Deep Relationships Research Articles

Wounded Healers: Job Termination in Faith Community Nursing.

Research regarding faith community nursing termination is scarce. Ziebarth (2018) surveyed faith community nurses (FCNs) who had experienced termination of their position. This article explores FCN feelings related to termination and provides an evidence-based practice component to translate the research into practice. Given that FCNs engage in deep relationships with clients, their feelings following termination express grief and lack of support. Support strategies as well as preemptive strategies that may be useful to prevent loss of an FCN position or ministry are presented.

Influence of relational resources on the development of relational operating skills

The debate about relational resources and their influences has followed an internal focus on organizations, and in the literature this has been shown to have a high influence on the ways of the resource-based view. This article aims to analyze the development of operational competencies following the focus of the relational view from the interaction of relational resources shared in supply chains. The research was guided by the strategy of multiple case studies in the steel industry, automotive and industrial applications, pulp production, and engineering for the energy industry. The results of the analysis of the four cases show that deeper relationships in the form of partnerships enable a process of investing in specific assets, knowledge accumulation, learning exchange, and a combination of complementary strategic resources that develop relational operational competencies. And, these competencies that represent the company's ability to promote a skill set to use resources efficiently and to provide a barrier to imitation are further developed by the information and knowledge constructs.

How Do Clinical Electives during the Clerkship Year Influence Career Exploration? A Qualitative Study

Problem: Although many students begin medical school with some idea of their specialty interest, up to 80% of these students choose a different specialty by their final year. This pivot tends to happen in the clerkship year, when students are immersed in the clinical environment, gaining a practical understanding of the day-to-day work in different fields. Yet, in this year students have limited experiences with specialties. Clinical electives during the clerkship year may aid students in career development. The authors examined student career exploration through the lens of social cognitive career theory (SCCT). SCCT posits three variables that influence career development: personal goals, self-efficacy, and understanding outcome expectations. With this framework, the authors sought to understand how a program of clinical electives during the clerkship year influences students’ perceptions of their career exploration. We aimed to: (1) describe an innovative clerkship elective program designed for career exploration, and (2) explore how this influenced students’ career exploration using qualitative analysis. Intervention: Beginning in 2018, students at our institution were required to participate in three 2-week clinical electives during their clerkship year, called Clinical Immersive Experiences (CIExes). CIExes were categorized into four different types: apprenticeship, clinical skills building, integrative (multi-disciplinary), or subspecialty. Authors invited fourth year students to participate in interviews (January to March 2019) about how they selected electives and how these electives contributed to their career exploration. Interviews continued until reaching information sufficiency. Authors coded and analyzed transcripts using template analysis. Context: This curricular intervention took place in the context of large-scale curricular redesign. Students began clerkships partway into their second year of medical school. The family and community medicine clerkship, which was previously a 6-week core clerkship, was changed to a longitudinal format, thus freeing up 6 weeks for electives. Other core clerkships included anesthesia (2 weeks), medicine (8 weeks), neurology (4 weeks), obstetrics and gynecology (6 weeks), pediatrics (6 weeks), psychiatry (4 weeks), and surgery (8 weeks). Impact: From 15 student interviews, we identified three major themes. First, CIExes facilitated personalized career exploration. All students felt that at least one elective helped them solidify their decision about a specialty choice. Second, CIExes promoted focused learning and skills development that complemented core rotations. They noted the benefit of positive relationships with supervisors, particularly attendings, during these electives. Third, students highlighted how these electives fostered a positive learning environment and enhanced wellbeing. SCCT clarified how the CIEx program helped students advance their personal goals, self-efficacy, and outcome expectations during a pivotal time in medical school. Lessons Learned: We learned that from the student perspective, the inclusion of clinical electives in the clerkship year benefited students’ career exploration by helping them develop and refine their career goals, increase self-efficacy, and test outcome expectations in a meaningful way as anticipated from SCCT. In addition, we found that CIExes created a positive learning environment that allowed deep relationships to develop in fields of interest and that supported a strong sense of wellbeing. Supplemental data for this article is available online at https://doi.org/10.1080/10401334.2021.1891545

The magnificent territory: Pausing to reflect on a lifetime of working with people and music

In this biographical account of a musical lifecourse, I highlight the psychosocial, vocational, functional and affective meaning of music. Using my own musical journey as an illustrative example, I draw attention to the ways in which music can be a catalyst for understanding of the self, for meaning-making in the world and for developing understandings of one’s role in that world. My overarching message is that a musical lifecourse can frame our deepest relationships and most significant moments of profound joy.

Agricultural extension for promoting citrus fruit growers’ innovation and profitability in Mexico

The citrus fruit grower has hardly adopted innovations, resulting in reduced income and profitability. This study aims to apply andragogic training and extension techniques to a group of 150 citrus fruit growers from five different communities in Veracruz, Mexico, to improve the adoption of social and technological innovations, their income, and profitability. The percentage of adoption of innovations (PAI) was calculated using the equation: PAI = n/N × 100, where n was the number of innovations adopted by the grower and N the total number of innovations available in the technological package. Two previous surveys, social and technological, were conducted in 2012 and revealed a significant lack of coordination in innovation networks, mainly in deep relationships, and little use of technological innovations. After the andragogic training and extension, better coordination was achieved, reflected in numerous and robust links, more nodes, and higher density, especially at the level of trust, contributing to consolidating social networks for 2014. The most adopted technological category was fertilization management. A survey was conducted in 2016 to quantify the permanence of the adoption of innovations. We conclude that social and technological intervention through andragogic training and extension improved the yield of orchards by more than 50 %, increased the income of citrus fruit growers 4.5 times, and consolidated workgroups and the permanence of innovations.

A survey of miRNAs involved in biomineralization and shell repair in the freshwater gastropod Lymnaea stagnalis

MicroRNAs (miRNAs) are a deeply conserved class of small, single stranded RNA molecules that post-transcriptionally regulate mRNA levels via several targeted degradation pathways. They are involved in a wide variety of biological processes and have been used to infer the deep evolutionary relationships of major groups such as the Metazoa. Here we have surveyed several adult tissues of the freshwater pulmonate Lymnaea stagnalis (the Great Pond Snail) for miRNAs. In addition we perform a shell regeneration assay to identify miRNAs that may be involved in regulating mRNAs directly involved in the shell-forming process. From seven mature tissues we identify a total of 370 unique precursor miRNAs that give rise to 336 unique mature miRNAs. While the majority of these appear to be evolutionarily novel, most of the 70 most highly expressed (which account for 99.8% of all reads) share sequence similarity with a miRBase or mirGeneDB2.0 entry. We also identify 10 miRNAs that are differentially regulated in mantle tissue that is actively regenerating shell material, 5 of which appear to be evolutionarily novel and none of which share similarity with any miRNA previously reported to regulate biomineralization in molluscs. One significantly down-regulated miRNA is predicted to target Lst-Dermatopontin, a previously characterized shell matrix protein from another freshwater gastropod. This survey provides a foundation for future studies that would seek to characterize the functional role of these molecules in biomineralization or other processes of interest.

Towards on-site hazards identification of improper use of personal protective equipment using deep learning-based geometric relationships and hierarchical scene graph

Construction sites are one of the most perilous environments where many potential hazards may occur. Personal Protective Equipment (PPE) is an important safety measure used to protect construction workers from accidents. However, PPE usage is not strictly enforced among workers due to all kinds of reasons. This paper proposes a unified model, which enjoys both perceptual and reasoning capabilities, to help to facilitate the safety monitoring work of construction workers to ensure PPE is appropriately used. In contrast to commonly used object detection-based identification approaches, this paper provides a novel solution to identify improper use of PPE by the combination of deep learning-based object detection and individual detection using geometric relationships analysis. Moreover, this paper presents a hierarchical scene graph structure that enables the conditional reasoning for automated hazards identification to address different requirements in each zone of construction sites. The experimental results demonstrate that the proposed approach was capable of identifying the hazards of improper use of PPE with high precision (94.47%) and recall rate (83.20%) while ensuring real-time performance (15.62 FPS on average).

A graph-based QoS prediction approach for web service recommendation

With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in both industry and academia. However, it is hard to make an accurate recommendation using sparse QoS data, which makes QoS prediction a growing concern in the context of web service recommendation. In this research, a novel Graph-based Matrix Factorization approach(GMF) is proposed for QoS prediction. First, a concept of integrated-graph is put forward to consolidate multi-source information from user–aware context and service-aware context, and to deep mine potential relationships based on QoS matrix. Furthermore, the integrated-graph is divided into several sub-graphs by cutting insignificant edges to reduce noises and strengthen interactions between users and services. Based on the local information of each sub-graph and the global information of integrated-graph, a Gaussian Mixture Model(GMM) of QoS value is built as a fusion method to combine local and global information adaptively and to complete final QoS prediction. The extensive experimental analysis on a publicly available dataset indicate that our graph-based method is both accurate and practical.

Guest Editorial: Special Issue on Hybrid Human–Artificial Intelligence for Social Computing

The unprecedented development of the Internet of Things (IoT), artificial intelligence (AI), and Big Data has stimulated a boom of social networks such as Twitter, WeChat, Facebook, etc., generating a huge amount of social data that are worth further analysis. Social computing has an important focus on mining the deep relationships between social organizations, networks, and media. The increasing volumes and complexities make big social data mining more and more difficult. Hybrid Human–Artificial Intelligence (H-AI) is an approach combining both human intelligence and AI, so as to handle demanding problems in a harmonious way. By adopting H-AI in social computing, it would provide more possibilities for social data analysis, relationship discovery, outlier detection, and prediction, and is proving to be an emerging and promising direction for AI and big data research.

Exploring the impact of RNA editing on mitochondrial phylogenetic analyses in liverworts, an early land plant lineage

AbstractPlant mitochondrial protein‐coding genes are slow evolving, and therefore are less subject to substitution saturation, and therefore perhaps more suitable for resolving deep relationships of high taxonomic categories. Plant mitochondrial genes hold, however, hundreds of RNA editing sites, involving mostly non‐synonymous substitutions in the first and second codon positions, which has been reported to affect phylogenetic reconstructions. We have previously identified ca. 4700 mitochondrial RNA editing sites within a group of liverworts representing the ordinal diversity of liverworts, allowing us to critically evaluate the impacts of RNA editing sites on phylogenetic reconstructions in liverworts. Our phylogenetic inferences are mostly congruent on topology inferred from the original mitochondrial gene dataset, dataset with RNA editing sites corrected, and dataset with RNA editing sites excluded. The RNA editing site excluded dataset recovered a topology identical to that of the RNA editing site corrected dataset, supporting the sister relationship of Ptilidium and Jungermanniales, whereas the original dataset supported the sister relationship of Ptilidium and Jungermanniidae. The controversial placements of Ptilidium could be explained by site‐wise log‐likelihood analysis, as the majority of liverwort RNA editing sites supported the sister relationship of Ptilidium and Jungermanniidae, hence the correction or exclusion of the RNA editing sites changed the tree topology and supported the sister relationship of Ptilidium and Jungermanniales. Our study shows that RNA editing sites potentially impact phylogenetic analyses, suggesting that both genome and transcriptome derived data should be used with caution for phylogenetic reconstruction with genes hosting vast numbers of RNA editing sites such as plant organellar genes.

Mitochondrial genes from 18 angiosperms fill sampling gaps for phylogenomic inferences of the early diversification of flowering plants

AbstractThe early diversification of angiosperms is thought to have been a rapid process, which may complicate phylogenetic analyses of early angiosperm relationships. Plastid and nuclear phylogenomic studies have raised several conflicting hypotheses regarding overall angiosperm phylogeny, but mitochondrial genomes have been largely ignored as a relevant source of information. Here we sequenced mitochondrial genomes from 18 angiosperms to fill taxon‐sampling gaps in Austrobaileyales, magnoliids, Chloranthales, Ceratophyllales, and major lineages of eudicots and monocots. We assembled a data matrix of 38 mitochondrial genes from 107 taxa to assess how well mitochondrial genomic data address current uncertainties in angiosperm relationships. Although we recovered conflicting phylogenies based on different data sets and analytical methods, we also observed congruence regarding deep relationships of several major angiosperm lineages: Chloranthales were always inferred to be the sister group of Ceratophyllales, Austrobaileyales to mesangiosperms, and the unplaced Dilleniales was consistently resolved as the sister to superasterids. Substitutional saturation, GC compositional heterogeneity, and codon‐usage bias are possible reasons for the noise/conflict that may impact phylogenetic reconstruction; and angiosperm mitochondrial genes may not be substantially affected by these factors. The third codon positions of the mitochondrial genes appear to contain more parsimony‐informative sites than the first and second codon positions, and therefore produced better resolved phylogenetic relationships with generally strong support. The relationships among these major lineages remain incompletely resolved, perhaps as a result of the rapidity of early radiations. Nevertheless, data from mitochondrial genomes provide additional evidence and alternative hypotheses for exploring the early evolution and diversification of the angiosperms.

Facilitating Sustainable Disaster Risk Reduction in Indigenous Communities: Reviving Indigenous Worldviews, Knowledge, and Practices through Two-Way Partnering.

The Sendai Framework of Action 2015–2030 calls for holistic Indigenous disaster risk reduction (DRR) research. Responding to this call, we synergized a holistic philosophical framework (comprising ecological systems theory, symbolic interactionism, and intersectionality) and social constructionist grounded theory and ethnography within a critical Indigenous research paradigm as a methodology for exploring how diverse individual and contextual factors influence DRR in a remote Indigenous community called Galiwinku, in the Northern Territory of Australia. Working together, Indigenous and non-Indigenous researchers collected stories in local languages using conversations and yarning circles with 20 community members, as well as participant observations. The stories were interpreted and analysed using social constructivist grounded theory analysis techniques. The findings were dialogued with over 50 community members. The findings deeply resonated with the community members, validating the trustworthiness and relevance of the findings. The grounded theory that emerged identified two themes. First, local Indigenous knowledge and practices strengthen Indigenous people and reduce the risks posed by natural hazards. More specifically, deep reciprocal relationships with country and ecological knowledge, strong kinship relations, Elder’s wisdom and authority, women and men sharing power, and faith in a supreme power/God and Indigenous-led community organizations enable DRR. Second, colonizing practices weaken Indigenous people and increase the risks from natural hazards. Therefore, colonization, the imposition of Western culture, the government application of top-down approaches, infiltration in Indigenous governance systems, the use of fly-in/fly-out workers, scarcity of employment, restrictions on technical and higher education opportunities, and overcrowded housing that is culturally and climatically unsuitable undermine the DRR capability. Based on the findings, we propose a Community-Based DRR theory which proposes that facilitating sustainable Indigenous DRR in Australian Indigenous communities requires Indigenous and non-Indigenous partners to genuinely work together in two-directional and complementary ways.

The Deep Evolutionary Relationships of the Morphologically Heterogeneous Nolinoideae (Asparagaceae) Revealed by Transcriptome Data.

The subfamily Nolinoideae of Asparagaceae is an extremely morphologically heterogeneous group, which is comprised of seven lineages, formerly known as Eriospermaceae, Polygonateae, Ophiopogoneae, Convallarieae, Ruscaceae s.s., Dracaenaceae, and Nolinaceae from different families or even orders. Their drastically divergent morphologies and low level of molecular resolution have hindered our understanding on their evolutionary history. To resolve reliable and clear phylogenetic relationships of the Nolinoideae, a phylogenetic study was conducted based on transcriptomic sequencing of 15 species representing all the seven lineages. A dataset containing up to 2,850,331 sites across 2,126 genes was analyzed using both concatenated and coalescent methods. Except for Eriospermum as outgroup, the transcriptomic data strongly resolved the remaining six lineages into two groups, one is a paraphyletic grade including the woody lineages of dracaenoids, ruscoids, and nolinoids and a monophyletic herbaceous clade. Within the herbaceous group, the Ophiopogoneae + Theropogon is sister to a clade that is composed of Convallarieae and the monophyletic Polygonateae. Our work provides a first robust deep relationship of the highly heterogeneous Nolinoideae and paves the way for further investigations of its complex evolution.

Key Competencies of Agricultural Managers in the Acute Stage of the COVID-19 Crisis

The acute phase of the COVID-19 crisis exacerbated the need for managerial skills of agricultural managers, provoked another wave of discussion on content of managerial competencies in times of crisis, and revealed a significant research gap. The main goal of our study is to identify the impact of competencies of agricultural crisis managers on the performance of employees in the acute phase of the crisis, when the performance was influenced by their subjective perception and evaluation of their working conditions, satisfaction and safety. We used statistical mediation to examine the connections and deeper relationships between several variables. The hypothesis of dependence between competencies of crisis management and performance of employees, mediated by information sharing, teamwork and cognitive diversity, has been confirmed. Partial mediation has been identified, when only part of the effect is mediated by the mediator variables, however the substantial one. The remaining, smaller part is transmitted directly. Employees’ performance in an acute crisis phase can be influenced by competent crisis management and enhance its effect through information sharing and teamwork support. The cognitive diversity of crisis management did not prove significant in our study. The emphasis is on consistency and the resulting sense of security and safety.

Determining and representing value in creative placemaking

Purpose This study aims to address the research gap about value in the holistic discourse of creative placemaking. It identifies and synthesises the often discounted social and environmental values of creative placemaking along with typically emphasised economic values. Design/methodology/approach This paper builds upon two research phases; first, a review and extraction of creative placemaking value indicators from relevant current urban, cultural and planning literature; and second, the identification of relevant, practice-based, value indicators through interviews with 23 placemaking experts including practitioners, urban planners, developers and place managers from the two largest cities of NSW, Australia; Sydney and Newcastle. Findings This study identifies three broad thematics for valuing creative placemaking along with several sub-categories of qualitative and quantitative indicators. These indicators reveal the holistic value of creative placemaking for its key stakeholders, including expert placemakers, designers, building developers, government and community groups. A key conclusion of the research is the need for tools that grasp the interconnected, and at times conflicting, nature of placemaking’s social, economic and environmental outcomes. Originality/value While a variety of value indicators exist to understand the need for ongoing resourcing of creative placemaking, stakeholders identified the limitations of current approaches to determine, represent and appraise the value of creative placemaking. The indicators of value proposed in this research consolidate and extend current discourse about the value of creative placemaking specifically. The indicators themselves have profound practical implications for how creative placemaking is conceived, executed and evaluated. Theoretically, the study builds on the deep relationships between values and practice in creative placemaking, as well as critiquing narrow forms of evaluation that entrench economic benefits over other outcomes.

Abnormal Bile Acid Metabolism is an Important Feature of Gut Microbiota and Fecal Metabolites in Patients with Slow Transit Constipation

Background: Destructions in the intestinal ecosystem are implicated with changes in slow transit constipation (STC), which is a kind of intractable constipation characterized by colonic motility disorder. However, the role of gut microbes in the pathophysiology of STC and deep relationships between the gut microbiota and fecal metabolites have not been understood. Methods: Fecal samples of 30 patients suffering from STC and 30 healthy subjects were performed 16S rDNA amplicon sequencing and metabolomics to obtain evidence of distinct phenotypes of fecal microorganisms and metabolites. The relationship between fecal microorganisms, metabolites, and clinical manifestations was explored according to the correlation analysis. Findings: The α-diversity of the STC group was changed to a certain degree, and the β-diversity was significantly different, which indicated that the composition of the gut microbiota of STC patients was inconsistent with healthy subjects. Different metabolites between STC and control group were involved in the process of bile acids and lipid metabolism. The colon histomorphology of STC patients was significantly disrupted, and TGR5 and FXR were significantly down-regulated. The differences in metabolites were related to changes in the abundance of specific bacteria and patients' intestinal dysfunction. Analysis of the fecal genomics and metabolomics enabled separation of the STC from controls based on random forest model prediction (STC vs control (14 gut microbiota and metabolite biomarker s) – Sensitivity: 1, Specificity: 0.877) Interpretation: Abnorma fecal microorganisms and metabolites related to bile acid metabolism is characteristic of STC, which provided a perspective for the diagnosis and intervention of STC. Funding: This work was supported by the National Key R&D Program of China (2018YFC1706506), Foundation of Tianjin Municipal Health Commission (No. ZC20097), Foundation of Tianjin Union Medical Center (No. 2020YJ017, 2017YJZD005), and National Natural Science Foundation of China (No. 82174374). Declaration of Interest: All authors declare that they do not have any competing interests. Ethical Approval: This study was conducted in Tianjin Union Medical Center, Tianjin, China. The samples and clinical data used in this study were obtained with approval of institutional review boards and under conditions of informed consent (China Clinical Trial Registry, ChiCTR2000033227).

A study on social media marketing podcast of food and beverage brands with reference to Tamilnadu

Social media marketing (SMM) is a form of Internet marketing that utilizes networking websites as a marketing tool. The goal of SMM is to produce content that users will share with their social network to help a company increase brand exposure and broaden customer reach. In the current scenario, social media marketing is very important for all kinds of products, especially food and beverage products / goods / items. Social media marketing influence the purchasing behavior of a consumer a lot. Various techniques are also notably emerging in the current trend. Like traditional marketing, the business reaches out to their target audience, but unlike traditional marketing, your audience can talk back, interact, and be a part of the medium itself. This interaction is very different from what most marketers are used to, but embracing this marketing style can lead to a new understanding of consumer and deeper relationships.

Current practices in clinical supervision in psychosocial oncology

Abstract Purpose: Clinical supervision is a common pedagogical practice, but one that has received little scrutiny. This descriptive study examined the state of supervision in psycho-oncology in the United States. We were interested in learning more about how supervision is being conducted to better understand how training and clinical supervision can be improved in psycho-oncology. Design/Sample: Fifty-three psychosocial oncology mental health professionals completed an electronic survey. Methods: Participants were asked about their training in supervision, how they practice, what is unique about supervising in an oncology setting, and whether participants viewed their training as sufficient. Findings: Most respondents (50; 94.3%) had received some training in supervision; however, about half (28, 52.8%) reported that they had not received enough training to be an effective supervisor of trainees working with cancer patients. The majority (49, 92.5%) believed it is necessary to gain specific training to be an effective supervisor in oncology settings. In open-ended responses, participants shared what they found unique about supervising in psychosocial oncology; 4 themes emerged. These were the considerations of working in a medical setting, differences in the process and structure of therapy, deeper relationships with colleagues and patients, and the effect of clinical work on the therapist/supervisee. Conclusions/Implications: More than half of our sample of psychosocial oncology practitioners in the United States felt that they were inadequately prepared to supervise and believed it is necessary to pursue specific training in this area. Enhanced training in supervision should be offered because of the specialized demands of practicing in oncology settings.

Collaborative Neural Social Recommendation

Collaborative filtering (CF) is one of the most popular techniques for building recommender systems. To overcome the data sparsity in CF, social recommender systems have emerged to boost recommendation performance by utilizing social correlation among users’ interests. Recently, inspired by the immense success of deep learning for embedding learning, neural network-based recommender systems have shown promising recommendation performance. Nevertheless, few researchers have attempted to tackle the social recommendation problem with neural models. To this end, in this paper, we design a neural architecture that organically combines the intrinsic relationship between social network structure and user–item interaction behavior for social recommendation. Two key challenges arise in this process: first, how to incorporate the social correlation of users’ interests in this neural model, and second, how to design a neural architecture to capture the unique characteristics of user–item interaction behavior for recommendation. To tackle these two challenges, we develop a model named collaborative neural social recommendation (CNSR) with two parts: 1) a social embedding part and 2) a collaborative neural recommendation (CNR) part. In CNSR, the user embedding leverages each user’s social embedding learned from an unsupervised deep learning technique with social correlation regularization. The user and item embeddings are then fed into a unique neural network with a newly designed collaboration layer to model both the shallow collaborative and deep complex interaction relationships between users and items. We further propose a joint learning framework to allow the social embedding part and the CNR part to mutually enhance each other. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.

An abnormal traffic detection method in smart substations based on coupling field extraction and DBSCAN

Smart Substation becomes more vulnerable to cyber attacks due to the high integration of information technologies, so it is essential to detect intrusion behaviour by abnormal traffic analysis in smart substations. Although there have been many detection methods for abnormal traffic, the existing ones all focus on the format check of a single field of the industrial transmission protocol, and ignore the deep coupling relationships among multiple protocol fields, which lead to more or less false detections and missed detections. To overcome this problem and further improve the detection accuracy, in this paper, we propose an abnormal traffic detection method based on the coupling field extraction and the density-based spatial clustering of applications with noise (DBSCAN). By using correlation analysis to extract the coupling fields of the protocol fields and using DBSCAN to remove the noise in the coupling fields, the deep coupling relationship between the coupling fields can be mined by the piecewise linear function fitting method, and used to detect abnormal traffic. The simulation results on 10,000 frames traffic prove that the proposed detection method can effectively identify the abnormal traffic.