Related Schemes Research Articles

Early maladaptive schemas in female patients with migraine and tension-type headache

Chronic headache is a serious clinical problem in women which psychological factors play major role and requires an approach with bio-psycho-social integrity. Psychiatric comorbidities such as anxiety disorder and major depression are frequent. Young described Early Maladaptive Schemas (EMS) as maladaptive and dysfunctional patterns that appear due to unpleasant situations occurred between patient and people who were important to him. EMS affect perception, emotions, thoughts and behaviors that makes a basis for many disorders. EMS are found to be related with depression, anxiety and somatization within recent literature. In literature, chronic pain and migraine’s relationship with EMS were studied before in a few separate articles. However, there is a lack of data that compares the relationship between EMS and clearly distinguished headache types: migraine (MH) and tension-type headache (TTH) especially in female patients who are more prone to somatic complaints/findings. Our study directly compared three groups, migraine and tension type headache and healthy controls. 150 female patients with chronic headache were enrolled for study in consecutive fashion. Exclusion criteria were as follows: age < 18 or > 60 years, male gender, existence of comorbidity affecting central nervous system, headache due to drug/substance abuse, previous diagnosis of migraine with aura, previous diagnosis of psychotic disease, mental retardation, cognitive disorders, being in delirium state. Patients were grouped into two categories according to type of headache and a third control group. All patients were evaluated with Young Schema Questionnaire (YSQ) and their scores were noted and categorized in related schema domains. Sociodemographic data were comparable among groups. However, there were significant differences in terms of previous psychiatric diagnosis and psychiatric drug usage. When we compared YSQ scores, groups differed in many schema categories. MH group showed significantly higher scores in dependency/incompetency, unrelenting standards and punitiveness schemas when compared to remaining two groups. TTH group had significantly more points in emotional deprivation, vulnerability to harm or illness schemas among all groups. On the other hand, control group had significantly higher scores in insufficient self-discipline and entitlement/grandiosity schemas when compared to both MH and TTH groups. Presence of EMS in female patients with headache significantly differed from control group. Types of EMS were also significantly different between patients with MH and TTH among this whole headache group. We suggest that a comprehensive psychotherapeutic approach that targets to improve coping methods for distinct EMS in distinct headache types would provide critical aid to general treatment of headaches especially in resistant cases.

Childhood adversity and racial discrimination forecast suicidal and death ideation among emerging adult Black men: A longitudinal analysis.

Disproportionate exposure to childhood adversity and the effects of racial discrimination take a toll on Black American men's mental health. Despite increasing rates of suicidal behaviors and thoughts among young adult, Black American men, few longitudinal studies examine their risk for suicidal and death ideation (SDI). We tested a developmental model linking childhood adversity (experiences of deprivation and threatening experiences) and emerging adult exposure to racial discrimination to increases in SDI and examined a potential mechanism for these effects, negative relational schemas. A sample of 504 Black men (Mage = 20.7) from rural Georgia were recruited with respondent-driven sampling and completed a baseline survey. Men participated in two additional follow-up surveys (Mage = 21.9 and Mage = 23.5). Hypotheses were tested using structural equation modeling. Analyses largely supported the proposed model. Childhood adversities were associated directly with reports of SDI. Childhood deprivation indirectly predicted SDI via negative schemas (β = 0.03, 95% CI [.014, .046]). Racial discrimination also indirectly predicted SDI via negative relational schemas (β = 0.01, 95% CI [.001, .018]). Study results suggest that clinical and preventive interventions for suicidality should target the influence of racism and adverse experiences and the negative relational schemas they induce. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Protein–protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis

BackgroundIt is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein–protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.MethodsThe relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.ResultsThe meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.ConclusionsThe implemented workflow could be used for other multifactorial diseases.

Parenting stress in parents with and without a mental illness and its relationship to psychopathology in children: a multimodal examination.

Children of parents with a mental illness are at heightened risk to develop a mental illness themselves due to genetics and environmental factors. Although parenting stress (PS) is known to be associated with increased psychopathology in parents and children, there is no study investigating PS multimodally in a sample of parents with a mental illness. This study aims to compare PS of parents with and without a mental illness and further to examine the relationship between PS and psychopathology of children. Participants were parents with a mental illness and parents without a mental illness and their children aged four to sixteen years. We assessed PS multimodally using a questionnaire, parents' evaluation of children's behavior (relational schemas) and psychophysiological arousal of parents during free speech task. Self-reported PS was increased, and evaluation of children's behavior was more negative and less positive in parents with a mental illness compared to parents without a mental illness. Children's psychopathology was associated with self-reported PS and relational schemas of parents. Regarding psychophysiological arousal, parents with a mental illness showed reduced reactivity in heart rate from baseline to free speech task in comparison to parents without a mental illness. Our findings highlight the importance of implementing intervention programs to reduce PS for parents and children. In particular, parents with a mental illness might benefit from specific intervention programs in order to interrupt the transgenerational transmission of mental disorders.

Lessons from decolonial and liberation psychologies for the field of trauma psychology.

Trauma, ranging from interpersonal to intergenerational, can create severe dysregulation and psychic suffering. Trauma may disrupt the nervous system, identity, affect regulation, and relationship schemas. Traumatic events can also disconnect survivors from the various aspects of themselves as well as their community. As a trauma survivor and trauma psychologist, I have dedicated my career to exploring ways of restoring and healing those severed connections. Exploring decolonial and liberation psychologies awakened me to conceptualizations and frameworks that center reclamation as a form of holistic healing and empowerment for trauma survivors. While much of the individually centered trauma literature focuses on skills-based psychoeducation and cognitive behavioral coping strategies, there has traditionally been less, although growing, attention paid to the diverse culturally grounded, sociopolitical pathways for survivors to reclaim themselves. In this article, I explore my scholarship and the scholarship of other underrepresented scholars as we discuss decolonial and liberation psychologies, the pathways they illuminate that can benefit the trauma recovery process, especially for marginalized survivors, and their implications for practice, training/teaching, research, and policy. The trauma and healing-informed decolonial and liberation pathways that emerge from the literature are culture as medicine, community support, spirituality and religiosity, expressive arts, and resistance. This article argues that the field would benefit from a more inclusive view of trauma and trauma recovery if it incorporates, builds on, explores, and learns from the scholarship of decolonial and liberation psychologists and traditional cultural healers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Time-Varying Resource Graph Based Processing on the Way for Space-Terrestrial Integrated Vehicle Networks

Desirable information processing in space-terrestrial integrated vehicle networks (STINs) handles data distributed in different satellites while transmitting, where efficient modeling time-varying resources is critical. Existing works are not applicable to STINs, however, because they lack the joint consideration of different movement patterns and fluctuating loads. In this paper, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Time-Varying Resource Graph (TVRG)</i> to model dynamic resources in STINs, by leveraging the advantages of software-defined networking in flexible resource management. Firstly, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STIN mobility model</i> to uniformly model different movement patterns in STINs. Then, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">layered Resource Modeling and Abstraction (RMA)</i> approach, where evolutions of node resources are modeled as Markov processes, by encoding predictable topologies and influences of fluctuating loads as states. Besides, we propose the low-complexity domain resource abstraction algorithm by defining two mobility-based and load-aware partial orders on resource abilities. Finally, we formulate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TVRG-based Processing on the Way (TPoW)</i> problem for data flows with processing requirements and multiple sources. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-level Processing on the Way (MPoW)</i> approach with a bounded approximation ratio, realizing adaptive matching of resources and demands of processing and transmission. To evaluate the RMA approach, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TVRG-based Routing (TR)</i> algorithm for time-sensitive and bandwidth-intensive data flows, with the multi-level on-demand scheduling ability. Comprehensive simulation results demonstrate that our RMA-TR and MPoW outperform most related schemes by decreasing nearly 40% bandwidth consumption with the shortest end-to-end delay.

Competency Development of Food Tourism in Thailand

Purpose: Food tourism has become great potential in the tourism industry, especially in Thailand. When foreign tourists come to travel and witness the diverse gastronomic experience at local levels, however, in 2022, Thailand received 11.15 million foreign tourists, and only 19.45% came because of food attractions. Therefore, this study aims to develop competency in food tourism in Thailand. Theoretical Framework: This study examines and reviews the concepts of competitive advantage and food tourism. Competitiveness in terms of human resource is the ability to sell his or her ability over other competitor’s employees. Competitive Advantage in human resource is developed either by himself or by company attitude and resource. Competitive Advantage can be developed by (1) Elevating productivity, (2) Purposeful collaboration, (3) Learning Agility, (4) Social binding, (5) well- being. Moreover, food tourism is defined as “visitation to primary and secondary food producers, food festivals, restaurants and specific locations for which food tasting and/or experiencing the attributes of specialist food production region are the primary motivating factor for travel”. Food tourism is also defined as cultural turn from those early management-focused studies to more wholesome and exploratory discussions of food and culture. Not only did the frequency or volume of the studies significantly increase, but so did the variety of research approaches and concepts be explored. Design/Methodology/Approach: This quantitative research collected data from food tourism stakeholders in Thailand 400, including foreign visitors, local visitors, restaurant owners, culinary academic institutions, and tourism authorities. Data were analyzed in descriptive and inferential statistics, and a structural equation model tested the hypothesis. Findings: The findings reveal that (1) Tourism policy must include food tourism in the tourism event and public relation scheme of tourism authorities, (2) Unique local cultural experiences must be embedded into food stories, and (3) Nutrition and herbal nutrition must be presented for health-conscious tourists. The observed variables had component weights between 0.241 and 0.79. They were significantly different from zero at the 0.01 level. Latent variables were between 0.058 - 1.153, and 2) the harmonization of the hypothetical model and the empirical data found that all criteria were within the acceptable range (Pass) structural equation model analysis (Chi-Square = 44.113, df = 35, p = 0.139, RMSEA = 0.026, GFI = 0.026, AGFI = 0.960). Research Practical and Social Implication: Food tourism has become great potential in the tourism industry, especially in Thailand. When foreign tourists come to travel and witness the diverse gastronomic experience at local levels. However, in 2022, Thailand received 11.15 million foreign tourists; only 19.45% came because of food attractions. Therefore, this study aims to develop competency in food tourism in Thailand. This quantitative research collected data from food tourism stakeholders in Thailand 400, including foreign visitors, local visitors, restaurant owners, culinary academic institutions, and tourism authorities. Data were analyzed in descriptive and inferential statistics; a structural equation model tested the hypotheses. Originality/Value: The findings reveal that (1) Tourism policy must include food tourism in the tourism event and public relation scheme of tourism authorities, (2) Unique local cultural experiences must be embedded into food stories, and (3) Nutrition and herbal nutrition must be presented for health-conscious tourists.

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

SummaryFlying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics.

A systematic overview of data federation systems

Data federation addresses the problem of uniformly accessing multiple, possibly heterogeneous data sources, by mapping them into a unified schema, such as an RDF(S)/OWL ontology or a relational schema, and by supporting the execution of queries, like SPARQL or SQL queries, over that unified schema. Data explosion in volume and variety has made data federation increasingly popular in many application domains. Hence, many data federation systems have been developed in industry and academia, and it has become challenging for users to select suitable systems to achieve their objectives. In order to systematically analyze and compare these systems, we propose an evaluation framework comprising four dimensions: (i) federation capabilities, i.e., query language, data source, and federation techniques; (ii) data security, i.e., authentication, authorization, auditing, encryption, and data masking; (iii) interface, i.e., graphical interface, command line interface, and application programming interface; and (iv) development, i.e., main development language, deployment, commercial support, open source, and release. Using this framework, we thoroughly studied 51 data federation systems from the Semantic Web and Database communities. This paper shares the results of our investigation and aims to provide reference material and insights for users, developers and researchers selecting or further developing data federation systems.

Secure healthcare monitoring of arrythmias in internet of things with deep learning and elgamal encryption

Arrhythmia disorders are the leading cause of death worldwide and are primarily recognized by the patient’s irregular cardiac rhythms. Wearable Internet of Things (IoT) devices can reliably measure patients’ heart rhythms by producing electrocardiogram (ECG) signals. Due to their non-invasive nature, ECG signals have been frequently employed to detect arrhythmias. The manual procedure, however, takes a long time and is prone to error. Utilizing deep learning models for early automatic identification of cardiac arrhythmias is a preferable approach that will improve diagnosis and therapy. Though ECG analysis using cloud-based methods can perform satisfactorily, they still suffer from security issues. It is essential to provide secure data transmission and storage for IoT medical data because of its significant development in the healthcare system. So, this paper proposes a secure arrhythmia classification system with the help of effective encryption and a deep learning (DL) system. The proposed method mainly involved two phases: ECG signal transmission and arrhythmia disease classification. In the ECG signal transmission phase, the patient’s ECG data collected through the IoT sensors is encrypted using the optimal key-based elgamal elliptic curve cryptography (OKEGECC) mechanism, and the encrypted data is securely transmitted to the cloud. After that, in the arrhythmia disease classification phase, the system collects the data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database to perform training. The collected data is preprocessed by applying the continuous wavelet transform (CWT) to improve the quality of the ECG data. Next, the feature extraction is carried out by deformable attention-centered residual network 50 (DARNet-50), and finally, the classification is performed using butterfly-optimized Bi-directional long short-term memory (BOBLSTM). The experimental outcomes showed that the proposed system achieves 99.76% accuracy, which is better than the existing related schemes.

A hybrid deep learning model for detecting DDoS flooding attacks in SIP-based systems

The Session Initiation Protocol (SIP) is widely used for multimedia communication as a signaling protocol to manage, establish, maintain, and terminate multimedia sessions among participants. However, SIP is vulnerable to attacks due to its text-based nature and the lack of adequate security measures. One of the most damaging attacks is the Distributed Denial of Service (DDoS) attack, which consumes resources and blocks legitimate users from accessing the available services. Although various machine learning-based schemes have been proposed to detect DDoS flooding attacks in SIP-based systems, most of them only address INVITE flooding attacks. REGISTER and ACK flooding attacks were not addressed. On the other hand, schemes based on statistical analysis are not sufficiently accurate. In this article, we propose a novel approach classifying DDoS flooding attacks in SIP-based systems using a combination of deep learning and entropy techniques. The proposed approach consists of two components: a hybrid deep learning model and an entropy-based model. The hybrid deep learning model combines convolutional neural networks and a stacked bidirectional long short-term memory network to extract features and classify traffic patterns. The entropy-based model measures the randomness and diversity of traffic flows using Shannon and Rényi entropies. To validate the proposed scheme, we built a balanced dataset of different types and intensities of DDoS flooding attacks. The results showed that the proposed scheme can effectively detect DDoS flooding attacks with high accuracy and low detection time for various attack intensities. Moreover, it surpasses other related schemes in terms of accuracy and detection time.

Application of power battery under thermal conductive silica gel plate in new energy vehicles

This study aims to improve the performance of automotive battery thermal management systems (BTMS) to achieve more efficient heat dissipation and thus reduce hazards during driving. Firstly, the research parameters and properties of composite thermally conductive silicone materials are introduced. Secondly, the heating principle of the power battery, the structure and working principle of the new energy vehicle battery, and the related thermal management scheme are discussed. Finally, the research results are presented from the experimental test and controller design. In addition, to achieve the research goal, the composite thermally conductive silica gel plate (CSGP) material is studied in detail and parametrically analyzed, and the heating mechanism of the power battery is discussed in depth. The temperature characteristics after adding CSGP are experimentally tested, and the controller of the BTMS of the new energy vehicle is designed, including hardware circuits and software modules. The findings show that the temperature characteristics of the battery module have obvious limitations without CSGP. When the battery module operates at a 4C magnification, the temperature exceeds the safety threshold by 38.4%, with particular potential safety risks. Then, the maximum temperature of the battery module with CSGP can be controlled within 50 °C, and the temperature characteristics are prominently improved. Lastly, the controller of the BTMS is tested, and the results reveal that it has remarkable voltage recovery ability. According to the research results, the performance of automotive BTMS can be significantly improved, and better heat dissipation can be effectively achieved by adding CSGP. This helps reduce the hazards of driving. Moreover, the designed controller performs well in voltage recovery, providing solid theoretical support for further developing the CSGP battery management system.

Timed-release encryption anonymous interaction protocol based on smart contract

Timed-release encryption (TRE) is a cryptographic primitive that can control the decryption time and has significant application value in time-sensitive scenarios. To solve the reliability issue of nodes in existing TRE anonymous interaction schemes, we propose a blockchain-based TRE protocol for anonymous query time trapdoors. In our protocol, the recipient divides the encrypted trapdoor request information into n ciphertext fragments using secret sharing technology near the decryption time, and employs the idea of onion routing to perform layer-by-layer encryption, creating onion-type data transmitted through middlemen selected from the smart contract. After receiving the ciphertext fragments, the time server integrates them to obtain the trapdoor request information and returns the corresponding time trapdoor to the recipient. This allows the recipient to query any time trapdoor anonymously. Our protocol provides a normative design for the smart contract and specific constraints on the participants’ behavior. Compared with the related anonymous query trapdoor schemes, our protocol improves the probability of successful queries. Security analysis shows that our protocol can resist release-ahead attack, interruption attack, eavesdropping attack, and replacement attack. Performance analysis shows that our protocol outperforms related protocols regarding anonymity, efficiency, and flexibility, achieving highly efficient anonymous interactions. Finally, we conducted an experiment in the Ethereum Rinkeby test network. For the settings of ciphertext fragment number n=3 and ciphertext fragment threshold t=2, the gas consumption for a user to execute the contract was $5.66, which was higher than the contract cost of related schemes, but the contract execution cost was within an acceptable range.

Secure and efficient intelligent transport system based on cryptographic authentication mechanism for vehicular ad hoc networks

Vehicular Adhoc Networks (VANETs) are two of the future’s most commonly used communication systems. VANETs are designed to protect vehicle drivers by establishing autonomous contacts with nearby vehicles. Vehicles operate as intelligent mobile nodes in adhoc networks characterized by mobility and complexity. Cybersecurity in VANET is affected by malicious attacks, which must be detected and prevented. The overall performance of the proposed GPCR-MA with blackhole and Sybil attack is average latency, packet transmission ratio, energy consumption and throughput. It is predicted because the Blackhole attack vehicle sends answers instantly without checking its routing table. These changes in the metrics used contribute to the network efficiency deteriorating primarily in the face of an attack. Compared with other related schemes, our proposal provides privacy and security guarantees for VANETs while improving efficiency.

Relation-aware deep neural network enables more efficient biomedical knowledge acquisition from massive literature

Biomedical knowledge is typically organized in a relational scheme, such as chemical-disease relation, gene-disease relation, and gene-pathway relation. Biomedical scientists heavily rely on search engines to acquire up-to-date relational knowledge from massive biomedical articles. The navigation efficiency of the retrieval process, however, is significantly restricted by keyword matching techniques unaware of the biomedical relations of these keywords in articles. To bridge the gap between existing retrieval techniques and practical access demands for relational knowledge, we present a novel framework, Biomedical Relation-Aware Document Ranking (BioRADR), capable of retrieving articles expressing specific relations with respect to the queried entity pair. Based on a deep neural network, BioRADR can be trained from large-scale data automatically annotated via distant supervision, and empirical evaluation reveals that it outperforms the strongest baseline by over 8 points in NDCG@1. We implement an online system (http://bioradr.ai.thunlp.org/) based on BioRADR, enabling more efficient relation-oriented retrieval of biomedical articles.

A practical and UC-secure decentralized key management and authentication scheme based on blockchain for VNDN

In Vehicular Named Data Networking (VNDN), most of the existing key management mechanisms still rely on the hierarchical key trust model or the Public Key Infrastructure (PKI), in which the centralized certificate authority is used as a Trusted Third Party (TTP) to provide a signature for the user?s public key. Thus, the TTP becomes vulnerable to attacks and maybe there exists a singlepoint failure problem. In addition, the in-network caching in the VNDN may incur a threat to the system and make it is vulnerable to the DoS attack caused by Interest flooding aimed at the Content-Store. To tackle these security issues, we firstly propose an efficient decentralized key management solution based on blockchain for VNDN. Secondly, based on the proposed key management scheme, a lightweight mutual authentication scheme and a key agreement protocol for V2X are respectively proposed in this paper. Finally, we analyze the security attributes of our solutions in the Universally Composable (UC) framework. Our analysis results show that our novel schemes can meet the security requirements of our solutions. In addition, our experimental results show our new schemes have highly efficient, lower computation and communication costs than other related schemes.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Similar Formation Control via Range and Odometry Measurements.

This article investigates the similar formation control problem for multirobot systems. Specifically, we propose an integrated relative localization and similar formation control scheme to navigate multirobot systems to a desired configuration, which is a similar transformation of a given template, based on interrobot and robot-landmark range measurements and odometry measurements of robots themselves. To achieve the exact relative localization, a persistent excitation (P.E.) signal is introduced in the controller which, however, perturbs the motion of each robot and affects the formation accuracy. To resolve the conflict, an autonomous system with its output regulated by a carefully designed function of range measurements is introduced to generate the persistent excitation. It is proved that the similar formation control problem can be solved by our proposed scheme with global asymptotic convergence for directed acyclic graphs (DAGs). Both numerical simulation and physical experiment are presented to verify and validate the effectiveness of our theoretical findings.

Managing the Complexity: An Ethnographic Approach to Understanding Noticing Within Orchestration

Sport coaching is an inherently complex endeavor. To manage this complexity, some coaches engage in orchestration to plan, organize, monitor, and respond to the dynamic sport environment. Using an ethnographic approach, the current research aimed to understand how a National Collegiate Athletic Association (NCAA) Division II female basketball coach orchestrated the complex and relational nature of coaching over the course of a sport season and with particular attention to how noticing occurred and informed orchestration. A combination of formal and informal coach and player interviews, observation and field notes, and audio reflections of the coach was carried out over the course of the season. The authors used a realist tale to illustrate the complexity of the coaching experience and how the coach used noticing and relational schemas to navigate ambiguity, the micropolitical landscape, and interpersonal relationships to steer the team toward personal growth and high performance.

“함께하는 힘이 이렇게 세구나!”: 달빛초 세계시민교육 교원 연구팀의 협력적 관계구조 형성에 관한 질적 사례연구

This qualitative case study explored the collaborative relational structure of a research team of educators at Moonlight primary school (pseudonym), which enabled the mainstreaming of global citizenship education(hereafter, GCED) into the school over the past three years. To do so, this research employed the relational AGIL scheme(Donati, 2022) as a theoretical framework to analyze changes in the purpose, values, means, and norms of the research team’s relational structure. The findings revealed that the research team underwent transition from an initially contrived collegiality to a later reciprocal one, with the turning point marked by a change in leadership. The late-stage research team’s reciprocal relational structure was characterized by a pursuit of situational goals (G) related to the mainstreaming of GCED at Moonlight primary school, grounded in values of mutual respect(L), a sophisticated planning of various means(A) meticulously designed for mainstreaming GCED, and an emphasis on ethical responsibilities within teacher relationships(I), forming an AGIL relational structure. The key mechanism in shaping this reciprocal relational structure(AGIL) was the relational reflexivity of the research team members. They, as relational subjects, were able to recognise the relational order of reality, that is, we-‘relation’, a third entity constructed by team members that remains invisible but embodies the potential for teacher collaboration.