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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]

Low‐temperature, ambient processing of high‐quality CsPbBr3 films is demanded for scalable production of efficient, low‐cost carbon‐electrode perovskite solar cells (PSCs). Herein, we demonstrate a crystal orientation engineering strategy of PbBr2 precursor film to accelerate its reaction with CsBr precursor during two‐step sequential deposition of CsPbBr3 films. Such a novel strategy is proceeded by adding CsBr species into PbBr2 precursor, which can tailor the preferred crystal orientation of PbBr2 film from [020] into [031], with CsBr additive staying in the film as CsPb2Br5 phase. Theoretical calculations show that the reaction energy barrier of (031) planes of PbBr2 with CsBr is lower about 2.28 eV than that of (020) planes. Therefore, CsPbBr3 films with full coverage, high purity, high crystallinity, micro‐sized grains can be obtained at a low temperature of 150 °C. Carbon‐electrode PSCs with these desired CsPbBr3 films yield the record‐high efficiency of 10.27% coupled with excellent operation stability. Meanwhile, the 1 cm2 area one with the superior efficiency of 8.00% as well as the flexible one with the champion efficiency of 8.27% and excellent mechanical bending characteristics are also achieved. [ABSTRACT FROM AUTHOR]