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In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape was realized by adding longitudinal and lateral maneuver overloads. The Maneuver command decision model is calculated based on soft-actor–critic (SAC) networks. Meta-learning was introduced to enhance the autonomous escape capability, which improves the performance of applications in time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this study used the prediction method to solve reward values, avoiding a large number of numerical integrations. The simulation results demonstrated that the proposed intelligent strategy can achieve highly precise guidance and effective escape.

In this study, a model-free  PIφ-sliding mode control ( PIφ-SMC) methodology is proposed to synchronize a specific class of chaotic fractional-order memristive neural network systems (FOMNNSs) with delays and input saturation. The fractional-order Lyapunov stability theory is used to design a two-level  PIφ-SMC which can effectively manage the inherent chaotic behavior of delayed FOMNNSs and achieve finite-time synchronization. At the outset, an initial sliding surface is introduced. Subsequently, a robust  PIφ-sliding surface is designed as a second sliding surface, based on proportional–integral (PI) rules. The finite-time asymptotic stability of both surfaces is demonstrated. The final step involves the design of a dynamic-free control law that is robust against system uncertainties, input saturations, and delays. The independence of control rules from the functions of the system is accomplished through the application of the norm-boundedness property inherent in chaotic system states. The soft actor-critic (SAC) algorithm based deep Q-Learning is utilized to optimally adjust the coefficients embedded in the two-level  PIφ-SMC controller’s structure. By maximizing a reward signal, the optimal policy is found by the deep neural network of the SAC agent. This approach ensures that the sliding motion meets the reachability condition within a finite time. The validity of the proposed protocol is subsequently demonstrated through extensive simulation results and two numerical examples.

Slovenia has one of the most extensive Natura 2000 networks in Europe with 259 SAC’s covering 31.4% of the country. To determine how well does the current network cover the areas of high butterfly diversity and/or aggregation of the butterfly species of conservation concern, the data from the recent survey for a distribution atlas were used. Altogether 99,423 records of 173 species collated after 1979 were used. The data distribution is slightly biased towards SAC’s, with 44.8% of localities within them, most likely due to sparsely sampled urban areas and intensive farmland areas which are found only outside SAC’s. The diversity and distribution of red listed species was evaluated at a 5 × 5 km grid square level. Additionally the importance of the size of the SAC’s was compared to their butterfly species diversity. In general the high diversity areas also hold the largest aggregation of red listed species with core areas concentrated in SW Slovenia. The SAC’s cover majority of areas with high diversity and the distribution of all but one threatened butterfly species. That species is Colias myrmidone , which is now considered extinct in Slovenia with no records after 1993. The most prominent areas with high conservation value in Slovenia not included in the SAC’s network are the Koroška region, Goriška Brda region, lower Sava River valley and Slovenske Gorice region. The butterfly diversity in small SAC’s is relatively high with increases in size only gradually increasing the species numbers, thus emphasizing the importance and conservation value of small SAC’s for sustaining high butterfly diversity in Slovenia.

It is well established that haematopoietic stem and progenitor cells (HSPCs) are generated from a transient subset of specialized endothelial cells termed haemogenic, present in the yolk sac, placenta and aorta, through an endothelial-to-haematopoietic transition (EHT). HSPC generation via EHT is thought to be restricted to the early stages of development. By using experimental embryology and genetic approaches in birds and mice, respectively, we document here the discovery of a bone marrow haemogenic endothelium in the late fetus/young adult. These cells are capable of de novo producing a cohort of HSPCs in situ that harbour a very specific molecular signature close to that of aortic endothelial cells undergoing EHT or their immediate progenies, i.e., recently emerged HSPCs. Taken together, our results reveal that HSPCs can be generated de novo past embryonic stages. Understanding the molecular events controlling this production will be critical for devising innovative therapies. Yvernogeau, Gautier, Petit et al. demonstrate the existence of a haemogenic endothelium capable of de novo haematopoietic stem and progenitor cell generation in the forming bone marrow of chicken and mouse fetuses and newborns.

The N-end rule pathway is a proteolytic system in which destabilizing N-terminal residues of short-lived proteins act as degradation determinants (N-degrons). Substrates carrying N-degrons are recognized by N-recognins that mediate ubiquitylation-dependent selective proteolysis through the proteasome. Our previous studies identified the mammalian N-recognin family consisting of UBR1/E3α, UBR2, UBR4/p600, and UBR5, which recognize destabilizing N-terminal residues through the UBR box. In the current study, we addressed the physiological function of a poorly characterized N-recognin, 570-kDa UBR4, in mammalian development. UBR4-deficient mice die during embryogenesis and exhibit pleiotropic abnormalities, including impaired vascular development in the yolk sac (YS). Vascular development in UBR4-def icient YS normally advances through vasculogenesis but is arrested during angiogenic remodeling of primary capillary plexus associated with accumulation of autophagic vacuoles. In the YS, UBR4 marks endoderm-derived, autophagy-enriched cells that coordinate differentiation of mesoderm-derived vascular cells and supply autophagy-generated amino acids during early embryogenesis. UBR4 of the YS endoderm is associated with a tissue-specific autophagic pathway that mediates bulk lysosomal proteolysis of endocytosed maternal proteins into amino acids. In cultured cells, UBR4 subpopulation is degraded by autophagy through its starvation-induced association with cellular cargoes destined to autophagic double membrane structures. UBR4 loss results in multiple misregulations in autophagic induction and flux, including synthesis and lipidation/activation of the ubiquitin-like protein LC3 and formation of autophagic double membrane structures. Our results suggest that UBR4 plays an important role in mammalian development, such as angiogenesis in the YS, in part through regulation of bulk degradation by lysosomal hydrolases.

The human yolk sac (hYS) is essential for embryo nutrient biosynthesis/transport and development. However, there lacks a comprehensive study of hYS nutrient-gene interactions. Here we performed a secondary analysis of hYS transcript profiles ( n  = 9 samples) to identify nutrient-sensitive hYS genes and regulatory networks, including those that associate with adverse perinatal phenotypes with embryonic origins. Overall, 14.8% highly expressed hYS genes are nutrient-sensitive; the most common nutrient cofactors for hYS genes are metals and B vitamins. Functional analysis of highly expressed hYS genes reveals that nutrient-sensitive hYS genes are more likely to be involved in metabolic functions than hYS genes that are not nutrient-sensitive. Through nutrient-sensitive gene network analysis, we find that four nutrient-sensitive transcription regulators in the hYS (with zinc and/or magnesium cofactors) are predicted to collectively regulate 30.9% of highly expressed hYS genes. Lastly, we identify 117 nutrient-sensitive hYS genes that associate with an adverse perinatal outcome with embryonic origins. Among these, the greatest number of nutrient-sensitive hYS genes are linked to congenital heart defects ( n  = 54 genes), followed by microcephaly ( n  = 37). Collectively, our study characterises nutrient-sensitive hYS functions and improves understanding of the ways in which nutrient-gene interactions in the hYS may influence both typical and pathological development.

Incomplete representations of physical processes often lead to structural errors in process-based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. As a result, ML algorithms may be unreliable if used to provide future hydroclimate projections where climates and land use patterns are outside the range of training data. Here we test hybrid models built by integrating PB model outputs with an ML algorithm known as long short-term memory (LSTM) network on their ability to simulate streamflow in 531 catchments representing diverse conditions across the Conterminous United States. Model performance of hybrid models as measured by Nash-Sutcliffe efficiency (NSE) improved relative to standalone PB and LSTM models. More importantly, hybrid models provide highest improvement in catchments where PB models fail completely (i.e. NSE < 0). However, all models performed poorly in catchments with extended low flow periods, suggesting need for additional research.

The growing need for high-speed services, coupled with data-intensive technologies like the Internet of Things (IoT), is expected to exacerbate congestion within existing Radio Frequency (RF) communication systems. As a result, Free Space Optics (FSO) has emerged as a promising alternative to RF, offering superior data transmission capabilities. Additionally, ensuring security has become a crucial concern to safeguard sensitive information. Accordingly, in this paper, an FSO system is proposed that uses an Identity Column Shift Matrix (ICSM) code for higher and confidential data transformation. The ICSM code is one of the Spectral-Amplitude-Optical-Code-Division-Multiple Access (SAC-OCDMA) codes which is characterized by easy construction due to zero cross-correlation property. Moreover, the effectiveness of clear, haze, fog and rain conditions are considered while examining the proposed model performance in addition to real meteorological data for two cities (Alexandria, Egypt, and Pune, India). Eye diagrams, received power, and Bit Error Rate (BER) are the evaluation parameters used for the proposed model performance. The simulation results reveal that as weather becomes severe, the FSO span decreases, and the performance becomes worst. As for clear weather, an FSO link of 26 km is obtained which is decreased to 1.1 km under the dense level of fog. Regarding the two cities, the distance covered by the information signal during rainy weather in Pune is 6.7 km, which is smaller than that in Alexandria due to Pune's higher attenuation value. These transmission ranges are obtained with an overall capacity of 3 × 10 Gbps, received power < − 22.8 dBm, and BER below 10 –5.6 .

When there are dynamic obstacles in the environment, it is difficult for traditional path-generation algorithms to achieve desired obstacle-avoidance results. To solve this problem, we propose a robot navigation control method based on SAC (Soft Actor-Critic) Deep Reinforcement Learning. Firstly, we use a fast path-generation algorithm to control the robot to generate expert trajectories when the robot encounters danger as well as when it approaches a target, and we combine SAC reinforcement learning with imitation learning based on expert trajectories to improve the safety of training. Then, for the hybrid data consisting of agent data and expert data, we use an improved prioritized experience replay method to improve the learning efficiency of the policies. Finally, we introduce RNN (Recurrent Neural Network) units into the network structure of the SAC Deep Reinforcement-Learning navigation policy to improve the agent’s transfer inference ability in a new environment and obstacle-avoidance ability in dynamic environments. Through simulation and practical experiments, it is fully verified that our method has a higher training efficiency and navigation success rate compared to state-of-the-art reinforcement-learning algorithms, which further enhances the obstacle-avoidance capability of the robot system.

Transformer-based methodologies in object detection have recently piqued considerable interest and have produced impressive results. DETR, an end-to-end object detection framework, ingeniously integrates the Transformer architecture, traditionally used in NLP, into computer vision for sequence-to-sequence prediction. Its enhanced variant, DINO, featuring improved denoising anchor boxes, has showcased remarkable performance on the COCO val2017 dataset. However, it often encounters challenges when applied to scenarios involving small object detection. Thus, we propose an innovative method for feature enhancement tailored to recursive prediction tasks, with a particular emphasis on augmenting small object detection performance. It primarily involves three enhancements: refining the backbone to favor feature maps that are more sensitive to small targets, incrementally augmenting the number of queries for small objects, and advancing the loss function for better performance. Specifically, The study incorporated the Switchable Atrous Convolution (SAC) mechanism, which features adaptable dilated convolutions, to increment the receptive field and thus elevate the innate feature extraction capabilities of the primary network concerning diminutive objects. Subsequently, a Recursive Small Object Prediction (RSP) module was designed to enhance the feature extraction of the prediction head for more precise network operations. Finally, the loss function was augmented with the Normalized Wasserstein Distance (NWD) metric, tailoring the loss function to suit small object detection better. The efficacy of the proposed model is empirically confirmed via testing on the VISDRONE2019 dataset. The comprehensive array of experiments indicates that our proposed model outperforms the extant DINO model in terms of average precision (AP) small object detection.