Dynamic Circumstances Research Articles

Clinical evaluation of a smart wristband for monitoring oxygen saturation, pulse rate, and respiratory rate.

Recently, photoplethysmography-based vital parameter measurements have increased in popularity. However, clinical evaluation of these measurements is lacking. The objective of this study was to rigorously evaluate the clinical accuracy and reliability of a novel photoplethysmography-based wristband for measuring key vital parameters-oxygen saturation (SpO2), respiratory rate (RR), and pulse rate (PR)-during heart catheterisations. Vital parameters obtained during heart catheterisations by means of a photoplethysmography-based wristband (CardioWatch 287-2, Corsano Health) were compared to reference measurements performed by a Nellcor fingerclip (SpO2, PR) as well as a 5-lead ECG (RR) (QMAPP Haemodynamic Monitoring module, Fysicon B.V.) by means of correlation coefficients and root means squared error (RMSE). Effects of skin colour and arm hair density were additionally evaluated. In total, 945 samples from a total of 100 patients were included in the analysis. The correlation coefficients and RSME obtained for the difference between reference and photoplethysmography-based wristband measurements were r = 0.815 and 1.6% for SpO2, r = 0.976 and 0.9 brpm for RR, and r = 0.995 and 1.3bpm for PR. Similar results were obtained across all skin colour and arm hair density subcategories. This study shows that photoplethysmography-based SpO2, RR, and PR measurements can be accurate during heart catheterisations. Future investigations are required to evaluate the wristband's performance under dynamic circumstances as well as over an extended time period. Trial registration: www.clinicaltrials.gov, NCT05566886.

Multi-fault diagnosis of lithium battery packs based on comprehensive analysis of locally weighted Manhattan distance and voltage ratio

The diagnosis of faults in lithium-ion battery packs is pivotal to ensuring the operational safety of electric vehicles. A fault diagnosis method is introduced to address the lack of a discharge phase and the existence of a single fault type in traditional diagnostic methods. This method relies on a comprehensive assessment involving locally weighted Manhattan distance and voltage ratio anomalies. The KD-Tree-MLLE dimensionality reduction technique is utilized for fast data processing, which improves the computational efficiency by 1.3 % compared to the original MLLE algorithm. The fault detection method utilizing locally weighted Manhattan distance can meet the same threshold evaluation criteria as the charging phase. This approach resolves the constraint of a single phase and accomplishes comprehensive fault detection. The precision and efficacy of the methodology are confirmed through F1-score evaluation. Accuracy and recall rates are compared under varying thresholds during the charging and discharging phases to determine the optimal threshold value. The ratio between the voltage data of the defective battery and the average voltage data of the normal batteries is calculated, and the comprehensive anomaly analysis method based on the voltage ratio and temperature is proposed. The use of comprehensive data such as voltage ratio and temperature to judge the type of fault can simultaneously achieve the judgment of a single fault and mixed fault types. The applicability of locally weighted Manhattan distance under dynamic circumstances and the efficacy of the voltage ratio analysis method across charging and discharging phases are confirmed through the establishment of an experimental and simulation platform dedicated to lithium-ion batteries. Findings indicate that all faults in both phases can be accurately located when the threshold is set to 0.17. This affirms the superior accuracy of the proposed method in detecting and localizing individual faults and combinations of faults quickly and reliably.

Empowered, Strained, or Stable: Teachers’ Experiences of Occupational Well-Being During the Two-Year Pre-Primary Education Trial in Finland

ABSTRACT Research Findings: This study investigated early childhood education and care (ECEC) teachers’ experiences of occupational well-being during a two-year pre-primary education trial in Finland. Profile groups of the trial experimental group teachers (n = 376) were identified. We also examined whether the identified profiles differed with respect to different ECEC classroom and teacher professional characteristics. Through latent profile analysis, three profile groups of occupational well-being were identified among the ECEC teachers: empowered (n = 34), strained (n = 138), and stable teachers (n = 200). In the majority of the pre-primary groups of empowered teachers, children represented the same age group, whereas the age distribution varied more among the pre-primary groups of strained and stable teachers. Empowered teachers reported having more instructed activity sessions and distributing responsibility more among the personnel teams. Strained teachers reported having more individual responsibility for instructing group activities. Practice or Policy: Attention to ECEC teachers’ occupational well-being should be paid in dynamic and changing circumstances of their work careers, such as the Finnish two-year pre-primary education trial. Nationally unitary and appropriate classroom resources should be guaranteed to support ECEC teachers’ well-being both in trials and in their everyday work.

SUSTAINABLE ENTREPRENEURSIP AND ENVIRONMENTAL DYNAMISM OF SELECTED METER MANUFACTURING COMPANIES IN LAGOS STATE

In the fields of business and environmental studies, sustainable entrepreneurship and environmental dynamism are inevitable concepts that drives the economy .Sustainable businesses may need to be especially inventive in highly dynamic circumstances, utilizing their knowledge of sustainability to foresee and adapt to changes.Therefore, the study was designed to investigate sustainable entrepreneurship and environmental dynamism of selected meter manufacturing companies in Lagos State. The study adopted a survey research design and the study relied on primary data through structural self-administered questionnaire. The targeted population of this study is 207 mid-level and top management level employees of MMCs in Lagos state. The list of MMCs was drawn from Directory of Certified Electricity Meters and Instruments Manufacturing /Assembling Companies and Nigerian Electricity Management Services Agency who served as a regulatory body to MMCs in Nigeria. Taro Yamane formula was used to choose 136 participants to which the questionnaires were administered and returned. Multiple regression and Pearson correlation method of analyses were used for hypotheses of the study. Findings revealed that social and economic sustainability are significant predictors of market dynamism of MMCs in Lagos State. The study concludes that both social sustainability and economic sustainability have a positive and significant influence on market dynamism. The study therefore, recommended that MMCs should always come up with both economic and social sustainability policy that can mitigate the effect of market dynamism and improve their operations.

The Role of Transformational Leadership in Improving Innovative Employee Work Behaviour: Organizational Citizenship Behaviours as a Model of Mediation

For a company to succeed in dynamic circumstances, it is vital to have leaders who are actively involved and focused on effectively addressing the issues. This emphasizes the need of proficient leadership in the present intricate and constantly evolving planet. Evaluating the influence of "organizational citizenship behavior" on innovative work behaviors in two varieties of OCBs (OCBi and OCBo) is the primary objective of this research. Data from a sample of 103 randomly selected officers were analyzed using the smartPLS bootstrap method to evaluate the hypothesis. In addition, the study utilized the Upsilon statistical method to quantify the magnitude of the influence of mediation. To examine the effect of mediation on fostering innovative employee work behaviors and facilitating transformational leadership styles, these developments have produced two distinct categories of OCBs. According to the study results, the correlation between OCBi and OCBo was fully mediated. This article presents and analyzes the significance of the results obtained from the hypothesis test.

OPDA/dn-OPDA actions: biosynthesis, metabolism, and signaling.

Plants cannot move, so they have evolved sophisticated strategies that integrate the external environmental cues and internal signaling networks for adaptation to dynamic circumstances. Cis-(+)-12-oxo-phytodienoic acid (OPDA) and 2,3-dinor-OPDA (dn-OPDA), the cyclopentenone-containing oxylipins, ubiquitously occur in the green lineage to orchestrate a series of growth and developmental processes as well as various stress and defense responses. OPDA/dn-OPDA are precursors of jasmonate (JA) biosynthesis in vascular plants. Dn-OPDA and its isomer also serve as bioactive JAs perceived by the coronatine insensitive1/jasmonate ZIM-domain (COI1/JAZ) co-receptor complex in bryophytes and lycophytes. In addition, OPDA/dn-OPDA display signaling activities independent of (+)-7-iso-jasmonoyl-L-isoleucine (JA-Ile) and COI1 in both vascular and non-vascular plants. In this review, we discuss recent advances in the biosynthesis, metabolism, and signaling of OPDA/dn-OPDA, and provide an overview of the evolution of OPDA/dn-OPDA actions to obtain a deeper understanding of the pervasive role of OPDA/dn-OPDA in the plant life cycle.

Business Process Management based on the project and knowledge perspective

New technological solutions require an organization to adapt constantly to changes in a dynamic environment. More and more enterprises implement Business Process Management, both at the strategic and operational levels, taking into account the project perspective based on knowledge resources. The aim of the article is to show the usefulness of building links between processes and projects based on knowledge resources in dynamic changeable technological circumstances based on research conducted in Poland. The approach guides Business Process Management activities by supporting organizations in identifying opportunities from business and technology trends and integrating them into business processes with novel value propositions.

Стратегічне управління ризиками на малих та середніх підприємствах в кризових умовах: концептуальна модель

This article seeks to explore the need to adapt enterprises risk management (ERM) to crisis environment, i.e., to the situation of martial law in Ukraine. To the best of the author’s knowledge, no ERM framework accounts for business operations in the situation of martial law (i.e. war environment). Organisational activities in hazardous, dynamic, and safety-oriented circumstances are researched in the area of high reliability theory. The paper presents the results of a preliminary literature review in these fields. As a result, the conceptual model "Enterprise Risk Management / High Reliability Theory" ("ERM/HRT") – which formulates the direction of the future research – was developed.

Multi-View Time-Series Hypergraph Neural Network for Action Recognition.

Recently, action recognition has attracted considerable attention in the field of computer vision. In dynamic circumstances and complicated backgrounds, there are some problems, such as object occlusion, insufficient light, and weak correlation of human body joints, resulting in skeleton-based human action recognition accuracy being very low. To address this issue, we propose a Multi-View Time-Series Hypergraph Neural Network (MV-TSHGNN) method. The framework is composed of two main parts: the construction of a multi-view time-series hypergraph structure and the learning process of multi-view time-series hypergraph convolutions. Specifically, given the multi-view video sequence frames, we first extract the joint features of actions from different views. Then, limb components and adjacent joints spatial hypergraphs based on the joints of different views at the same time are constructed respectively, temporal hypergraphs are constructed joints of the same view at continuous times, which are established high-order semantic relationships and cooperatively generate complementary action features. After that, we design a multi-view time-series hypergraph neural network to efficiently learn the features of spatial and temporal hypergraphs, and effectively improve the accuracy of skeleton-based action recognition. To evaluate the effectiveness and efficiency of MV-TSHGNN, we conduct experiments on NTU RGB+D, NTU RGB+D 120 and imitating traffic police gestures datasets. The experimental results indicate that our proposed method model achieves the new state-of-the-art performance.

Perceived Importance of Inclusive Education Preparedness in Xinxiang Vocational and Technical College in Henan Province (China) Before and During the COVID-19 Pandemic

This study explored Xinxiang Vocational and Technical College’s (XVTC) readiness for inclusive education across four domains before and during the pandemic. It aimed to identify nuanced variations in importance levels within these domains and assess any significant differences in perceived importance between the specified periods. Conducted through a postpositivist paradigm and an online survey on Weibo, the study involved students, teachers, non-teaching staff, and administrators from both XVTC campuses. Using the modified Inclusive Education Preparedness Questionnaire (IEPQ) with 100 items, half measured the “level of importance” through Likert scales. Descriptive and inferential analyses, including T-tests and Pearson r correlation tests, processed the data. Participants consistently rated inclusive education as extremely important across all domains during both periods. Increased importance during the pandemic was noted in Curriculum, Instruction, and Assessment, and Individual Student Support, with no significant change in Administration and Scheduling. The study rejected the null hypothesis for certain domains, indicating significant differences in perceived importance between pre-pandemic and pandemic periods but accepted the null hypothesis for correlations. Insights highlight the need for targeted interventions in specific domains to enhance inclusive education preparedness amid dynamic circumstances. Future studies should explore these variables among a marginalized student cluster in XVTC for proper comparison.

Značaj marketinga u sektoru zdravstva

In modern society, the application of the marketing concept becomes crucial, even in the nonprofit sector, including areas such as healthcare, education, and culture. Although marketing traditionally has not been prevalent in nonprofit organizations, changes in funding sources and increased competition encourage these organizations to reconsider their approach. Analyzing the roles of marketing in the healthcare sector, we emphasize the need for adaptable strategies due to dynamic circumstances and the necessity of embracing the marketing concept in the daily operations of healthcare institutions. Many healthcare institutions have not fully grasped the specificity of healthcare services compared to other service industries, nor the specificity and concept of patients compared to typical customers. Besides healthcare, staff should provide patients with so-called cognitive and emotional care. It is precisely from such specificities, such as patient participation in their treatment, that reasons for building loyalty or patient disengagement from a particular healthcare institution arise. By utilizing numerous studies in the field of relational marketing, this paper aims to highlight numerous shortcomings that healthcare institutions make in their relationships with current or prospective patients. A holistic marketing approach can ensure the influx of new patients, but only the creation and maintenance of quality relationships with them can provide healthcare institutions with patient loyalty, as well as the opportunity for long-term growth and development.

A Machine Learning Approach for Energy-Efficient Intelligent Transportation Scheduling Problem in a Real-World Dynamic Circumstances

A Machine Learning Approach for Energy-Efficient Intelligent Transportation Scheduling Problem in a Real-World Dynamic Circumstances

Nuclear perceptions from radioactive blue to sustainable green: The EU taxonomy as reflection of a divided public

Following the Fukushima disaster, several European countries revisited their policies and decided to phase out nuclear energy production. More recently, however, the EU taxonomy proposed a classification system of environmentally sustainable economic activities and consequently reopened the debate regarding nuclear energy. This study analyses public comments about the inclusion or exclusion of nuclear energy as a response to climate change, in the conceptual frameworks of ecological modernization, sustainability and technological neutrality. The study highlights the challenge of shaping energy policies amid dynamic circumstances and diverse viewpoints. Proponents of nuclear energy emphasised its low‐carbon emissions and climate change mitigation potential, while opponents focused on issues of radioactive waste and environmental risks. The study reveals that sustainability emerges as a potential reconciling framework to match arguments to influence the EU's decision‐making, while it reflects the political primacy of this process. Finally, this research recommends improving feedback visibility and enhancing clear communication, and suggests further research directions to understand future EU nuclear energy policies.

Mobile edge assisted multi-view light field video system: Prototype design and empirical evaluation

Metaverse is recently envisioned as the main driver for immersive multimedia in future networks. Light field video (LFV), considered an intermediate transition scheme towards the metaverse, is conducive to sensing and reconstructing realistic scenes in the digital twins. However, the research on LFV systems still lacks comprehensive investigation, impeding their practical application. This paper addresses major technical challenges involved in LFV delivery through mobile networks, showing a trade-off between equipment cost, calibration effort, and workflow simplicity. It also considers the balance of communications and computations latency when maintaining reconstruction performance. Leveraging edge and cloud infrastructure, a novel Mobile Edge Assisted Multi-view Light-field-video System (MEAMLS) is proposed, integrating LFV collection, volumetric video construction, and an immersive viewing scheme. A custom-made LFV capture array is designed to capture real-world scenes, while employing a learning method-integrated edge server to facilitate adaptive LFV coding. Moreover, a fast sparse reconstruction algorithm is established, leveraging edge–cloud collaboration to minimize computation latency during volumetric video construction. Intelligent service for users is deployed on edge to enable viewport-driven virtual reality viewing. By providing an immersive visual experience, the proposed MEAMLS bridges the physical world and its digital twins. The system is prototyped on a realistic 5G network to empirically validate the performance under static and dynamic circumstances. Experimental results yield fundamental insights for designing mobile edge networks-assisted LFV system from the perspective of transmission resource, adaptability, and deployment.

The Impact of Sustainable Balance Scorecard to Achieve Competitive Advantage in the Kurdistan Region of Iraq Economic Unit

The current research explores the important role of SBSC in initiating basic and important elements of competitive advantages in education sector economic units. Private universities and institutions in the current dynamic and complex environmental circumstances should consider SBSC as a unique tool and perceived to be good in the hands of managers to improve the abilities of their competitive advantage. A sample of (137) respondents has been analyzed by using statistical methods (SPSS 24), and principally measures of SBSC as independent variables represent the following perspectives: financial, customer, learning and growth, internal business, environmental and social as employed construct measures enhance effectiveness measures of competitive advantage components which represent in this study (cost, quality, time and innovation). Although, analysis of the study model test shows a statistically significant association of proposed SBSC perspectives on determinants of competitive advantage; hence result recommends reliance on SBSC orientation on private university and institution performance and competitive advantage.

Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation

Digital twin (DT) and artificial intelligence (AI) technologies are powerful enablers for Industry 4.0 toward sustainable resilient manufacturing. Digital twins of machine tools and machining processes combine advanced digital techniques and production domain knowledge, facilitate the enhancement of agility, traceability, and resilience of production systems, and help machine tool builders achieve a paradigm shift from one-time products provision to on-going service delivery. However, the adaptability and accuracy of digital twins at the shopfloor level are restricted by heterogeneous data sources, modeling precision as well as uncertainties from dynamical industrial environments. This article proposes a novel modeling framework to address these inadequacies by in-depth integrating AI techniques and machine tool expertise using aggregated data along the product development process. A data processing procedure is constructed to contextualize metadata sources from the design, planning, manufacturing, and quality stages and link them into a digital thread. On this consistent data basis, a modeling pipeline is presented to incorporate production and machine tool prior knowledge into AI development pipeline, while considering the multi-fidelity nature of data sources in dynamic industrial circumstances. In terms of implementation, we first introduce our existing work for building digital twins of machine tool and manufacturing process. Within this infrastructure, we developed a hybrid learning-based digital twin for manufacturing process following proposed modeling framework and tested it in an external industrial project exemplarily for real-time workpiece quality monitoring. The result indicates that the proposed hybrid learning-based digital twin enables learning uncertainties of the interaction of machine tools and machining processes in real industrial environments, thus allows estimating and enhancing the modeling reliability, depending on the data quality and accessibility. Prospectively, it also contributes to the reparametrization of model parameters and to the adaptive process control.

Design and analysis of high-gain DC–DC chopper topologies for PV-fed BLDC motor drive system

PurposeSwitching power converters for photovoltaic (PV) applications with high gain are rapidly expanding. To obtain better voltage gain, low switch stress, low ripple and cost-effective converters, researchers are developing several topologies.Design/methodology/approachIt was decided to use the particle swarm optimization approach for this system in order to compute the precise PI controller gain parameters under steady state and dynamic changing circumstances. A high-gain q- ZS boost converter is used as an intermittent converter between a PV and brushless direct current (BLDC) motor to attain maximum power point tracking, which also reduces the torque ripples. A MATLAB/Simulink environment has been used to build and test the positive output quadratic boost high gain converters (PQBHGC)-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. The simulation results show that the PQBHGC-3 topology is effective in comparison with other HG cell DC–DC converters in terms of efficiency, reduced ripples, etc. which is most suitable for PV-driven BLDC applications.FindingsThe simulation results have showed that the PQBHGC-3 gives better performance with minimum voltage ripple of 2V and current ripple of 0.4A which eventually reduces the ripples in the torque in a BLDC motor. Also, the efficiency for the suggested PQBHGC-3 for PV-based BLDC applications is the best with 99%.Originality/valueThis study is the first of its kind comparing the different topologies of PQBHGC-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. This study suggests that the PQBHGC-3 topology is most suitable in PV-driven BLDC applications.

An Evolutionary Game of Perception-Based Stochastic User-Equilibrium in a Parallel Network

In recent years, advanced information communication technology (such as beyond 5G and 6G) will enable convenient end-to-end communication. This would allow collections of individual preferences as predictions of travellers’ perceptions, and thus support data analysis and traffic control in a dynamic circumstance. Therefore, analysis of nonlinearly modelled perceptions and perception-based user equilibrium are the necessary problem we encounter. This research explores the existence characteristics of perception-based stochastic user equilibrium (P-SUE), a type of stochastic user equilibrium where travelers make route choices according to their perceptive traffic circumstances instead of actual ones. A nonlinear perception model with stochasticity is developed and the traffic arrivals are approximately transformed from Poisson-distributed to Normal-distributed. From a static perspective, we develop a P-SUE model with a heterogeneous traveler community and discuss the interaction among travelers’ attitude, grouping and static perception-based stochastic user equilibrium (P-SSUE). After embedding a day-to-day learning process into the P-SSUE model, we further explore evolutionary dynamics of the equilibrium. Several scenarios are provided to investigate effects and sensitivity of traveler-related and road-related factors. It is found that learning produces perturbation and makes the existence condition of P-SSUE more complicated; the convergence processes of P-SSUEs are significantly shortened as the learning rate increases to higher than 0.1.

Application of Time-Frequency Analysis with Selection of Suitable Mother Wavelets for Transient States of HVDC Transmission Lines

Recent developments in power semiconductor devices have made High Voltage Direct Current (HVDC) transmission systems the most crucial technology for future power systems. Today several HVDC projects have been commissioned around the world. The primary concern for such an advanced HVDC grid is to follow standard grid codes to maintain the safety and stability of the electrical equipment during transient and dynamic circumstances. Although the HVDC system has been recognized as the most prominent technology to transmit electric power, some issues must be addressed, such as selective and quick detection of faults. For selective fault detection, it is essential to look into the several fault detection strategies that fortify the HVDC system with time-frequency signal processing techniques. This research uses a time frequency-based method to identify the different HVDC transmission line faults. The most suitable mother wavelet for various transient states is selected by analyzing fault voltage and current signals based on variance, standard deviation, and range parameters. An experimental setup of a thyristor-based HVDC transmission system has been developed in the MATLAB/Simulink environment, and multiple fault scenarios have been analyzed. The simulation outcomes reveal that the most effective mother wavelet for detecting DC line, symmetrical and unsymmetrical faults occurs at the 6th level of fault signal decomposition. By examining current and voltage signals, our work has determined the best mother wavelet for various faults. Using more than one mother wavelet to detect faults is more reliable than using a single mother wavelet to detect all faults. Additionally, it has been noted that each fault's voltage and current signals have two distinct mother wavelets.

Role satisfaction among community volunteers working in mass COVID-19 vaccination clinics, Waterloo Region, Canada

IntroductionUnpaid community volunteers are a vital public health resource in times of crisis. In response to the COVID-19 pandemic, community volunteers were mobilized to support mass vaccination efforts in many countries. To have this group’s continued engagement, it is essential to understand the community volunteer experience, including the opportunities and challenges they encounter and how these contribute to their role satisfaction. This qualitative study investigated the factors contributing to community volunteers’ role satisfaction at COVID-19 mass vaccination clinics in the Region of Waterloo, Canada.MethodsQualitative data were analyzed from 20 volunteers (aged 48–79 years) who had worked at one of four COVID-19 vaccination clinics in the Region of Waterloo, Canada. Data were analyzed thematically using an inductive coding process followed by an iterative process of grouping and identifying linkages and relationships within the themes.ResultsFour interrelated themes were developed from the inductive analysis process. The theme of community volunteers feeling valued or disesteemed in their role depends on the interaction between the three themes of role description, role preparation, and clinic context.ConclusionsFor volunteers in crises such as the COVID-19 pandemic, volunteer role satisfaction depends on how their contributions are valued, the clarity of their role descriptions, volunteer-specific training, and the sentiments of volunteers and staff within the clinic context. Greater role satisfaction can help with retention as volunteers become more resilient and adaptable to the complex dynamic circumstances of a crisis response. Activities such as training and materials development for role preparations should be explicitly planned and well-resourced, even in crisis/pandemic situations. Building clinic managers’ or supervisors’ skills in communication during crisis/pandemic situations and the skills for the creation of team cohesion are critical investment areas.