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Aqueous zinc-ion batteries (AZIBs) show enormous potential as a large-scale energy storage technique. However, the growth of Zn dendrites and serious side reactions occurring at the Zn anode hinder the practical application of AZIBs. For the first time, we reported a fluorine-containing surfactant, i.e., potassium perfluoro-1-butanesulfonate (PPFBS), as an additive to the 2 M ZnSO4 electrolyte. Benefitting from its hydrophilic sulfonate anion and hydrophobic long fluorocarbon chain, PPFBS can promote the uniform distribution of Zn2+ flux at the anode/electrolyte interface, allowing the Zn/Zn cell to cycle for 2200 h. Furthermore, PPFBS could inhibit side reactions due to the existence of the perfluorobutyl sulfonate (C4F9SO3−) adsorption layer and the presence of C4F9SO3− in the solvation structure of Zn2+. The former can reduce the amount of H2O molecules and SO42− in contact with the Zn anode and C4F9SO3− entering the Zn2+-solvation structure by replacing SO42−. The Zn/Cu cell exhibits a superior average CE of 99.47% over 500 cycles. When coupled with the V2O5 cathode, the full cell shows impressive cycle stability. This work provides a simple, effective, and economical solution to the common issues of the Zn anode. [ABSTRACT FROM AUTHOR]

[Display omitted] • Utilized machine learning to enhance the development of heat-resistant aluminum alloys. • Designed alloys exhibit outstanding high-temperature performance. • SHAP analysis highlights the critical roles of Ti and V, guiding future designs. • The simplicity and affordability of these alloys suggest significant industrial application potential. The high-temperature strength of aluminum alloys must be enhanced for improving their applicability across industries. This study proposes a machine learning approach for developing heat-resistant aluminum alloys. Using a combination of correlation-based screening and genetic algorithms, feature selection was performed on descriptors derived from the atomic compositions of alloys. Then, alloy compositions and descriptors were used as input variables of the model to improve its robustness and applicability due to the richness of information. Four distinct alloys were discovered by employing Bayesian optimization within the framework of a quaternary alloy system. The best alloy demonstrated an exceptional high-temperature strength of 175 MPa at 300 °C in the absence of heat treatment. Microstructural analyses of these alloys indicated the critical role of vanadium-rich intermetallics in enhancing the high-temperature strength of aluminum alloys. Furthermore, the output of the model was explained using the SHapley Additive exPlanations method. The findings emphasize the critical importance of titanium and vanadium in enhancing the high-temperature strength of aluminum alloys tailored for environments with high thermal stress. [ABSTRACT FROM AUTHOR]

Seaweed has attracted great attention as a healthy and nutritious food. Traditional seaweed processing plants mainly rely on manual visual inspection to identify and remove inferior seaweed. Accurate and rapid harvest period classification and impurity detection are key to improving productivity and processing speed in seaweed processing plants. Although many studies on seaweed have been conducted in laboratory environments, currently, the plants lack effective tools to obtain real-time and reliable information on seaweed quality. To address this challenge, the deep transfer learning-based computer vision was applied to identify inferior seaweeds, including those from the third harvest, fourth harvest, and impure seaweeds in this work. Specifically, YOLOv8 and YOLOv5 were utilized as base transfer learning models. By loading various pre-trained weight files, this study was able to automatically classify Porphyra haitnensis into four categories based on the harvest period and simultaneously detect four types of common impurities in it. Among the tested models, YOLOv8n-cls achieved the best trade-off in classifying the harvest period, with a Top-1 accuracy of 93.5%. This represented a significant improvement of 16% compared to the performance without transfer learning. The detection speed for a single image was 8.2 ms, and the model size was only 2.82 Mb. On the other hand, YOLOv8n exhibited outstanding performance in impurity detection, with a mean average precision of 99.14%, a single image detection speed of 4.3 ms, and a model size of 5.95 Mb. The results demonstrated the potential of YOLOv8 with transfer learning to objectively assist or even replace decision-making by assembly line workers. This study will not only enhance the quality control, production efficiency, and economic benefits of the seaweed processing industry but also drive the automation equipment and systems of seaweed-related enterprises towards greater intelligence and efficiency.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it.Graphical abstract: Seaweed has attracted great attention as a healthy and nutritious food. Traditional seaweed processing plants mainly rely on manual visual inspection to identify and remove inferior seaweed. Accurate and rapid harvest period classification and impurity detection are key to improving productivity and processing speed in seaweed processing plants. Although many studies on seaweed have been conducted in laboratory environments, currently, the plants lack effective tools to obtain real-time and reliable information on seaweed quality. To address this challenge, the deep transfer learning-based computer vision was applied to identify inferior seaweeds, including those from the third harvest, fourth harvest, and impure seaweeds in this work. Specifically, YOLOv8 and YOLOv5 were utilized as base transfer learning models. By loading various pre-trained weight files, this study was able to automatically classify Porphyra haitnensis into four categories based on the harvest period and simultaneously detect four types of common impurities in it. Among the tested models, YOLOv8n-cls achieved the best trade-off in classifying the harvest period, with a Top-1 accuracy of 93.5%. This represented a significant improvement of 16% compared to the performance without transfer learning. The detection speed for a single image was 8.2 ms, and the model size was only 2.82 Mb. On the other hand, YOLOv8n exhibited outstanding performance in impurity detection, with a mean average precision of 99.14%, a single image detection speed of 4.3 ms, and a model size of 5.95 Mb. The results demonstrated the potential of YOLOv8 with transfer learning to objectively assist or even replace decision-making by assembly line workers. This study will not only enhance the quality control, production efficiency, and economic benefits of the seaweed processing industry but also drive the automation equipment and systems of seaweed-related enterprises towards greater intelligence and efficiency.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it.In order to provide consumers with high-quality seaweed, it is necessary to identify and remove inferior seaweed in seaweed processing factories, such as those from the third harvest, fourth harvest, and impure seaweed. This study employs the deep transfer learning-based YOLOv8 to automatically classify seaweed from different harvest periods and detect impurities in it. [ABSTRACT FROM AUTHOR]

The rate of serious infection (1.0% in AS and 1.8% in PsA) and candida infection (0.4% in AS and 0.9% in PsA) remained stable at the fifth year. Ixekizumab, an interleukin-17A specific monoclonal antibody, for the treatment of biologic-naive patients with active psoriatic arthritis: results from the 24-week randomised, double-blind, placebo-controlled and active (adalimumab)-controlled period of the phase III trial SPIRIT-P1. Of special interest, candida infections (genital candidiasis, oral candidiasis and candida thrush infection) were reported. The efficacy of IL-17 inhibitor (IL-17i) in inflammatory arthritis (IA) including ankylosing spondylitis (AS) and psoriatic arthritis (PsA) has been ascertained in large-scale clinical trials, while safety, especially infections, should be further addressed and paid more attention to by clinicians in long-term administration. [Extracted from the article]

Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real‐time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone‐based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre‐training and fine‐tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin‐Tiny (Swin‐T) achieved the highest trade‐off performance, with a Top‐1 accuracy of 0.997, a detection speed of 3.2 ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin‐T model was also capable of distinguishing between different types of hydrocolloids with a Top‐1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real‐time quantitative information about meat adulteration anytime, anywhere. Practical Application: This research provides a practical solution for regulators and consumers to non‐destructively and quantitatively detect the content and type of hydrocolloids in beef in real‐time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments. [ABSTRACT FROM AUTHOR]

This case report discusses a female patient with axial spondyloarthritis (AS) who developed inflammatory bowel disease (IBD) after being treated with secukinumab (SEC), an interleukin-17 inhibitor. The patient had a family history of AS and psoriasis, and her symptoms worsened after starting SEC treatment. She experienced upper abdominal pain, nausea, vomiting, and gastrointestinal bleeding. The report highlights the association between SEC and the exacerbation of gastrointestinal symptoms leading to IBD. It also mentions the low incidence rate of IBD in patients treated with SEC and the lack of reports on neurological damage associated with SEC use. [Extracted from the article]