to its high efficiency, operational simplicity, controllability, and pollution-free nature. However, the anodic oxygen evolution reaction (OER) involves a high overpotential and sluggish kinetics, which severely constrain the overall efficiency of water electrolysis. Transition metal oxide (TMO) catalysts are regarded as promising substitutes for noble-metal-based catalysts, given their advantages of low cost, elemental abundance, tunable electronic structures, and favorable stability. This review systematically elaborates on the reaction mechanisms of TMO catalysts, including the adsorbate evolution mechanism (AEM) and lattice oxygen mechanism (LOM), and summarizes various performance-enhancement strategies, such as morphology control, doping engineering, support engineering, and heterostructure construction. Furthermore, it outlines current challenges and future research directions, covering precise synthesis and structural control, identification of active sites and mechanistic elucidation, and stability and degradation issues, as well as multifunctional applications and broad-pH-range adaptability. The aim is to offer theoretical guidance and technical insights for designing and developing high-performance TMO electrocatalysts.

Iron accumulates in the brain during aging, which catalyzes radical formation, causing neuronal impairment, and is thus considered a pathogenic factor in Alzheimer's disease (AD). To scavenge excess iron-catalyzed radicals and thereby protect the brain and decrease the incidence of AD, we synthesized a soluble pro-iron 5-YHEDA peptide. However, the blood-brain barrier (BBB) blocks large drug molecules from entering the brain and thus strongly reduces their therapeutic effects. However, alternative receptor- or transporter-mediated approaches are possible. A low-density lipoprotein receptor (LDLR)-binding segment of Apolipoprotein B-100 was linked to the 5-YHEDA peptide (bs-5-YHEDA) and intracardially injected into senescent (SN) mice that displayed symptoms of cognitive impairment similar to those of people with AD. We successfully delivered 5-YHEDA across the BBB into the brains of the SN mice via vascular epithelium LDLR-mediated endocytosis. The data showed that excess brain iron and radical-induced neuronal necrosis were reduced after the bs-5-YHEDA treatment, together with cognitive amelioration in the SN mouse, and that the senescence-associated ferritin and transferrin increase, anemia and inflammation reversed without kidney or liver injury. bs-5-YHEDA may be a mild and safe iron remover that can cross the BBB and enter the brain to relieve excessive iron- and radical-induced cognitive disorders.

Human pose estimation aims to localize the body joints from image or video data. With the development of deep learning, pose estimation has become a hot research topic in the field of computer vision. In recent years, human pose estimation has achieved great success in multiple fields such as animation and sports. However, to obtain accurate positioning results, existing methods may suffer from large model sizes, a high number of parameters, and increased complexity, leading to high computing costs. In this paper, we propose a new lightweight feature encoder to construct a high-resolution network that reduces the number of parameters and lowers the computing cost. We also introduced a semantic enhancement module that improves global feature extraction and network performance by combining channel and spatial dimensions. Furthermore, we propose a dense connected spatial pyramid pooling module to compensate for the decrease in image resolution and information loss in the network. Finally, our method effectively reduces the number of parameters and complexity while ensuring high performance. Extensive experiments show that our method achieves a competitive performance while dramatically reducing the number of parameters, and operational complexity. Specifically, our method can obtain 89.9% AP score on MPII VAL, while the number of parameters and the complexity of operations were reduced by 41% and 36%, respectively.

In order to be able to quickly locate the position of the gap of an industrial device in the industrial inspection process, a fast gap detection algorithm based on machine vision is proposed by using the feature of a high gray level difference between the image gap and the background in machine vision. First, nonlinear equalization preprocessing is adopted, and then multiple one-dimensional functions are composed based on the sum of the gray values of the horizontal axis as well as the differentiation, and then the proximity position averaging operation is done after obtaining the positional information through the extreme value and the most value operation. The algorithm transforms the two-dimensional image into a combination of multiple one-dimensional functions with related features, and finally accurate position information is obtained by analysing and fusing the one-dimensional functions. Multiple discriminative bases are used for the same data source to ensure the accuracy of the results. Experiments show that the method proposed is faster and less error-prone, has higher accuracy and detection efficiency, and meets the needs of industrial real-time detection, compared with the traditional Hough transform algorithm and the LSD detection algorithm.

The use of solar energy to convert CO[sub.2] into value-added chemicals is a promising sustainable development strategy. In this study, a black graphitic carbon nitride (CN-B) photocatalyst was fabricated through a single-step calcination process, employing phloxine B and urea as the precursor materials. The catalysts were characterized using TEM, XRD, FTIR, XPS and so on. The amount of prepolymer phloxine B was 25 mg, 35 mg and 45 mg, respectively, and the obtained samples were CN-B-0.025, CN-B-0.035 and CN-B-0.045. All samples were used for visible-catalyzed CO[sub.2] reduction. The experimental findings indicate that the CO evolution rate of the optimal photocatalyst CN-B-0.035 reaches 27.56 μmol g[sub.cat.] [sup.−1] h[sup.−1]. This value is nine-fold higher than that of pure CN, which has a CO evolution rate of 3.22 μmol g[sub.cat.] [sup.−1] h[sup.−1]. The excellent photocatalytic reduction performance is due to the following factors: Firstly, the exceedingly thin nanosheet structure of the catalyst enhances the velocity of the charge transfer, and transmission electron microscopy (TEM) analysis shows that the nanosheet thickness of the catalyst CN-B is significantly thinner. Secondly, the light absorption capacity of the catalyst is enhanced. The absorbance of CN-B increases significantly in the ultraviolet region and extends to the near-infrared region, as shown with UV diffuse reflection spectroscopy. Finally, the photothermal effect of CN-B causes the catalyst temperature to rise rapidly from 20 °C to 131 °C within 120 s, which further promotes photogenerated carrier separation. This research offers a novel approach to the development of photocatalysts aimed at the photothermal-assisted photocatalytic conversion of CO[sub.2].

The use of solar energy to convert CO2 into value-added chemicals is a promising sustainable development strategy. In this study, a black graphitic carbon nitride (CN-B) photocatalyst was fabricated through a single-step calcination process, employing phloxine B and urea as the precursor materials. The catalysts were characterized using TEM, XRD, FTIR, XPS and so on. The amount of prepolymer phloxine B was 25 mg, 35 mg and 45 mg, respectively, and the obtained samples were CN-B-0.025, CN-B-0.035 and CN-B-0.045. All samples were used for visible-catalyzed CO2 reduction. The experimental findings indicate that the CO evolution rate of the optimal photocatalyst CN-B-0.035 reaches 27.56 μmol gcat.−1 h−1. This value is nine-fold higher than that of pure CN, which has a CO evolution rate of 3.22 μmol gcat.−1 h−1. The excellent photocatalytic reduction performance is due to the following factors: Firstly, the exceedingly thin nanosheet structure of the catalyst enhances the velocity of the charge transfer, and transmission electron microscopy (TEM) analysis shows that the nanosheet thickness of the catalyst CN-B is significantly thinner. Secondly, the light absorption capacity of the catalyst is enhanced. The absorbance of CN-B increases significantly in the ultraviolet region and extends to the near-infrared region, as shown with UV diffuse reflection spectroscopy. Finally, the photothermal effect of CN-B causes the catalyst temperature to rise rapidly from 20 °C to 131 °C within 120 s, which further promotes photogenerated carrier separation. This research offers a novel approach to the development of photocatalysts aimed at the photothermal-assisted photocatalytic conversion of CO2.

The effect of five constant temperatures (16, 20, 24, 28 and 32°C) on the development, survival and reproduction of Neoseiulus barkeri Hughes fed on Aleuroglyphus ovatus Toupeau (Acari: Acaridae) was examined in the laboratory at 85% relative humidity. Development time of different immature stages decreased with increasing temperature, total egg-to-adult development time varied from 5.0 ± 0.13 to 17.5 ± 0.29 days. The lower thermal threshold for development was 9.7 ± 2.48°C and the thermal constant from egg to adult was 111.1 ± 12.34 degree-days. Pre- and post-oviposition period and female longevity all shortened as temperature increased. The longest oviposition period was observed at 24°C with 20.4 ± 1.13 days. At 20, 24, 28 and 32°C, mated females laid on average 0.7 ± 0.08, 1.5 ± 0.04, 1.6 ± 0.11 and 1.5 ± 0.11 eggs per day, respectively, but no eggs were laid at 16°C. Both the maximum fecundity (30.9 eggs per female) and the highest intrinsic rate of increase (r m = 0.166) were obtained at 28°C. The results of this study indicated that a mass rearing of N. barkeri with A. ovatus as prey is feasible at the appropriate temperature.

Different concentrations of PAHs could promote the synthesis and photosynthesis of chlorophyll in cucumber and Chinese cabbage leaves and the vitamin C content in radish. PAHs are difficult to degrade in the environment. Because of their semi-volatile and fat-soluble nature, PAHs can be absorbed into plant bodis through plant leaves or into the soil by atmospheric deposition, and migrate, metabolize and accumulate in plants, thereby harming human health through the food chain'7'. [...]the surface morphology of the plant leaves, the chemical composition of the epidermis wax, the number and distribution of stomata and the growth of hair are different, and the interaction process between plants and air is different'12'. [...]The absorption rate of PAHs by a plant is directly proportional to the adaptation of the plant itself and the bioavailability of PAHs'13'. PAHs pollution is an urgent environmental problem facing the world. Because of their carcinogenic, teratogenic and mutagenic effects, they have a serious impact on human health and have attracted people's attention.

The effect of five constant temperatures (16, 20, 24, 28 and 32°C) on the development, survival and reproduction of Neoseiulus barkeri Hughes fed on Aleuroglyphus ovatus Toupeau (Acari: Acaridae) was examined in the laboratory at 85% relative humidity. Development time of different immature stages decreased with increasing temperature, total egg-to-adult development time varied from 5.0 ± 0.13 to 17.5 ± 0.29 days. The lower thermal threshold for development was 9.7 ± 2.48°C and the thermal constant from egg to adult was 111.1 ± 12.34 degree-days. Pre- and post-oviposition period and female longevity all shortened as temperature increased. The longest oviposition period was observed at 24°C with 20.4 ± 1.13 days. At 20, 24, 28 and 32°C, mated females laid on average 0.7 ± 0.08, 1.5 ± 0.04, 1.6 ± 0.11 and 1.5 ± 0.11 eggs per day, respectively, but no eggs were laid at 16°C. Both the maximum fecundity (30.9 eggs per female) and the highest intrinsic rate of increase ( r m  = 0.166) were obtained at 28°C. The results of this study indicated that a mass rearing of N. barkeri with A. ovatus as prey is feasible at the appropriate temperature.

Ambulatory electrocardiogram (ECG) readings are prone to mixed noise from physical activities, including baseline wander (BW), muscle artifact (MA), and electrode motion artifact (EM). Developing a method to remove such complex noise and reconstruct high-fidelity signals is clinically valuable for diagnostic accuracy. However, denoising of multi-beat ECG segments remains understudied and poses technical challenges. To address this, we propose Time-Frequency Complementary Diffusion (TFCDiff), a novel approach that operates in the Discrete Cosine Transform (DCT) domain and uses the DCT coefficients of noisy signals as conditioning input. To refine waveform details, we incorporate Temporal Feature Enhancement Mechanism (TFEM) to reinforce temporal representations and preserve key physiological information. Comparative experiments on a synthesized dataset demonstrate that TFCDiff achieves state-of-the-art performance across five evaluation metrics. Furthermore, TFCDiff shows superior generalization on the unseen SimEMG Database, outperforming all benchmark models. Notably, TFCDiff processes raw 10-second sequences and maintains robustness under flexible random mixed noise (fRMN), enabling plug-and-play deployment in wearable ECG monitors for high-motion scenarios. Source code is available at https://github.com/Miroircivil/TFCDiff.