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In a service ecosystem, the trust of users in services serves as the foundation for maintaining normal interactions among users, service providers, and platforms. However, malicious attacks can tamper with the trust value of these services, making it difficult for users to identify reliable services and undermining the benefits of reliable service providers and platforms. When existing trust management models address the impact of malicious attacks on service reliability, they rarely consider leveraging different attack targets to improve the accuracy of compromised service trust. Therefore, we propose a trust enhancement model based on distributed learning and blockchain in the service ecosystem, which adaptively enhances the trust values of compromised services according to the targets of anomalous attacks. Firstly, we conduct a comprehensive analysis of the targets of malicious attacks using distributed learning. Secondly, we introduced a trust enhancement contract that utilizes different methods to enhance the trust of the service based on various attack targets. Finally, our approach outperforms the baseline method significantly. For different attack targets, we observe a reduction in RMSE by 12.38% and 12.12%, respectively, and an enhancement in coverage by 24.94% and 14.56%, respectively. The experimental results show the reliability and efficacy of our proposed model.

Maize growth and development are regulated by light quality, intensity and photoperiod. Cryptochromes are blue/ultraviolet-A light receptors involved in stem elongation, shade avoidance, and photoperiodic flowering. To investigate the function of cryptochrome 1 (CRY1) in maize, where it is encoded by ZmCRY1, we obtained two ZmCRY1a genes (ZmCRY1a1 and ZmCRY1a2), both of which share the highest similarity with other gramineous plants, in particular rice CRY1a by phylogenetic analysis. In Arabidopsis, overexpression of ZmCRY1a genes promoted seedling de-etiolation under blue and white light, resulting in dwarfing of mature plants. In seedlings of the maize inbred line Zong 31 (ZmCRY1a-OE), overexpression of ZmCRY1a genes caused a reduction in the mesocotyl and first leaf sheath lengths due to down-regulation of genes influencing cell elongation. In mature transgenic maize plants, plant height, ear height, and internode length decreased in response to overexpression of ZmCRY1a genes. Expression of ZmCRY1a were insensitive to low blue light (LBL)-induced shade avoidance syndrome (SAS) in Arabidopsis and maize. This prompted us to investigate the regulatory role of the gibberellin and auxin metabolic pathways in the response of ZmCRY1a genes to LBL treatment. We confirmed a link between ZmCRY1a expression and hormonal influence on the growth and development of maize under LBL-induced SAS. These results reveal that ZmCRY1a has a relatively conservative function in regulating maize photomorphogenesis and may guide new strategies for breeding high density-tolerant maize cultivars.

Federated Learning (FL) can train models in an edge environment without sending raw data. However, the performance is still constrained by data heterogeneity. To address the problems of data heterogeneity and resource scarcity in edge devices, we propose Federal Learning via Dynamic Aggregation (FedDA), which eliminates the influence of data heterogeneity and improves model accuracy. FedDA updates the impact of individual local models on the global model in real-time at different stages. It adjusts the local epoch in each round to prevent the device from dropping out while obtaining a more accurate local model. The core module is the model impact factor (MIF) that inscribes the aggregation weights to solve the impact of fixed weights on the aggregation model with improper extraction of local information. We conducted several experiments to evaluate the convergence speed using different algorithms on the MINIST. FedDA consistently outperforms the other six SOTA algorithms on MNIST, Cifar10, and Cifar100 datasets. In significant data heterogeneity, FedDA improves accuracy by up to 6% over the different algorithms and at least about 3%, especially in resource-scarce environments. To reach the specified accuracy, FedDA is 3 times faster than SCAFFOLD and at least 50% faster than other algorithms.

Graph neural networks (GNNs) have shown significant success in graph representation learning. However, the performance of existing GNNs degrades seriously when their layers deepen due to the over-smoothing issue. The node embedding incline converges to a certain value when GNNs repeat, aggregating the representations of the receptive field. The main reason for over-smoothing is that the receptive field of each node tends to be similar as the layers increase, which leads to different nodes aggregating similar information. To solve this problem, we propose an adaptive multi-channel deep graph neural network (AMD-GNN) to adaptively and symmetrically aggregate information from the deep receptive field. The proposed model ensures that the receptive field of each node in the deep layer is different so that the node representations are distinguishable. The experimental results demonstrate that AMD-GNN achieves state-of-the-art performance on node classification tasks with deep models.

With the mutual interaction and dependence of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. Such a development produces an ever-increasing effect on our lives and the functioning of the whole society. These facts call for research on these phenomena with a new theory or perspective, including what a smart society looks like, how it functions and evolves, and where its boundaries and challenges are. However, the research on service ecosystems is distributed in many disciplines and fields, including computer science, artificial intelligence, complex theory, social network, biological ecosystem, and network economics, and there is still no unified research framework. The researchers always have a restricted view of the research process. Under this context, this paper summarizes the research status and future developments of service ecosystems, including their conceptual origin, evolutionary logic, research topic and scale, challenges, and opportunities. We hope to provide a roadmap for the research in this field and promote sound development.

The traditional industrial chain consists of a group of entities that are familiar with each other, and the relationship between the entities is maintained by signing contracts. However, on the one hand, the inability to reach mutual trust between unfamiliar entities limits the collaboration of entities, and on the other hand, most of the profits in the industrial chain are grabbed by the dominant enterprises. In this paper, an information system for collaboration among business entities is developed based on blockchain, based on which a business model called Decentralized Industry Alliance (DIA) is proposed. Transactions and profit distribution in the DIA are executed by smart contracts, which enables unfamiliar entities to trust each other without relying on a third party. Any entity that voluntarily complies with the smart contracts automatically becomes a member of the DIA, thereby promoting the expansion of the industry scale. The smart contracts distribute profits according to each entity’s contribution to the alliance, so as to ensure the fairness of value distribution. A case about the alliance of elderly care industry is carried out, which shows that the DIA achieves rapid expansion on the premise of ensuring fairness. As a general business model in the web 3.0 era, the DIA can be referenced by other industries.

Composite patches are widely accepted as a useful practice for the repair of cracked aircraft components and the repair method is of vital importance to the final performance of the repaired structures. The present research experimentally studied the repair efficiency and processing stability of pre-cured, prepreg (including unidirectional and plain weave prepregs) and wet-layup methods for use on cracked Ti-alloy panels through the configuration of a butt joint bonded with a one-sided composite patch. The efficiency and stability of these repair methods were elaborately evaluated and compared via the load bearing behavior, the microstructure of the bonding interface, and the structural failure morphology through two batches of testing specimens. Typical patterns were found in load-displacement curves where the initial damage and ultimate bearing load points divided them into elastic-linear, damage propagation and complete fracture phases. Although the co-cure process of both unidirectional prepreg and wet-layup methods can form a jigsaw-like demarcation interface between the adhesive layer and the composite patch to achieve a good bonding force and a high recovery of loading performance, the latter presents porous patches with a high coefficient of variation in load-carrying capacity. Conversely, the pre-cured laminate and the plain weave prepreg patches failed to restore the mechanical properties owing to the weak bonding interface and the low axial patch strength, respectively. The unidirectional prepreg patch was proven to be the optimal repair method for the cracked metallic structures when balancing repair efficiency and processing stability.

Common wheat, Triticum aestivum, is the most widely grown staple crop worldwide. To catch up with the increasing global population and cope with the changing climate, it is valuable to breed wheat cultivars that are tolerant to abiotic or shade stresses for density farming. Arabidopsis LONG HYPOCOTYL IN FAR-RED 1 (AtHFR1), a photomorphogenesis-promoting factor, is involved in multiple light-related signaling pathways and inhibits seedling etiolation and shade avoidance. We report that overexpression of AtHFR1 in wheat inhibits etiolation phenotypes under various light and shade conditions, leading to shortened plant height and increased spike number relative to non-transgenic plants in the field. Ectopic expression of AtHFR1 in wheat increases the transcript levels of TaCAB and TaCHS as observed previously in Arabidopsis, indicating that the AtHFR1 transgene can activate the light signal transduction pathway in wheat. AtHFR1 transgenic seedlings significantly exhibit tolerance to osmotic stress during seed germination compared to non-transgenic wheat. The AtHFR1 transgene represses transcription of TaFT1, TaCO1, and TaCO2, delaying development of the shoot apex and heading in wheat. Furthermore, the AtHFR1 transgene in wheat inhibits transcript levels of PHYTOCHROME-INTERACTING FACTOR 3-LIKEs (TaPIL13, TaPIL15-1B, and TaPIL15-1D), downregulating the target gene STAYGREEN (TaSGR), and thus delaying dark-induced leaf senescence. In the field, grain yields of three AtHFR1 transgenic lines were 18.2–48.1% higher than those of non-transgenic wheat. In summary, genetic modification of light signaling pathways using a photomorphogenesis-promoting factor has positive effects on grain yield due to changes in plant architecture and resource allocation and enhances tolerances to osmotic stress and shade avoidance response.

Late embryogenesis abundant (LEA) proteins are members of a large and highly diverse family that play critical roles in protecting cells from abiotic stresses and maintaining plant growth and development. However, the identification and biological function of genes of Secale cereale LEA (ScLEA) have been rarely reported. In this study, we identified 112 ScLEA genes, which can be divided into eight groups and are evenly distributed on all rye chromosomes. Structure analysis revealed that members of the same group tend to be highly conserved. We identified 12 pairs of tandem duplication genes and 19 pairs of segmental duplication genes, which may be an expansion way of LEA gene family. Expression profiling analysis revealed obvious temporal and spatial specificity of ScLEA gene expression, with the highest expression levels observed in grains. According to the qRT-PCR analysis, selected ScLEA genes were regulated by various abiotic stresses, especially PEG treatment, decreased temperature, and blue light. Taken together, our results provide a reference for further functional analysis and potential utilization of the ScLEA genes in improving stress tolerance of crops.

GPS trajectory data plays a critical role for the intelligent transportation. Due to many factors like measure error, sampling error and battery power-saving requirement, the directly obtained trajectories will inaccurately align to the digital map. This requires an online map matching algorithm with high precision, low latency, and energy consumption saving. However, the existing approaches need to make some trade off among these criterions. Hence, based on the hidden Markov model, this paper proposes an adaptive online map matching algorithm to improve the performances in these perspectives at the same time: 1) the probabilistic method integrating the geometric information and topological information is developed to improve the accuracy; 2) the adaptive sampling frequency method is proposed to reduce the energy consumption; and 3) the adaptive sliding window method is presented to reduce the output delay. The experiments demonstrate that our approaches can not only improve the matching precision, but also reduce the latency and energy consumption simultaneously.

As more and more ontologies are defined with different terms, ontology matching plays a crucial role in addressing the semantic heterogeneity problem in many different disciplines. Many efforts have been made to discover correspondences among terms in different ontologies. Most studies directly match two ontologies by utilizing terminological and structural methods that rely on ontologies themselves only. However, the decentralized characteristic of ontologies raises the uncertainty in ontology matching. To address this problem, we propose a four-stage ontology matching framework (FOMF) to enhance ontology matching performance. It is built upon the commonly accepted claim that an external comprehensive knowledge base can be used as a semantic bridge between domain ontologies for ontology matching. First, FOMF semantically maps domain ontologies to a knowledge base and then produces different types of alignments, including equivalence, subclass, sameas, and instance alignments. Similarities between two domain ontologies are next employed to enhance the equivalence and sameas alignments discovery. Finally, based on acquired alignments, inferred alignments are deduced to guarantee the completeness of matching results. Our experimental results show the superiority of the proposed method over the existing ones.