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Dry-aging is the aerobic aging process where meat carcasses or primal cuts are hanged and aged for a period of time (28 to 55 days, or even longer) under specific environmental conditions of temperature (0–4 ℃), relative humidity (61%–85%), and air flow (0.5–2.0 m/s). Dry-aging in a breathable bag, dry-aging combined with ultraviolet irradiation treatment, and stepwise dry/wet-aging have been successively developed. Due to dehydration during dry-aging, the surface of meat shrinks to produce a hard ‘crust’, resulting in significant mass loss and trim loss. However, compared with wet-aging, dry-aging can significantly enhance meat flavor characteristics, imparting a strong ‘dry-aged flavor’ and unique roasted beef-like, roasted nutty and buttery aromas to aged meat. Dry-aging can be used effectively to improve the eating quality and economic value of low-marbled beef. In this paper, the key parameters of dry-aging are reviewed, the potential mechanism by which dry-aging improves meat quality, especially flavor, is discussed. Future prospects for the application and development of dry-aging are discussed as well. This review will provide theoretical support for the development of the meat industry and for exploring the market for high-quality meat.

The objective of this study was to conduct a comprehensive comparison of the effects of hip and knee strengthening training in patients with patellofemoral pain (PFP). A meta-analysis was conducted to investigate the effects of these two types of strengthening training on patients' lower limb biomechanics, knee pain and function. The aim was to evaluate the effectiveness of the two training modalities and provide evidence-based recommendations for the rehabilitation of patients with PFP. A total of 12 studies were identified through a search of the Web of Science, EBSCO, and PubMed databases. The selected studies comprised nine randomized controlled trials (RCTs), one comparative controlled trial (CCTs) and two cohort studies (CSs), with a total of 1,066 patients. The quality of the included studies was evaluated via the PEDro scale, and a meta-analysis was conducted via Stata18 software. The results show that both types of strengthening training positively impact pain reduction and improved knee function in PFP patients. Moderate evidence from meta-analyses indicated that hip strengthening training (SMD = −1.740, 95%; CI −2.212 to −1.267, P < 0.001) was more effective than knee strengthening training (SMD = −1.302, 95%; CI −1.75 to −0.86, P < 0.001) in reducing pain (VAS). Similarly, Strong evidence suggests that hip strengthening training (SMD = 1.205, 95%; CI 0.968 to 1.443, P < 0.001) was significantly more effective than knee strengthening training (SMD = 1.023, 95%; CI 0.722 to 1.325, P < 0.001) in improving knee function (AKPS). Additionally, moderate evidence suggests that hip strengthening training significantly increased hip abductor strength (SMD = 0.848, 95%; CI 0.508–1.187, P < 0.001) and external rotator strength (SMD = 0.780, 95%; CI 0.416–1.145, P < 0.001), while strong evidence suggests that knee strengthening training did not significantly enhance knee extensor strength (SMD = 0.212, 95%; CI −0.014 to 0.439, P = 0.066). Therefore, clinicians should use hip strengthening as one of the primary training interventions when treating patients with PFP. [ABSTRACT FROM AUTHOR]

Structural damage caused to under-vehicle equipment under adverse working conditions affects the safe operation of high-speed electric multiple units (EMUs). To address the low optimization efficiency and take into account vibration fatigue in traditional under-vehicle equipment design, herein, a multi-objective optimization design method (PB-MNSGA-II) that combines particle swarm optimization-back propagation (PSO-BP) neural network and modified non-dominated sorting Genetic Algorithm II (MNSGA-II) was proposed. It can help engineers choose better equipment component thickness and achieve efficient and accurate design. First, MNSGA-II, the core content of PB-MNSGA-II, was proposed, which improved four aspects of NSGA-II: Latin hypercube sampling population initialization, adaptive crossover and mutation probability, mutation interference of Levy flight strategy, and optimal elite strategy. Then, zero-ductility transition (ZDT) series functions were employed to test MNSGA-II, thereby verifying its convergence and computational efficiency. Subsequently, utilizing the frame structure of the traction transformer (TTR) as an illustrative example, the PB-MNSGA-II approach was employed to enable multi-objective optimization. During this optimization process, the focus was on random vibration fatigue damage, thus enhancing the rationality of the optimization outcomes. After structural optimization, the mass of the TTR decreased by 20.1 kg, and the maximum vibration fatigue damage value decreased from 1.09 to 0.808, verifying the effectiveness and feasibility of the proposed method. The findings of this study provide important insights for the multi-objective optimization design of under-vehicle equipment under complex operating conditions. [ABSTRACT FROM AUTHOR]

The article introduces the Disentangled Dynamic Graph Attention Network (I-DIDA) for handling out-of-distribution challenges in sequential recommendation systems. Topics discussed include the role of Dynamic Graph Neural Networks in sequential recommendation, the proposed disentangled spatio-temporal attention mechanism for capturing invariant patterns, and the use of intervention and environment inference to enhance model robustness under distribution shifts.

This paper takes Fenghe River in Langfang City as the research object to conduct river health evaluation,which is expected to provide a direction for the future health management of Fenghe River and achieve the goal of healthy development of the water ecology of local rivers.On the basis of the relatively perfect evaluation standard,the scientific and reasonable health evaluation of Fenghe River is carried out by the item-by-item evaluation method and the AHP-fuzzy mathematical model,which is suitable for the local actual situation.An evaluation system is constructed,which consists of four criterion layers covering “basin”,“water”,“biology”,and social service functions and 12 subordinate index layers.The evaluation results show that Fenghe River is healthy according to the item-by-item evaluation method.In the evaluation of the AHP-fuzzy mathematical model,Fenghe River is “very healthy”,with a grade of 0.411 0.By this method,the grade of “healthy” is 0.343 8,and the grades of 0.223 9,0.021 3,and 0 denote the health status of “sub-healthy”,“unhealthy”,and “bad”,respectively.The Fenghe River Basin in Langfang Economic and Technological Development Zone has poor performance in terms of riparian bandwidth,sediment pollutants,and water quality.The proposed evaluation method can provide a reference for the health evaluation of rivers heavily influenced by cities and artificial interventions.

Globally, a significant amount of food waste is generated annually, representing a considerable potential resource. Anaerobic digestion for producing methane is the most effective method for utilizing food waste resources. However, the by-product, food waste digestate (FWD), is rich in C/N/P. Direct discharge of FWD may cause significant environmental burdens and lead to the loss of valuable resources. In this study, we established a system to convert FWD into the high-value agricultural antibiotic streptothricin F through biological fermentation by Streptomyces lavendulae and simulated moving bed (SMB) separation for energy and resource recovery. After biological fermentation, the concentration of streptothricin F in the co-fermentation of FWD and soluble starch by Streptomyces lavendulae reached 0.5144 g L−1, and its concentration was 60.51 times higher than in the control. The COD conversion rate reached 82.50% after biological fermentation, achieving high-value utilization and harmless treatment of FWD. Streptothricin F was effectively separated using SMB technology, achieving a high purity of 97.47% and a recovery rate of 91.16%. Additionally, streptothricin F showed inhibitory effects against Alternaria, Fusarium oxysporum, and Colletotrichum, indicating its broad-spectrum antifungal properties. The pure streptothricin F obtained through SMB separation against Alternaria had an EC50 value of 0.66 μg mL−1, representing an 81.24 times improvement in antifungal activity compared to the unpurified fermentation broth (53.62 μg mL−1). Life Cycle Assessment (LCA) results indicated that the system had a relatively low environmental impact. This research presents a novel strategy for the high-value utilization of food waste digestate, advancing the technological level of biomass utilization. [ABSTRACT FROM AUTHOR]

The goal of the multi-focus image fusion (MFIF) task is to merge images with different focus areas into a single clear image. In real world scenarios, in addition to varying focus attributes, there are also exposure differences between multi-source images, which is an important but often overlooked issue. To address this drawback and improve the development of the MFIF task, a new image fusion dataset is introduced called EDMF. Compared with the existing public MFIF datasets, it contains more images with exposure differences, which is more challenging and has a numerical advantage. Specifically, EDMF contains 1000 pairs of color images captured in real-world scenes, with some pairs exhibiting significant exposure difference. These images are captured using smartphones, encompassing diverse scenes and lighting conditions. Additionally, in this paper, a baseline method is also proposed, which is an improved version of memory unit-based unsupervised learning. By incorporating multiple adaptive memory units and spatial frequency information, the network is guided to focus on learning features from in-focus areas. This approach enables the network to effectively learn focus features during training, resulting in clear fused images that align with human visual perception. Experimental results demonstrate the effectiveness of the proposed method in handling exposure difference, achieving excellent fusion results in various complex scenes. [ABSTRACT FROM AUTHOR]

High‐quality epitaxial graphene is prepared on semi‐insulated 4H‐SiC (0001) by ultra‐high vacuum thermal decomposition method and used in graphene/SiC/graphene ultraviolet‐visible dual‐band photodetectors. The dual‐band detector exhibits an extremely low dark current (5.2 × 10−14 A) and a peak responsivity of 1.17 A W−1 corresponding to an external quantum efficiency of 518%. A high detectivity of 3.14 × 1014 Jones is achieved under 280 nm light illumination at 30 V, while a high response speed is obtained with a rise time of 25.17 ns and a decay time of 540.10 ns. The detector shows a responsivity of 1.4 × 10−5 A W−1 and a detectivity of 6.5 × 109 Jones under 430 nm light illumination. The dual‐band detector equipped with SiC grating and asymmetrical graphene electrodes is demonstrated for high‐performance optoelectronic logic "AND" gate with high detectivity at 280 and 430 nm. [ABSTRACT FROM AUTHOR]