Dynamic Setting Research Articles

The role of pharmacists in mitigating medication errors in the perioperative setting: a systematic review

IntroductionMedication errors occur at any point of the medication management process and are a major cause of death and harm globally. The perioperative environment introduces challenges in identifying medication errors due to the frequent use of time-sensitive, high-alert medications in a dynamic and intricate setting. Pharmacists could potentially reduce the occurrence of these errors because of their training and expertise.AimTo provide the most up-to-date evidence on the roles and effects of pharmacist interventions on medication errors in perioperative settings.MethodsPubMed, CINAHL, and Embase were searched from inception to September 2023. Studies were included if they tested a pharmacist-led intervention aimed at reducing medication errors in adult perioperative settings. The included studies were assessed for quality using the Crowe Critical Appraisal Tool. Data were extracted and synthesized using the DEPICT-2 (Descriptive Elements of Pharmacist Intervention Characterization Tool). Screening, quality assessment, and data extraction were performed by two independent researchers.ResultsSixteen studies were eligible. All included studies incorporated multicomponent interventions, primarily medication reconciliation (n = 13), medicine-related recommendations (n = 12), staff education (n = 6), and patient counselling (n = 4). The development of implemented interventions was poorly reported across all papers. A diverse range of error reporting was observed, and none of the included studies provided definitions or basis for the categorization of errors. Although the studies showed that pharmacist interventions were associated with a reduction in overall medication errors rates, some studies showed inconsistent findings regarding error subtypes. The most common pharmacist intervention was medication optimization via holding or switching between agents.ConclusionWhile there is some evidence of positive impact of the pharmacist-led interventions on medication errors in perioperative setting, this evidence is generally of low quality and insufficient volume. Heterogeneity in study design, definitions, and case detection is common; hence, high-quality research that applies more stringent controls and uses clearer definitions is warranted.Systematic review registrationPROSPERO CRD42023460812.

KSP-SCOS: new Karp-Steele patching based streamlined customer order scheduling approach

Customer order scheduling (COS) encapsulates one of the most intricate problems in operational optimization, primarily due to the multifaceted interplay of constraints such as temporal dependencies, resource limitations, and cost minimization. This paper introduces the Karp-Steele Patching for Streamlined COS (KSP-SCOS), a cutting-edge framework designed to address these complexities with unparalleled precision and efficiency. By incorporating the dynamic problem decomposition capabilities of Karp-Steele Patching, KSP-SCOS enhances the scheduling landscape through systematic exploration of feasible solutions while eliminating suboptimal paths through strategic pruning. The proposed approach was subjected to rigorous testing across diverse datasets representing a broad spectrum of real-world operational scenarios. These scenarios range from minimally constrained environments to those characterized by extensive resource contention and tight scheduling windows. Experimental findings reveal that KSP-SCOS consistently delivers near-optimal solutions, demonstrating robust scalability and significant reductions in computational overhead, regardless of the problem complexity. This research makes several noteworthy contributions to COS optimization : (1) The integration of the Karp-Steele Patching method within COS, ensuring a systematic yet adaptable solution process. (2) An empirical analysis across datasets of varying complexity, illustrating the algorithm’s effectiveness in balancing computational efficiency and scheduling quality. (3) An exploration of cost-time trade-offs, shedding light on the practical implications of for resource allocation in dynamic operational settings. The adaptability of KSP-SCOS establishes it as a groundbreaking for industries demanding dynamic, accurate, and resource-efficient scheduling strategies. Its capability to seamlessly adjust to evolving operational constraints underscores its critical relevance in scenarios requiring immediate and responsive decision-making. This study marks a pivotal advancement in resolving the pressing challenges of modern COS, paving the way for elevated operational efficacy and adaptability in increasingly complex environments.

Entanglement asymmetry and symmetry defects in boundary conformal field theory

A state in a quantum system with a given global symmetry, G, can be sensitive to the presence of boundaries, which may either preserve or break this symmetry. In this work, we investigate how conformal invariant boundary conditions influence the G-symmetry breaking through the lens of the entanglement asymmetry, a quantifier of the “distance” between a symmetry-broken state and its symmetrized counterpart. By leveraging 2D boundary conformal field theory (BCFT), we investigate the symmetry breaking for both finite and compact Lie groups. Beyond the leading order term, we also compute the subleading corrections in the subsystem size, highlighting their dependence on the symmetry group G and the BCFT operator content. We further explore the entanglement asymmetry following a global quantum quench, where a symmetry-broken state evolves under a symmetry-restoring Hamiltonian. In this dynamical setting, we compute the entanglement asymmetry by extending the method of images to a BCFT with non-local objects such as invertible symmetry defects.

Adaptive Radiative Thermal Management Using Transparent, Flexible Ag Nanowire Networks.

Effective heat management is critical for improving energy efficiency and minimizing environmental impact. Passive radiative heat management systems rely on specific materials and design configurations to naturally modulate temperature, enhance system reliability, and decrease operational costs by modulating infrared light. However, their static nature proves insufficient in dynamic settings experiencing significant temperature fluctuations. Adaptive radiative thermal management systems offer real-time heat exchange control, optimizing performance in varying conditions. However, such systems often interfere with the visible light response of the material, restricting application. Here, we present an adaptive thermal heat management system based on Ag nanowire (AgNW) networks on polydimethylsiloxane (PDMS). The AgNW network functions like a Faraday cage, with critical dimensions that do not interfere with visible light while effectively interacting with infrared light. Mechanical actuation enables over 40% modulation of thermal infrared light, leading to a perceived temperature difference of 6 °C when observed with a thermal camera relative to a 100 °C heat source.

Effect of dynamic pulsation and milk flow rate switch-point settings on milking duration and post-milking teat condition.

The objective of this study was to investigate the effect of dynamic pulsation settings that increased the open phase and reduced the closed phase of pulsation during the peak milk flow period together with increasing the milk flow rate switch-point for cluster detachment on milking duration and teat condition after milking. The present study filled current gaps in knowledge by informing on the effects of both milk flow rate switch-points and dynamic pulsation together in one experiment, while presenting data on milking performance, strip milk, teat condition and vacuum levels in the cluster during milking. To this end, 4 treatments consisting of different milk flow rate switch-points and pulsator settings combinations were deployed across 4 groups of 24 cows for 8 weeks. Treatments consisted of 2 levels of milk flow rate switch-point (0.2 kg/min and 0.8 kg/min) and 2 pulsator settings (static and dynamic). The static pulsator settings had a pulsator ratio of 65:35. For the dynamic settings a pulsator ratio of 63:37 below a milk flow rate of 2 kg/min was used, which changed to 73:27 above 2 kg/min. Milking parameters and teat scoring data were analyzed using mixed models. The effect of milk flow rate switch-point on milking duration was significant, whereas the effect of dynamic pulsation was not. The reductions in milking duration were larger for PM milking (for static pulsation settings, the average reduction was 21% between switch-point of 0.2 kg/min and 0.8 kg/min) compared with a 12% reduction for AM milking. In addition, we found a significant effect of treatment on teat-barrel congestion, with reduced odds of teat-barrel ringing for treatments with a milk flow rate switch-point of 0.8 kg/min relative to 0.2 kg/min due to significantly reduced over milking time and hence reduced exposure to higher mouthpiece chamber vacuum during the over milking period. On average across all treatments cows spent 32% of the milking above 2 kg/min, however this varied from 37% for AM milking to 23% on average for PM milking, hence an uneven milking interval impeded the ability of dynamic pulsation to contribute, especially for PM milking. We concluded that milk flow rate switch-point settings are more impactful than dynamic pulsation settings for reducing milking duration. Furthermore, we found no evidence of interactions between the effects of switch-point and pulsator setting on milking duration, milk yield or milk flow rate.

The direct thrust control of permanent magnet linear synchronous motor based on dynamic candidate vector set

The direct thrust control of permanent magnet linear synchronous motor based on dynamic candidate vector set

Convergent finite element methods for antiferromagnetic and ferrimagnetic materials

We consider the numerical approximation of a continuum model of antiferromagnetic and ferrimagnetic materials. The state of the material is described in terms of two unit-length vector fields, which can be interpreted as the magnetizations averaging the spins of two sublattices. For the static setting, which requires the solution of a constrained energy minimization problem, we introduce a discretization based on first-order finite elements and prove its Γ-convergence. Then, we propose and analyze two iterative algorithms for the computation of low-energy stationary points. The algorithms are obtained from (semi-)implicit time discretizations of gradient flows of the energy. Finally, we extend the algorithms to the dynamic setting, which consists of a nonlinear system of two Landau–Lifshitz–Gilbert equations solved by the two fields, and we prove unconditional stability and convergence of the finite element approximations toward a weak solution of the problem. Numerical experiments assess the performance of the algorithms and demonstrate their applicability for the simulation of physical processes involving antiferromagnetic and ferrimagnetic materials.

An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set

Accurately identifying bearing faults in aeroengines is crucial for maintaining their lifespan and cost. However, most current models are black-box models, such as deep learning models such as deep neural networks. The decision-making process of these models is more complex and lacks interpretability, which results in insufficient credibility of the results. Furthermore, data collected in real industrial environments can suffer from unbalanced sample categories. Moreover, the models can suffer from local ignorance in the prediction process. These problems can lead to a decrease in the prediction accuracy of the model. Therefore, a fault diagnosis method based on the interpretable belief rule base with a dynamic power set (D-HBRBP-I) is proposed in this study. First, a diagnostic model based on a belief rule base with a dynamic power set was used to address the problem of sample category imbalance and local ignorance. Second, optimizing the model via the P-CMAES algorithm with interpretability constraints can ensure the interpretability of the model after optimization. Finally, experiments were conducted on an aeroengine-bearing dataset. The results show that the proposed model effectively solves the above problem while achieving 99% accuracy.

Strategic Responses to Employee Well-Being Issues in VUCA (Volatile, Uncertain, Complex, and Ambiguous) Conditions: Expanding the JD-R (Job Demands–Resources) Model with Job Crafting

Current work environments, driven by globalization, demographic changes, and digitalization, demand substantial adaptation, which leads to decreased employee well-being. While occupational psychology research has identified supportive mechanisms, it often lacks a deepened understanding of how interventions function. This study aims to analyze the impacts of VUCA contexts and leadership behavior on job crafting, focusing on white-collar workers. Our objective is to identify workplace configurations that safeguard employee health in dynamic settings. Utilizing the JD-R model augmented by job crafting as a theoretical framework, we conducted qualitative comparative case studies using the GABEK® methodology to analyze and systematize data. This approach captures complex organizational interrelationships through sentence analysis, transforming subjective meanings into a comprehensive network and providing deeper insights into organizational dynamics. Research findings reveal that evolving work demands, notably from digital transformations, impact knowledge management, communication, and employee well-being. IT discrepancies and communication deficits intensify work complexity, underscoring the need for enhanced management frameworks. Proactive and adaptive leadership, along with job crafting, is crucial for protecting health and enhancing performance in fast-paced digital environments. These strategies promote structured decision-making and efficient knowledge management, fostering a resource-oriented culture that enhances employee well-being and overall effectiveness.

Identity, (Dis)Ability and Belonging: Liturgy and Worship through the Lens of Autism

Disabled people often feel excluded from church activities. Focusing on autism, this article explores the problematic, relative absence of autistic people in churches. It will be argued that the concept of normalcy lies at the heart of the problem. Normalcy is understood as the set of dynamics that guard the boundaries of communities, in other words, who does and does not belong. These boundaries are part of what Charles Taylor calls the “framework” of the community, which determines what the community values. This article will argue that those with disabilities easily fall outside of the community’s boundaries. After discussing the dynamics of normalcy, this article offers a theological critique and a proposal for a framework inspired by the Gospel. This enables a “theology of presence” which envisions members of the community being present to each other in a way that reflects the kenosis of Christ.

Identity, (Dis)Ability and Belonging: Liturgy and Worship Through the Lens of Autism

Disabled people often feel excluded from church activities. Focusing on autism, this article explores the problematic, relative absence of autistic people in churches. It will be argued that the concept of normalcy lies at the heart of the problem. Normalcy is understood as the set of dynamics that guard the boundaries of communities, in other words, who does and does not belong. These boundaries are part of what Charles Taylor calls the “framework” of the community, which determines what the community values. This article will argue that those with disabilities easily fall outside of the community’s boundaries. After discussing the dynamics of normalcy, this article offers a theological critique and a proposal for a framework inspired by the Gospel. This enables a “theology of presence” which envisions members of the community being present to each other in a way that reflects the kenosis of Christ.

Application of human-computer interaction technology integrating biomimetic vision system in animation design with a biomechanical perspective

The combination of Human-Computer Interaction (HCI) technology with biomimetic vision systems has transformational potential in animation design, particularly by incorporating biomechanical principles to create immersive and interactive experiences. Traditional animation approaches frequently lack sensitivity to real-time human motions, which can restrict engagement and realism. This study addresses this constraint by creating a framework that uses Virtual Reality (VR) and Augmented Reality (AR) to generate dynamic settings that include a variety of human activities, informed by biomechanical analysis. A biomimetic vision system is used to record these motions with wearable sensors, allowing for precise monitoring of user activity while considering biomechanical factors such as joint angles, force distribution, and movement patterns. The recorded data is preprocessed using Z-score normalization methods and extracted using Principal Component Analysis (PCA). This study proposed an Egyptian Vulture optimized Adjustable Long Short-Term Memory Network (EVO-ALSTM) technique for motion classification, specifically tailored to recognize biomechanical characteristics of human movements. Results demonstrate a significant improvement in precision (93%), F1-score (91%), accuracy (95%), and recall (90%) for the motion recognition system, highlighting the effectiveness of biomechanical insights in enhancing animation design. The findings indicate that integrating real-time biomechanical data into the animation process leads to more engaging and realistic user experiences. This study not only advances the subject of HCI but also provides the framework for future investigations into sophisticated animation technologies that use biomimetic and biomechanical systems.

Risk Budgeting Allocation for Dynamic Risk Measures

Time-Consistent Diversified Portfolio Allocations Portfolio allocation problems typically involve diversification of risks, which can be achieved by risk budgeting strategies. Of additional importance is being time consistent—that is, ensuring that decisions about what to do in the future under certain outcomes remain optimal when those future outcomes are realized. In “Risk Budgeting Allocation for Dynamic Risk Measures,” Pesenti, Jaimungal, Saporito, and Targino develop time-consistent portfolio strategies, termed dynamic risk budgeting strategies, where the diversification is on the level of risk contribution of assets. The authors formalize the dynamic setting of risk budgeting strategies, recast dynamic risk budgeting strategies as solutions of sequences of convex optimization problems, and develop an efficient actor-critic deep reinforcement learning algorithm for estimating dynamic risk budgeting strategies. The algorithm and the dynamic risk budgeting strategies are illustrated on a complex simulation case study involving five assets and 12 time steps.

Mapping the FGF2 Interactome Identifies a Functional Proteoglycan Coreceptor.

Fibroblast growth factor 2 (FGF2) is a multipotent growth factor and signaling protein that exhibits broad functions across multiple cell types. These functions are often initiated by binding to growth factor receptors and fine-tuned by glycosaminoglycan (GAG)-modified proteins called proteoglycans. The various outputs of FGF2 signaling and functions arise from a dynamic and cell type-specific set of binding partners. However, the interactome of FGF2 has yet to be comprehensively determined. Moreover, the identity of the proteoglycan proteins carrying GAG chains is often overlooked and remains unknown in most cell contexts. Here, we perform peroxidase-catalyzed live cell proximity labeling using an engineered APEX2-FGF2 fusion protein to map the interactome of FGF2. Across two cell lines with established and distinct FGF2-driven functions, we greatly expand upon the known FGF2 interactome, identifying >600 new putative FGF2 interactors. Notably, our results demonstrate a key role for the GAG binding capacity of FGF2 in modulating its interactome.

Structure, Agency, and Persian Gulf security: Continuity amid change

The Persian Gulf’s security architecture is in a period of transition, caused by the interplay of two sets of dynamics, one related to structural factors, and the other a product of agency. Structurally, competition among the region’s two middle powers, Iran and Saudi Arabia, along with those of a small state that views itself as a middle power and behaves as such, the UAE, is both transforming the Persian Gulf’s security architecture and also keeping regional tensions high. Moreover, elite political priorities within some of the most active actors in the region are changing in favor of tension reductions.

Simultaneous Clustering and Feature Selection using Social Group Optimization with Dynamic Threshold Setting for Microarray Data

Simultaneous Clustering and Feature Selection using Social Group Optimization with Dynamic Threshold Setting for Microarray Data

MOBILE ROBOT NAVIGATION BASED ON LASER RANGE-METRY METHODS

The article is devoted to solving the problem of finding people who are under the debris of destroyed buildings, which requires an urgent solution to ensure an effective rescue operation. The primary focus of this research is the development and refinement of a navigation system for a mobile rescue robot. This paper specifically explores localization techniques and methods for constructing a map of an unknown environment, which are crucial for the robot’s effective operation in complex and dynamic settings. The overarching goal of the study is to identify the most adaptable and precise navigation method that can effectively manage the movement of a mobile robot in environments characterized by unpredictable and shifting obstacles. To solve the tasks, modern methods of laser telemetry and SLAM methods (Simultaneous Localization and Mapping) are used in the work, which are used to accurately create a map of the territory and localize the robot on it. The research proposes an innovative algorithm for the navigation system of a mobile robot, which leverages laser telemetry to meticulously scan the surrounding area. It has been shown that scanning process is essential for real-time data acquisition, enabling the robot to make informed decisions regarding its movement. It is stated that, the proposed lidar-based navigation method is particularly advantageous as it facilitates the creation of a detailed spatial map, even in environments where the layout is not known in advance. This approach allows for the precise determination of the robot’s position within the mapped space, ensuring that the robot can navigate effectively and avoid potential hazards. It is proved that, the integration of these advanced techniques significantly enhances the robot’s ability to perform in search and rescue missions, where the accurate and timely localization of trapped individuals is of paramount importance.

AI-Based Identity Verification Through Behavioral Biometrics: Enhancing Remote Work Security

With the increased adaptability of remote work patterns, secure and efficient identity verification has also become a paramount concern. The traditional model of authentication involving the use of passwords or security tokens fails to provide any continuous verification of the user and is also prone to hacking attempts. Behavioral biometrics, especially keystroke dynamics and mouse movement patterns, offer an effective alternative as they allow for unobtrusive user authentication that is based on the individual user's behavior and is therefore continuous. This paper explores the application of Long Short-Term Memory (LSTM) networks, a sequence-based AI model, for studying and differentiating behavioral biometrics. We are using freely available data sets of keystroke and mouse dynamics to design and test an LSTM based system which is capable of making a distinction between users and imposters. We have shown that LSTM networks are significantly better in handling time series data such as state transition sequences than other statistical machinery traditional machine learning methods, such as Random Forests and Support Vector Machines, where they scored 89% accuracy between normal user operations and abuse activity. This aspect of the research addresses the reason why LSTM networks are appropriate for live remote identity verification systems, which is their ability to learn long strips of sequences and the behavioral flow. The originality of the study is to expand secure authentication solutions based on artificial intelligence systems that can be integrated to facilitate remote working in any sector for enhanced security.

For Palestine or for us too? Examining Indonesian youth’s willingness to buy local products amid the Israel boycott

Purpose The aim of this study is to explore the drivers behind the willingness of Indonesian youth to buy local products as an alternative to Israeli-affiliated products amid geopolitical tensions between Palestine and Israel, using the framework of identity-based motivation theory. Design/methodology/approach This study uses SmartPLS 4 to examine the proposed hypotheses using survey data collected from 307 Indonesian youth, within the context of an ongoing Israel boycott. This dynamic setting allows us to capture real-time and authentic responses of Indonesian youth to socio-political pressures and religious endorsements in their purchasing decisions. Findings The findings challenge the belief that religious altruism alone drives Indonesian youth to buy local products during the Israel boycott. Instead, those with strong religious and ethnocentric values are more likely to support local industries when they believe in the effectiveness of boycotting Israeli-affiliated products. Practical implications The findings offer valuable insights for domestic marketers, policymakers, educators and communities seeking to strengthen local industries in the face of geopolitical tensions. Originality/value This study contributes to the underexplored area of local product consumption by integrating religious, geopolitical and national identity factors through the lens of identity-based motivation theory, offering a novel perspective on the motivational drivers behind Indonesian youth’s willingness to buy local products during a boycott.

Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration.

The ever-increasing volume of complex data poses significant challenges to conventional sequential global processing methods, highlighting their inherent limitations. This computational burden has catalyzed interest in neuromorphic computing, particularly within artificial neural networks (ANNs). In pursuit of advancing neuromorphic hardware, researchers are focusing on developing computation strategies and constructing high-density crossbar arrays utilizing history-dependent, multistate nonvolatile memories tailored for multiply-accumulate (MAC) operations. However, the real-time collection and processing of massive, dynamic data sets require an innovative computational paradigm akin to that of the human brain. Spiking neural networks (SNNs), representing the third generation of ANNs, are emerging as a promising solution for real-time spatiotemporal information processing due to their event-based spatiotemporal capabilities. The ideal hardware supporting SNN operations comprises artificial neurons, artificial synapses, and their integrated arrays. Currently, the structural complexity of SNNs and spike-based methodologies requires hardware components with biomimetic behaviors that are distinct from those of conventional memristors used in deep neural networks. These distinctive characteristics required for neuron and synapses devices pose significant challenges. Developing effective building blocks for SNNs, therefore, necessitates leveraging the intrinsic properties of the materials constituting each unit and overcoming the integration barriers. This review focuses on the progress toward memristor-based spiking neural network neuromorphic hardware, emphasizing the role of individual components such as memristor-based neurons, synapses, and array integration along with relevant biological insights. We aim to provide valuable perspectives to researchers working on the next generation of brain-like computing systems based on these foundational elements.