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Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wavelet-based feature extraction has demonstrated effectiveness in deep learning (DL)-based ECG diagnosis, selecting the optimal wavelet base poses a significant challenge, as it directly influences feature quality and diagnostic accuracy. Traditional methods typically rely on fixed wavelet bases chosen heuristically or through trial-and-error, which can fail to cover the distinct characteristics of individual ECG signals, leading to suboptimal performance. To address this limitation, we propose a reinforcement learning-based wavelet base selection (RLWBS) framework that dynamically customizes the wavelet base for each ECG signal. In this framework, a reinforcement learning (RL) agent iteratively optimizes its wavelet base selection (WBS) strategy based on successive feedback of classification performance, aiming to achieve progressively optimized feature extraction. Experiments conducted on the clinically collected PTB-XL dataset for ECG abnormality classification show that the proposed RLWBS framework could obtain more detailed time-frequency representation of ECG signals, yielding enhanced diagnostic performance compared to traditional WBS approaches.

International trade has a significant impact on global environmental quality and sustainable economic development. Global value chains (GVCs) have become a crucial component of international trade and development policy. The global production structure has become more complicated with the inclusion of domestic markets in GVC, putting significant pressure on world energy resources and environmental sustainability. Therefore, traditional trade measures no longer accurately reflect how global trade affects the energy security of developing and developed countries. Thus, this study is the first to use a panel-corrected standard error method to look at the relationship between GVC participation and energy security by using a global sample of 35 developed and 27 developing nations from 1995 to 2018. A feasible generalized least squares model was also applied to confirm the robustness of the model. Six indicators—foreign direct investment, industrialization level, capital formation, human capital index, political stability, and GVC—were used in this research to look at their impact on the four fundamental pillars of energy security (availability, applicability, sustainability, and affordability) for sustainable economic development. For developed countries, it was confirmed that there is a non-linear relationship between GVC participation and energy intensity, renewable energy consumption, and non-fossil fuel use. In the case of developing countries, the non-linear relationship in terms of all aspects of energy security was also confirmed. The findings also indicated that GVC’s involvement benefits all four dimensions of energy security in both developing and developed countries once it reaches a certain threshold. Our findings further support the impacts of long-term cointegration between GVC and energy security for sustainable economic development. Therefore, the nations must promote technology transfer and capacity building within GVCs for inclusive energy security. Similarly, they may foster sustainable practices through collaborative governance for a stable global energy network by acknowledging the positive impact of income levels on energy security.

Background Endoplasmic reticulum (ER)-associated protein degradation (ERAD) is a critical component of the ER-mediated protein quality control (ERQC) system and plays a vital role in plant stress responses. However, the ubiquitination machinery underlying plant ERAD—particularly the ubiquitin-conjugating enzymes (E2s)—and their contributions to stress tolerance remain poorly understood. Results In this study, we identified UBC32, UBC33, and UBC34 as ER-localized ubiquitin E2 enzymes involved in ERAD and demonstrated their roles in biotic and abiotic stress tolerance in tomato ( Solanum lycopersicum ) and Arabidopsis ( Arabidopsis thaliana ). In response to biotic stress, UBC33 and UBC34 collectively contribute more substantially than UBC32 to plant immunity against Pseudomonas syringae pv. tomato ( Pst ). Under abiotic stress and ER stress induced by tunicamycin (TM), all three E2s play important roles. Notably, mutation of UBC32 enhances tolerance to TM-induced ER stress, whereas the loss of function in UBC33 or UBC34 suppresses this response. Additionally, UBC32, UBC33, and UBC34 act synergistically in Arabidopsis seed germination under salt stress and abscisic acid (ABA) treatment. While the single mutants atubc32 , atubc33 , and atubc34 exhibit germination rates comparable to Col-0 under salt stress or ABA treatment, the double mutants atubc32/33 , atubc32/34 , and atubc33/34 show a significantly greater reduction in germination rate. Interestingly, the atubc32/33/34 triple mutant exhibits a seed germination rate under salt stress and ABA treatment, as well as a level of host immunity to Pst , comparable to that of the atubc33/34 and atubc32/34 double mutants. Conclusions Our findings establish UBC32, UBC33, and UBC34 as key components of the plant ERAD machinery, contributing to plant tolerance to both abiotic and biotic stress. Despite their close phylogenetic relationship, these E2 enzymes exhibit redundant, synergistic, or antagonistic roles depending on the specific stress response pathway, underscoring the complexity of their functional interactions.

In exploiting large propagation delays in underwater acoustic (UWA) networks, the time-domain interference alignment (TDIA) mechanism aligns interference signals through delay-aware slot scheduling, creating additional idle time for improved transmission at the medium access control (MAC) layer. However, perfect alignment remains challenging due to arbitrary delays. This study enhances TDIA by incorporating power allocation into its transmission scheduling framework across the physical and MAC layers, following the cross-layer design principle. The proposed quasi-interference alignment (QIA) mechanism enables controlled interference on useful signals by jointly optimizing the transmission schedule and power. The formulated optimization problem to maximize network throughput is divided into two sub-problems: one for coarse slot scheduling and another for refining both scheduling and power allocation. The simulation results validate the QIA framework’s superiority over the traditional TDIA and genetic algorithm benchmarks.

Biological sodium channels ferry sodium ions across the lipid membrane while rejecting potassium ions and other metal ions. Realizing such ion selectivity in an artificial solid-state ionic device will enable new separation technologies but remains highly challenging. In this work, we report an artificial sodium-selective ionic device, built on synthesized porous crown-ether crystals which consist of densely packed 0.26-nm-wide pores. The Na + selectivity of the artificial sodium-selective ionic device reached 15 against K  +  , which is comparable to the biological counterpart, 523 against Ca 2 +  , which is nearly two orders of magnitude higher than the biological one, and 1128 against Mg 2 +  . The selectivity may arise from the size effect and molecular recognition effect. This work may contribute to the understanding of the structure-performance relationship of ion selective nanopores. Artificial sodium channels open up the way to new separation technologies but remains highly challenging. In this work, the authors report an artificial sodium-selective ionic device, built on porous crown-ether crystals with a sodium ion selectivity against calcium ions exceeding that one of biological ion channel counterparts.

To enhance the efficiency of mechanical automatic picking of cherry tomatoes in a precision agriculture environment, this study proposes an improved target detection algorithm based on YOLOv5n. The improvement steps are as follows: First, the K-means++ clustering algorithm is utilized to update the scale and aspect ratio of the anchor box, adapting it to the shape characteristics of cherry tomatoes. Secondly, the coordinate attention (CA) mechanism is introduced to expand the receptive field range and reduce interference from branches, dead leaves, and other backgrounds in the recognition of cherry tomato maturity. Next, the traditional loss function is replaced by the bounding box regression loss with dynamic focusing mechanism (WIoU) loss function. The outlier degree and dynamic nonmonotonic focusing mechanism are introduced to address the boundary box regression balance problem between high-quality and low-quality data. This research employs a self-built cherry tomato dataset to train the target detection algorithms before and after the improvements. Comparative experiments are conducted with YOLO series algorithms. The experimental results indicate that the improved model has achieved a 1.4% increase in both precision and recall compared to the previous model. It achieves an average accuracy mAP of 95.2%, an average detection time of 5.3 ms, and a weight file size of only 4.4 MB. These results demonstrate that the model fulfills the requirements for real-time detection and lightweight applications. It is highly suitable for deployment in embedded systems and mobile devices. The improved model presented in this paper enables real-time target recognition and maturity detection for cherry tomatoes. It provides rapid and accurate target recognition guidance for achieving mechanical automatic picking of cherry tomatoes.

The purpose of this study is to evaluate the efficacy of plate augmentation and hybrid bone grafting for treating atrophic nonunion of the femur with original intramedullary nail retained in situ.In this study, 36 patients with atrophic nonunion of the femur who underwent surgery using the technique of plate augmentation and a hybrid bone grafting while retaining the original intramedullary nail in situ in Xi’an Honghui Hospital from January 2019 to December 2021 were enrolled. 28 patients who met the inclusion and exclusion criteria were ultimately included in the study. These 28 patients, consisting of 20 males and 8 females with a mean age of 38 years, were evaluated based on factors such as operation time, intraoperative blood loss, the average hospitalization days. Additionally, the results and function of these patients were evaluated by union time, Wu’s scores of limb function and incidence of serious complications.All 28 patients achieved bone union at the 12 month follow-up, with an average follow-up time of 14.6 ± 4.2 months.The average operation time was 68.3 ± 11.2 min, and the average intraoperative blood loss was 140 ± 22.6 ml. Patients were hospitalized for an average of 5.8 ± 1.1 days. Full clinical and radiological bone union was achieved on average at 5.1 ± 1.9 months. The mean value of Wu's scores at the 12 month follow-up was significantly higher than before the operation. Limb function was excellent in 27 patients and good in one patient at the 12 month follow-up. However, five patients experienced the lower limb vein thrombosis, including one deep vein thrombosis and four lower limb intermuscular vein thromboses. One patient had a superficial infections of the surgical incision site, while three patients reported pain and numbness where their iliac bone graft was extracted at the 12 month follow-up. The technique of plate augmentation and hybrid bone grafting, combined with retaining the original intramedullary nail in situ has been shown to be a safe, effective, simply and standardizable practice for treating atrophic femoral nonunion with an intact original IMN fixation.

Purpose This retrospective study aimed to compare the efficacy of augmented plate fixation with autogenous hybrid bone grafting (Group A) and vertical double-plate fixation with autogenous bone grafting through channels after removal of the original intramedullary nail (IMN) (Group B) for the treatment of atrophic nonunion of femoral shaft after IMN. Methods In the retrospective study, 56 patients who were diagnosed atrophic femoral nonunion after IMN fixation were enrolled and evaluated from January 2019 to July 2024 in Xi’an Honghui Hospital. All cases were fixed with either group A ( n  = 29) or group B ( n  = 27). Operation time, total blood loss, visual analog scale (VAS) scores, time to union, union rate, and the surgical complications were compared between the two groups. Results The results showed that Group A had a shorter operation time (96.2 ± 22.9 min vs. 122.4 ± 30.6 min;  p  < 0.001), less total blood loss (320.5 ± 98.7 m vs. 645.8 ± 142.3 mLL;  p  < 0.001), lower VAS scores at postoperative day 3 (5.0 ± 1.0 vs. 6.9 ± 1.1, p  < 0.05), and significantly shorter radiographic union time (5.0 ± 1.4 vs.6.6 ± 1.6 months, p  < 0.05). There was no significant difference in the surgical complications between the two groups. Conclusion The method of retaining the original intramedullary nail using hybrid bone grafting combined with local augmented plate fixation had a shorter operation time, less total blood loss, lower VAS scores, and shorter union time compared to the method of removing the original intramedullary nail, performing channel bone grafting, and using vertical double-plate fixation. In clinical practice, the treatment method of retaining the original intramedullary nail using hybrid bone grafting combined with local augmented plate fixation may be a more preferable treatment option for atrophic nonunion of femoral shaft after intramedullary nailing.

This work studies online learning-based trajectory planning for multiple autonomous underwater vehicles (AUVs) to estimate a water parameter field of interest in the under-ice environment. A centralized system is considered, where several fixed access points on the ice layer are introduced as gateways for communications between the AUVs and a remote data fusion center. We model the water parameter field of interest as a Gaussian process with unknown hyper-parameters. The AUV trajectories for sampling are determined on an epoch-by-epoch basis. At the end of each epoch, the access points relay the observed field samples from all the AUVs to the fusion center, which computes the posterior distribution of the field based on the Gaussian process regression and estimates the field hyper-parameters. The optimal trajectories of all the AUVs in the next epoch are determined to maximize a long-term reward that is defined based on the field uncertainty reduction and the AUV mobility cost, subject to the kinematics constraint, the communication constraint and the sensing area constraint. We formulate the adaptive trajectory planning problem as a Markov decision process (MDP). A reinforcement learning-based online learning algorithm is designed to determine the optimal AUV trajectories in a constrained continuous space. Simulation results show that the proposed learning-based trajectory planning algorithm has performance similar to a benchmark method that assumes perfect knowledge of the field hyper-parameters.