Adaptive Knowledge Research Articles

DNA methylome analysis provides insights into gene regulatory mechanism for better performance of rice under fluctuating environmental conditions: epigenomics of adaptive plasticity.

Roots play an important role in adaptive plasticity of rice under dry/direct-sown conditions. However, hypomethylation of genes in leaves (resulting in up-regulated expression) complements the adaptive plasticity of Nagina-22 under DSR conditions. Rice is generally cultivated by transplanting which requires plenty of water for irrigation. Such a practice makes rice cultivation a challenging task under global climate change and reducing water availability. However, dry-seeded/direct-sown rice (DSR) has emerged as a resource-saving alternative to transplanted rice (TPR). Though some of the well-adapted local cultivars are used for DSR, only limited success has been achieved in developing DSR varieties mainly because of a limited knowledge of adaptability of rice under fluctuating environmental conditions. Based on better morpho-physiological and agronomic performance of Nagina-22 (N-22) under DSR conditions, N-22 and IR-64 were grown by transplanting and direct-sowing and used for whole genome methylome analysis to unravel the epigenetic basis of adaptive plasticity of rice. Comparative methylome and transcriptome analyses indicated a large number (4078) of genes regulated through DNA methylation/demethylation in N-22 under DSR conditions. Gene × environment interactions play important roles in adaptive plasticity of rice under direct-sown conditions. While genes for pectinesterase, LRK10, C2H2 zinc-finger protein, splicing factor, transposable elements, and some of the unannotated proteins were hypermethylated, the genes for regulation of transcription, protein phosphorylation, etc. were hypomethylated in CG context in the root of N-22, which played important roles in providing adaptive plasticity to N-22 under DSR conditions. Hypomethylation leading to up-regulation of gene expression in the leaf complements the adaptive plasticity of N-22 under DSR conditions. Moreover, differential post-translational modification of proteins and chromatin assembly/disassembly through DNA methylation in CHG context modulate adaptive plasticity of N-22. These findings would help developing DSR cultivars for increased water-productivity and ecological efficiency.

Adaptive knowledge push method of product intelligent design based on feature transfer

To facilitate more effective knowledge utilization in the process of product intelligent design, aiming at the sparsity and cold start issues in knowledge push, an adaptive push method of product intelligent design knowledge based on feature transfer is proposed in the current study. First, based on the research of the current solution strategy, a heterogeneous domain collaborative filtering algorithm model based on feature transfer is constructed, which completed the knowledge transfer from the user behavior domain to the user rating domain, and alleviates the data sparsity issue. Then, for new users, the user-item rating model has been transformed into a tag-keyword relationship model, completing the adaptive reconstruction of the new user rating model, and solves the cold start issue. Finally, the method proposed in this study has been verified by an example, which is proven to be able to accurately push knowledge to new users under the condition of data sparseness while increasing the push rate. It provides a new method for solving knowledge push problems of similar nature.

Mutual antagonism of mouse-adaptation mutations in HA and PA proteins on H9N2 virus replication

Avian H9N2 viruses have wide host range among the influenza A viruses. However, knowledge of H9N2 mammalian adaptation is limited. To explore the molecular basis of the adaptation to mammals, we performed serial lung passaging of the H9N2 strain A/chicken/Hunan/8.27 YYGK3W3-OC/2018 (3W3) in mice and identified six mutations in the hemagglutinin (HA) and polymerase acidic (PA) proteins. Mutations L226Q, T511I, and A528V of HA were responsible for enhanced pathogenicity and viral replication in mice; notably, HA-L226Q was the key determinant. Mutations T97I, I545V, and S594G of PA contributed to enhanced polymerase activity in mammalian cells and increased viral replication levels in vitro and in vivo. PA-T97I increased viral polymerase activity by accelerating the viral polymerase complex assembly. Our findings revealed that the viral replication was affected by the presence of PA-97I and/or PA-545V in combination with a triple-point HA mutation. Furthermore, the double- and triple-point PA mutations demonstrated antagonistic effect on viral replication when combined with HA-226Q. Notably, any combination of PA mutations, along with double-point HA mutations, resulted in antagonistic effect on viral replication. We also observed antagonism in viral replication between PA-545V and PA-97I, as well as between HA-528V and PA-545V. Our findings demonstrated that several antagonistic mutations in HA and PA proteins affect viral replication, which may contribute to the H9N2 virus adaptation to mice and mammalian cells. These findings can potentially contribute to the monitoring of H9N2 field strains for assessing their potential risk in mammals.

Local Artisans’ Knowledge of Flood Resilient Construction and Adaptation of Residential Buildings in Flood-Prone Informal Settlements in Dar es Salaam, Tanzania

Flood-prone informal settlements are among the most complex and challenging construction sites that require adequate knowledge to enhance flood resilience and adaptation. While such settlements present construction challenges that need the attention of professionals, anecdotal observations and experiences in developing countries and Tanzania, in particular, indicate a vast preference to engage and prioritise local artisans in such areas under the pretext of the high cost of affording professionals. This article seeks to understand how knowledgeable artisans are about constructing residential buildings in flood-prone informal settlements in Dar es Salaam. To understand this phenomenon, various flood-resilient and adaptive measures that local artisans know and use in constructing buildings in flood-prone areas were analysed. The article adopted a case study design where qualitative data were collected using key informant interviews and focus-group discussions and analysed thematically. Results showed that local artisans constructing houses in the flood-prone areas of Msasani Bonde la Mpunga have knowledge about various flood-resilient and adaptation measures, including engineering-related skills (structural) and tacit categories. These measures include; raising/elevating ground floor levels, using reinforced foundations and aprons around the foundation plinth and using waterproof building materials such as waterproof cement. However, using stilts/columns was not often applied because it was conceived as a complex and costly flood construction measure. The article concludes that most local artisans in the Msasani Bonde la Mpunga settlement had good and relevant knowledge to enhance flood resilience and adaptation of buildings. However, they require the technical proficiency of professionals. The article calls for regular training and inseminations of flood-resilient construction techniques for local artisans. This will enable the blending of the engineering and local knowledge/expertise fundamental for enhancing sustainable urban planning and resilient building construction in flood-prone informal settlements. Local artisans’ training should be synchronised with sensitization to enlighten communities about the significance of utilising skilled and trained local artisans.

Legal-practical segment for adapting refugees to European society

This article aims to address important issues about a prosperous European democratic society, such as through practical elements for adapting refugees to the European community. Various new research and analysis methods are used to receive the correct information. This information presents more knowledge of refugee adaptation disputes in the process of integrating the European community, starting from the final assimilation of European instruments, in relation to the main vital human pillars assimilating the practical elements of refugee adaptation. A cruel truth is exposed to all practical segments of refugee coexistence, with the objectives of improvement and alarming the public about the danger in which refugees are unaware of the local social traditions of EU citizens. Following the analysis of a voluminous level of scientific acts in the field of refugees, we determine the importance of guaranteeing a dignified standard of living of refugees. The authors of several doctrinal acts refer to a multitude of ideas, in order to establish an applicable situation of refugee coexistence in the European community. At the end of the study, conclusions are present

Knowledge updating in real-world estimation: Connecting hindsight bias and seeding effects.

When people estimate the quantities of objects (e.g., country populations), are then presented with the objects' actual quantities, and subsequently asked to remember their initial estimates, responses are often distorted towards the actual quantities. This hindsight bias-traditionally considered to reflect a cognitive error-has more recently been proposed to result from adaptive knowledge updating. But how to conceptualize such knowledge-updating processes and their potentially beneficial consequences? Here, we provide a framework that conceptualizes knowledge updating in the context of hindsight bias in real-world estimation by connecting it with research on seeding effects-improvements in people's estimation accuracy after exposure to numerical facts. This integrative perspective highlights a previously neglected facet of knowledge updating, namely, recalibration of metric domain knowledge, which can be expected to lead to transfer learning and thus improve estimation for objects from a domain more generally. We develop an experimental paradigm to investigate the association of hindsight bias with improved estimation accuracy. In Experiment 1, we demonstrate that the classical approach to induce hindsight bias indeed produces transfer learning. In Experiment 2, we provide evidence for the novel prediction that hindsight bias can be triggered via transfer learning; this establishes a direct link from knowledge updating to hindsight bias. Our work integrates two prominent but previously unconnected research programs on the effects of knowledge updating in real-world estimation and supports the notion that hindsight bias is driven by adaptive learning processes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Memorizing and Generalizing Framework for Lifelong Person Re-Identification.

In this paper, we introduce a challenging yet practical setting for person re-identification (ReID) task, named lifelong person re-identification (LReID), which aims to continuously train a ReID model across multiple domains and the trained model is required to generalize well on both seen and unseen domains. It is therefore critical to learn a ReID model that can learn a generalized representation without forgetting knowledge of seen domains. In this paper, we propose a new MEmorizing and GEneralizing framework (MEGE) for LReID, which can jointly prevent the model from forgetting and improve its generalization ability. Specifically, our MEGE is composed of two novel modules, i.e., Adaptive Knowledge Accumulation (AKA) and differentiable Ranking Consistency Distillation (RCD). Taking inspiration from the cognitive processes in the human brain, we endow AKA with two special capacities, knowledge representation and knowledge operation by graph convolution networks. AKA can effectively mitigate catastrophic forgetting on seen domains while improving the generalization ability to unseen domains. By considering the ranking factor that is specifically important in ReID, RCD is designed to distill the ranking knowledge in a differentiable manner, which can further prevent the catastrophic forgetting. To supporting the study of LReID, we build a new and large-scale benchmark with two practical evaluation protocols that consider the metrics of non-forgetting and generalization. Experiments demonstrate that 1) our MEGE framework can effectively improve the performance on seen and unseen domains under the domain-incremental learning constraint, and that 2) the proposed MEGE outperforms state-of-the-art competitors by large margins.

Unsupervised domain adaptation-based crack segmentation using transformer network

Surface cracks are a common structural defect. The intelligent inspection of these defects through computer vision and deep learning is of paramount importance for early maintenance and operation. Despite the remarkable success of supervised learning methods in detecting surface cracks, their performance heavily relies on the availability of extensive labeled datasets. Annotating a single image can be a time-consuming process, prone to human error. Moreover, these methods often struggle to generalize effectively to unseen datasets due to disparities between source and target images. To address this issue, unsupervised domain adaptation comes into play, as it aims to transfer knowledge learned from the labeled source domain to the unlabeled target domain. Consequently, we conducted an evaluation of a recent unsupervised domain adaptation model for semantic segmentation that incorporates masked image consistency into DAFormer, a state-of-the-art model with the ability to adapt to various datasets. To assess the model’s performance, we employed three publicly available crack datasets, each containing background and crack classes. Our study has revealed that : (1) SegFormer, a transformer-based model, outperforms ConvNet-based models without utilizing adaptation knowledge, demonstrating superior generalizability to previously unseen data. (2) The unsupervised domain-adaptation model consistently outperforms the source model, resulting in a significant enhancement in the mean intersection over union of SegFormer’s source-only approach by a remarkable 10% to 22%. With the exception of a single case, the relative performance of unsupervised domain adaptation compared to supervised training with labeled data exceeds 85%, underscoring its promising performance in crack segmentation. Consequently, our adopted method emerges as a viable alternative, particularly in scenarios where labeled data is scarce or prohibitively expensive.

Synechococcus nitrogen gene loss in iron-limited ocean regions

Synechococcus are the most abundant cyanobacteria in high latitude regions and are responsible for an estimated 17% of annual marine net primary productivity. Despite their biogeochemical importance, Synechococcus populations have been unevenly sampled across the ocean, with most studies focused on low-latitude strains. In particular, the near absence of Synechococcus genomes from high-latitude, High Nutrient Low Chlorophyll (HNLC) regions leaves a gap in our knowledge of picocyanobacterial adaptations to iron limitation and their influence on carbon, nitrogen, and iron cycles. We examined Synechococcus populations from the subarctic North Pacific, a well-characterized HNLC region, with quantitative metagenomics. Assembly with short and long reads produced two near complete Synechococcus metagenome-assembled genomes (MAGs). Quantitative metagenome-derived abundances of these populations matched well with flow cytometry counts, and the Synechococcus MAGs were estimated to comprise >99% of the Synechococcus at Station P. Whereas the Station P Synechococcus MAGs contained multiple genes for adaptation to iron limitation, both genomes lacked genes for uptake and assimilation of nitrate and nitrite, suggesting a dependence on ammonium, urea, and other forms of recycled nitrogen leading to reduced iron requirements. A global analysis of Synechococcus nitrate reductase abundance in the TARA Oceans dataset found nitrate assimilation genes are also lower in other HNLC regions. We propose that nitrate and nitrite assimilation gene loss in Synechococcus may represent an adaptation to severe iron limitation in high-latitude regions where ammonium availability is higher. Our findings have implications for models that quantify the contribution of cyanobacteria to primary production and subsequent carbon export.

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Many-task optimization problem is a kind of challenging multi-task optimization problem with more than three tasks. Two significant issues in solving many-task optimization problems are measuring inter-task similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the inter-task similarity because the inter-task similarity is a concept with multiple different aspects. To address this limitation, this paper proposes a bi-objective knowledge transfer framework, which aims firstly to accurately measure different types of inter-task similarity using two different measurements and secondly to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure inter-task similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of inter-task similarity. We compare the bi-objective knowledge transfer framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world many-task optimization problems with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.

“Nothing for Free”: Intermediary actors and cross-scalar knowledge translation for climate adaptation in the Mekong Delta

In Vietnam, an entire multilevel governance apparatus is now engaged in furthering the climate adaptation agenda, much of it focused on the Mekong Delta. A politics of translation is at the heart of these operations, as differently situated actors reproduce, negotiate, and mobilize knowledge for adaptation in pursuit of varied objectives. In this article, I examine the role of intermediary actors that work as knowledge brokers and translators, transmitting knowledge upwards, downwards, and horizontally within this governance system, influencing adaptation practice in the process. Drawing on cross-scalar ethnographic research with Vietnamese scientists and researchers, development practitioners, agricultural extension agents, and provincial-level bureaucrats, it considers the agency these actors have in shaping the trajectory of socio-ecological change in the delta. Exploring strategies of translation and the interests they reflect, it finds that knowledge for adaptation is largely constrained by the dominant economic development agenda, where neoliberal discourses and state goals of "building socialism" intersect in defining the success of adaptation and its ideal subjects. Finally, the article identifies contestations and switch-points that occur and seeks to identify potential openings for transformative pathways to emerge.

Microstructural Adaptation for Prey Manipulation in the Millipede Assassin Bugs (Hemiptera: Reduviidae: Ectrichodiinae).

Species in Ectrichodiinae are known for their prey specialization on millipedes. However, knowledge of the morphological adaptations to this unique feeding habit was limited. In the current study, we examined the microstructures of the antennae, mouthparts, and legs of four millipede feeding ectrichodiines, Ectrychotes andreae (Thunberg, 1888), Haematoloecha limbata Miller, 1953, Labidocoris pectoralis (Stål, 1863), and Neozirta eidmanni (Taueber, 1930), and compared them with those of three species of tribelocephalines, a group closely related to Ectrichodiinae. On the antennae, we found four types of antennal sensilla. On the mouthparts, we recognized four types of labial sensilla. Sampled ectrichodiines have distinctly more and denser slightly transverse ridges on the external side of mandibles than tribelocephalines. E. andreae and H. limbata possess numerous small papillae fringed with densely arranged finger-print-like grains on the trochanter and femur; these probably facilitate the immobilization of prey. Overall, our study illustrates, at a microstructural level, the remarkable morphological adaption of prey manipulation in ectrichodiine, and has enhanced our understanding about stenophagy in the family Reduviidae.

Multi-condition operational optimization with adaptive knowledge transfer for municipal solid waste incineration process

To satisfy incineration efficiency and nitrogen oxides ultra-low emission requirements, it is of great importance to improve the operational performance of the municipal solid waste incineration (MSWI) process. However, due to the complex and fluctuating composition of municipal solid waste, it is challenging to guarantee optimal operation under changing conditions. To address this issue, a multi-condition operational optimization method based on adaptive knowledge transfer (MCOO-TR) is proposed for the MSWI process. First, an integrated optimization scheme, where different operation conditions are identified based on cluster analysis and expert knowledge, is developed. Second, a surrogate-assisted multi-objective particle swarm optimization algorithm is designed to solve the data-driven optimization problems for optimal operation, where the surrogate model selection mechanism is proposed to cope with multi-condition characteristics. Third, an adaptive knowledge transfer strategy, corresponding to condition identification, is designed aiming to improve optimization efficiency. Finally, the proposed MCOO-TR method is evaluated based on actual industrial data. The experimental results demonstrate that the MCOO-TR method can achieve satisfactory operational performance under changing operation conditions.

Are the rights and knowledge of indigenous peoples and local communities being attended to by climate framework laws?

ABSTRACT Climate change has become a central concern on the international political agenda, challenging the decision-making of different levels of administration and types of actors. Indigenous Peoples and Local Communities (IPLC) have been recognized as relevant actors in climate matters, given their knowledge about territory, biodiversity, and their harmonious practices towards nature. With the evolution of knowledge on climate change, an increasing number of countries have developed climate laws. Given the provisions of the Paris Agreement to consider IPLC and their knowledge for climate action, it is relevant to assess how the contents of these types of laws pursue such ambition. By describing and categorizing the contents of climate laws, this article develops evidence-based research about how IPLC are attended to in climate framework laws of different countries. It examines whether the related contents of these laws align with the recommendation of the Paris Agreement regarding the need to consider traditional, indigenous, and local knowledge in the design of adaptation measures. The results show that only one-third of the identified climate laws refer to IPLC. Within these laws, those communities and their knowledge are scarcely attended to. However, a few climate laws develop relevant elements about these communities. The most common element relates to the participation of IPLC in deliberative bodies or climate decisions. In contrast, the least common element relates to the involvement of relevant communities in climate research. Notably, the climate laws of Finland (Europe) and Peru (South America) emerge as more comprehensive in addressing the IPLC and their knowledge than what is found in other countries. Despite the recognized relevance of IPLC and their traditional knowledge for climate change adaptation, the use of climate framework laws to formally foster such recognition is still lacking. Setting up a scheme to monitor how the translation of the Paris Agreement is being undertaken into subsequent legislative processes is desirable. Such a scheme may clarify how IPLC and their traditional knowledge are effectively being considered as initially expected.

Improved illumination homogeneity increased accuracies of derived light utilization efficiency for aquatic photosynthesis-irradiance curve analysis

Accumulating knowledge of photo-physiological acclimation and adaptation in aquatic phototrophs to altered environmental factors are valuable for managing and conserving aquatic ecosystems. Photosynthesis-irradiance curve (PI curve) analysis is an essential technique to assess the photo-physiological states of and environmental stresses on photosystems. For PI curve analysis, replicates were rarely homogeneously illuminated, which could generate variations potentially obscuring treatment effects or lead to considerable errors. Here we present an incubation apparatus with a novel configuration of illuminating unit that supplied a gradient of irradiances with improved homogeneity. The achieved homogeneity exceeds that of other homogeneous illuminating apparatus reported for photosynthetic research. We used the elaborated apparatus to develop PI curves for S. pectinata photo-acclimated to contrasting light conditions in both greenhouse and field scenarios. Photo-acclimation to lower irradiances enhanced both maximum photosynthetic rates and light utilization efficiencies in general. And improved homogeneity for PI curve analysis most likely reduced variations of derived light utilization efficiency compared to those using conventional incubation apparatus. The elaborated incubation apparatus could provide insights into developments of illumination techniques for photosynthetic studies and has the potential to refine the subtleties of photo-acclimation studies.

To Flee or Not to Flee: How Age, Reproductive Phase, and Mate Presence Affect White Stork Flight Decisions.

Recognizing, assessing, and responding to threats is critical for survival in the wild. Birds, especially in their role as parents, must decide whether to flee or delay flight when threatened. This study examines how age, reproductive stage, and the presence of a mate influence flight initiation distance (FID) and nest recess duration in white storks. Analyzing the data with a generalized additive mixed model (GAMM), we found significant correlations between FID and age, reproductive stage, and presence of a mate. These results suggest that the trade-off between current and future reproduction shifts during critical breeding periods, such as incubation and nestling care. To increase breeding success, White Storks appear willing to take risks and extend their stay in the nest when offspring are most valuable and vulnerable. In the presence of a mate, individuals leave the nest earlier, suggesting possible sexual conflict over parental care. The duration of nest abandonment is consistent with FID, except for age. These results illustrate how parental age, brood value, vulnerability, and sexual dynamics influence white stork flight decisions in complex ways. Understanding these dynamics enriches our knowledge of bird behavior and adaptations to environmental challenges and highlights the complexity of parental decision making.

Conceptual model of the product development process using the tacit knowledge of disables

Knowledge management (KM) in an organization has always been an important element of creating its value and ensuring its development. Economic entities and market organizations that strive to ensure a competitive advantage must care for the best use of their knowledge, both explicit and tacit. Tacit knowledge may constitute up to 80% of all knowledge accumulated in an organization, but its nature is much less accessible to codification. The aim of the conducted research is to present a conceptual model of product development for the disabled, which assumes the functioning of the value network (VN) based on information technologies (IT). These people are the best source of knowledge necessary to formulate the framework defining the parameters of the developed product. The presented model considers the specific requirements of a given social group, which is characterized by a large dispersion and diverse needs. This is the biggest obstacle to extracting the knowledge necessary in the design and testing process. The article is an introduction to the discussion on the acquisition of tacit knowledge for personalization and adaptation of products to the needs of a given social group. The research into the conceptual model is the initial stage of research work that focuses on the analysis of literature and the creation of the concept of tacit knowledge management in the product development model based on the value network. The management of dispersed tacit knowledge is to systematize specific information in the field of product adaptation to the needs of the disabled. The concept of using tacit knowledge is always a challenge in the organization's knowledge management. The disabled often face difficulties in everyday life. Especially in the current situation in Ukraine, where military operations lead to an increase in the number of injured people for whom later adaptation in society will require appropriate knowledge and resources managed within the value network.

Many-task evolutionary algorithm with adaptive knowledge transfer via density-based clustering

Evolutionary multitask optimization (EMTO) explores how to solve two or more related optimization tasks simultaneously by exploiting useful knowledge of other related tasks while solving one task, with the goal of enhancing the performance of the algorithm in solving each task. There has been a large body of work in the literature on knowledge transfer and reuse. However, when the number of tasks increases to a certain extent, the potential negative knowledge transfer between tasks becomes a great challenge for most multitask evolutionary algorithms. To allay this threat, this study proposes an adaptive knowledge transfer strategy for evolutionary many-task optimization via density-based clustering (AEMaTO-DC). AEMaTO-DC contains an adaptive mating selection mechanism, a correlation task evaluation and selection mechanism and a cluster-based knowledge interaction mechanism between tasks. Specifically, AEMaTO-DC regulates the probability of knowledge transfer by comparing the relative intensity of the intertask evolution rate and intratask evolution rate. In knowledge transfer, AEMaTO-DC selects related tasks by a maximum mean discrepancy metric, and it determines the intensity of intertask interaction by a density-based clustering method. We compare the performance of AEMaTO-DC with several state-of-the-art multitask evolutionary algorithms on a variety of synthetic benchmarks and a real-world multitask optimization problem. The experimental results demonstrate that AEMaTO-DC shows competitive success rates in solving the many-task problems used, and it takes advantage of synergistic relationships among solution processes for the tasks addressed.

Exploration the route of information integration for vehicle design: A knowledge-enhanced energy management strategy

With the development of the technologies for Vehicle-to-Everything (V2X), driving information sharing leads to a potential solution for intelligent vehicles to enhance their controlling effect, particularly for energy management of the vehicles equipped with the complex powertrain. Transplanting the optimal and adaptive knowledge of the vehicle driving data into a target vehicle can enhance both the adaptability and economic optimization of energy management. In this paper, a V2X-based knowledge-enhanced energy management strategy (KE-EMS) is proposed to transplant offline optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge into online driving in practice. Firstly, in the offline processing, based on micro trip segments collected from volunteered sharing vehicles by V2X, optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge are generated by dynamic programming (DP) and improved particle swarm optimization algorithm (iPSO) respectively. In the online implementation, the KE-EMS invokes the generated knowledge accordingly by a driving segment matching method (DSM) estimating the similarity between the current driving condition and micro driving segments. Finally, an improved equivalent consumption minimization strategy (ECMS) embedded into KE-EMS further adjusts the electric charging tendency to accomplish the energy-saving potential release. A simulation evaluation and hardware-in-the-loop (HIL) test manifest that the proposed KE-EMS can improve economic performance and guarantee real-time performance for the studied plug-in hybrid electric vehicles (PHEVs).

A Fast-Warning Method of Financial Risk Behavior Based on BP Neural Network

With the speedy development of economy, there are many issues in business enterprise finance, and organization finance is going through large risks. With more and more complicated market environment, the uncertainty danger of business enterprise operation intensifies, and economic crises happen frequently. The monetary disaster of a company regularly shows that there can also be a complete crisis. Once the organization is deeply in economic crisis, it can also now not be capable to make certain the ordinary capital chain of the enterprise, and in serious cases, it may also have an effect on the sustainable operation of the agency or even make the employer bankrupt and liquidate. Therefore, we have to set up a best financial catastrophe early warning model to prevent and control the occurrence of economic disaster risk. BP neural network can, quite in shape nonlinear feature relationship, have true gaining knowledge of adaptability, excessive parallel computing and statistics processing ability. In view of the actual state of affairs of commercial enterprise, business enterprise and economic risk, the BP neural community algorithm is used to predict agency financial risk and a hazard prediction model in particular primarily based on BP neural community is established. The simulation consequences exhibit that the accuracy and correctness of economic hazard conduct early warning primarily based on BP neural network are 91.51% and 95.28%, respectively. It is proved that the fast-warning approach of economic threat that is conducted primarily based on BP neural network has excessively taken a look at the accuracy and robust cognizance ability.