Dynamic Management Research Articles

Graph Theoretical Models for Enhancing Highway Connectivity and Safety in Vehicular Networks

Vehicular networks play a crucial role in modern transportation systems, significantly impacting connectivity and safety on highways. This paper explores the application of graph theoretical models to enhance both connectivity and safety in vehicular networks. Graph theory, a branch of discrete mathematics, provides a robust framework for modeling and analyzing complex networks, including those formed by vehicles on highways. Our study begins by defining the vehicular network as a graph where nodes represent vehicles, and edges denote communication links between them. We employ various graph theoretical concepts such as connectivity, centrality, and network flow to evaluate and improve the network's performance. Key metrics, including the degree of nodes, clustering coefficients, and shortest path lengths, are utilized to quantify network connectivity and identify critical nodes and edges that influence overall network efficiency. One of the primary objectives is to ensure uninterrupted connectivity in the presence of dynamic and often unpredictable vehicular movement. To this end, we analyze the network's resilience to node failures and propose strategies to enhance robustness using redundancy and alternative routing paths. By incorporating concepts like k-connectivity and network diameter, we develop models that maintain high levels of connectivity despite the removal or failure of multiple nodes or edges. Safety is addressed through the lens of network stability and reliability. We investigate the impact of vehicular density, speed, and communication range on the network's ability to sustain reliable communication channels. Techniques such as dynamic topology management and adaptive power control are proposed to mitigate the risks associated with network fragmentation and communication delays. Furthermore, we introduce optimization algorithms that leverage graph partitioning and community detection to improve the management of vehicular clusters, facilitating efficient data dissemination and reducing the likelihood of congestion-related incidents. The proposed models are validated through simulations that mimic real-world highway conditions, demonstrating significant improvements in both connectivity and safety metrics. In conclusion, the application of graph theoretical models offers a promising approach to enhancing highway connectivity and safety in vehicular networks

HyperGCN – a multi-layer multi-exit graph neural network to enhance hyperspectral image classification

ABSTRACT Graph neural networks (GNNs) have recently garnered significant attention due to their exceptional performance across various applications, including hyperspectral (HS) image classification. However, most existing GNN-based models for HS image classification are limited depth models and often suffer from performance degradation as model depth increases. This study introduces HyperGCN, an exclusive GNN-based model designed with multiple graph convolutional layers to exploit the rich spectral information inherent in HS images, thereby enhancing classification performance. To address performance degradation, HyperGCN incorporates techniques resistant to oversmoothing into its architecture. Additionally, multiple-side exit branches are integrated into the intermediate layers of HyperGCN, enabling dynamic management of the complexity of HS images. Less complex HS images are processed by fewer layers, exiting early via attached branches, while more complex images traverse multiple layers until reaching the final output layer. Extensive experiments on four benchmark HS datasets (Indian Pines, Pavia University, Salinas, and Botswana) demonstrate HyperGCN’s superior performance over basic GNN-based models. Notably, HyperGCN outperforms or performs comparably to the CNN-GNN combined model in classifying HS images. Furthermore, the superior performance of multi-exit HyperGCN over its single-exit counterpart emphasizes the effectiveness of incorporating side exit branches in GNN-based HS image classification. Compared to state-of-the-art models, multi-exit HyperGCN demonstrates competitive performance, highlighting its effectiveness in handling complex spectral information in HS images while maintaining an acceptable balance between accuracy and computational efficiency.

Genome resequencing reveals population divergence and local adaptation of blacklegged ticks in the United States.

Tick vectors and tick-borne disease are increasingly impacting human populations globally. An important challenge is to understand tick movement patterns, as this information can be used to improve management and predictive modelling of tick population dynamics. Evolutionary analysis of genetic divergence, gene flow and local adaptation provides insight on movement patterns at large spatiotemporal scales. We develop low coverage, whole genome resequencing data for 92 blacklegged ticks, Ixodes scapularis, representing range-wide variation across the United States. Through analysis of population genomic data, we find that tick populations are structured geographically, with gradual isolation by distance separating three population clusters in the northern United States, southeastern United States and a unique cluster represented by a sample from Tennessee. Populations in the northern United States underwent population contractions during the last glacial period and diverged from southern populations at least 50 thousand years ago. Genome scans of selection provide strong evidence of local adaptation at genes responding to host defences, blood-feeding and environmental variation. In addition, we explore the potential of low coverage genome sequencing of whole-tick samples for documenting the diversity of microbial pathogens and recover important tick-borne pathogens such as Borrelia burgdorferi. The combination of isolation by distance and local adaptation in blacklegged ticks demonstrates that gene flow, including recent expansion, is limited to geographical scales of a few hundred kilometres.

A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total.

Autonomous Thermal Modulator Based on Gold Film‐Coated Liquid Crystal Elastsomer

AbstractRadiative cooling has been recently intensively explored for thermal management and enhancing energy efficiency. Yet, traditional materials with singular emissivity fall short in dynamic thermal management, highlighting the need for materials that can adjust their thermal radiation in real time. Active modulation methods, requiring external stimuli such as mechanical stretch, electric potential, or humidity change, offer adaptability but can increase energy use and complexity. Passive approaches, using materials' inherent thermal‐responsive properties, face manufacturing and scalability challenges. Here, a scalable yet effective passive approach is introduced for adaptive thermal modulation based on gold (Au) and liquid crystal elastomer (LCE) with a reversible response to environmental temperature changes. This modulator enables a “low thermal resistance” state through actuation‐induced microcracks that expose a high‐emissivity polymer substrate, and a “high thermal resistance” state by closing these microcracks and forming a high thermal resistance air gap between the modulator and the target object. The flexible design and fixed external dimensions of the Au‐LCE thermal modulator make it adaptable to various surface geometries. Furthermore, by adjusting the LCE's chemical composition, the modulator's transition temperature can be tailored, broadening its applications from enhancing building energy efficiency to improving clothing thermal comfort.

Two pyridoxal phosphate homeostasis proteins are essential for management of the coenzyme pyridoxal 5'-phosphate in Arabidopsis.

Coenzyme management is important for homeostasis of the pool of active metabolic enzymes. The coenzyme pyridoxal 5'-phosphate (PLP) is involved in diverse enzyme reactions including amino acid and hormone metabolism. Regulatory proteins that contribute to PLP homeostasis remain to be explored in plants. Here, we demonstrate the importance of proteins annotated as PLP homeostasis proteins (PLPHPs) for controlling PLP in Arabidopsis (Arabidopsis thaliana). A systematic analysis indicates that while most organisms across kingdoms have a single PLPHP homolog, Angiosperms have two. PLPHPs from Arabidopsis bind PLP and exist as monomers, in contrast to reported PLP-dependent enzymes, which exist as multimers. Disrupting the function of both PLPHP homologs perturbs vitamin B6 (pyridoxine) content, inducing a PLP deficit accompanied by light hypersensitive root growth, unlike PLP biosynthesis mutants. Micrografting studies show that the PLP deficit can be relieved distally between shoots and roots. Chemical treatments probing PLP-dependent reactions, notably those for auxin and ethylene, provide evidence that PLPHPs function in the dynamic management of PLP. Assays in vitro show that Arabidopsis PLPHP can coordinate PLP transfer and withdrawal from other enzymes. This study thus expands our knowledge of vitamin B6 biology and highlights the importance of PLP coenzyme homeostasis in plants.

A Fully Auditable Data Propagation Scheme With Dynamic Vehicle Management for EC-ITS

A Fully Auditable Data Propagation Scheme With Dynamic Vehicle Management for EC-ITS

Dynamic map management for Gaussian Splatting SLAM

Dynamic map management for Gaussian Splatting SLAM

Systems dynamics research in management and organization studies: Overview and research agenda

This paper presents a bibliometric analysis of the systems dynamics (SD) research landscape, drawing on 2,091 documents from Scopus and Web of Science. This research employs bibliometric techniques to explore the evolution of the scientific community over the past 50 years and assess research productivity and impact. Through network analysis, the study further reveals the field's social and conceptual structures. This approach revealed four pivotal thematic clusters, which were discussed based on content analysis: (1) operations research and strategy formulation, (2) behavioral studies and collaborative approaches, (3) dynamic performance management, and (4) systems thinking for sustainable development. The findings reveal a diverse and interdisciplinary trajectory of SD research, reflecting its integration into a broad array of fields and its potential to inform both theoretical and practical applications. The paper concludes by providing targeted recommendations for future SD research, with a particular emphasis on enhancing management and organizational studies through the incorporation of SD methodologies. This includes the potential for SD to influence the design of adaptive strategies, the use of SD in participatory policymaking, and the application of SD tools in promoting organizational learning and sustainability.

Supply Chain Optimization: Machine Learning Applications in Inventory Management for E-Commerce

This study delves into the potential impact of machine learning (ML) on supply chain optimization and inventory management for e-commerce. Our primary focus is analyzing the accuracy of demand forecasting, optimizing inventory levels, and evaluating the impact of real-time decision-making on supply chain efficiency. Using a secondary data-based review methodology, this study explores the implementation of advanced predictive analytics, real-time data processing, autonomous operations, and personalized customer experiences in prominent e-commerce companies like Amazon, Walmart, and Alibaba. Our findings show impressive advancements in demand forecasting accuracy, dynamic inventory management, and operational responsiveness. These improvements have led to cost savings and increased customer satisfaction. Nevertheless, some drawbacks exist, such as the significant expenses associated with implementation, concerns about data privacy, and the potential for overfitting the model. Policy implications call for solid data protection regulations, financial assistance for smaller businesses, and ethical guidelines for AI to promote fair and responsible machine learning applications. By tackling these obstacles, companies can maximize the potential of ML technologies to enhance efficiency, promote sustainability, and gain a competitive edge in the ever-changing world of e-commerce.

Principles of Cloud Computing Infrastructure IaaS

Infrastructure as a Service (IaaS) represents a cornerstone in contemporary cloud computing, providing essential on-demand computing resources, including servers, storage, and networking. This paper explores the foundational principles of IaaS, highlighting its ability to facilitate dynamic server management through practices like auto-scaling and ephemeral server utilization. The use of custom images in IaaS ensures minimal downtime and scalable deployment, while loose coupling enhances fault tolerance and adaptability. Emphasizing high availability through auto-scaling, IaaS supports continuous operations even in geographically vulnerable areas. The security framework of IaaS, encompassing a shared responsibility model, ensures robust data protection and compliance with industry standards. The paper also addresses cost optimization strategies and the integration of hybrid cloud architectures to maximize resource efficiency and maintain data integrity. Overall, IaaS is integral to modern IT infrastructure, offering flexible, scalable, and cost-effective solutions that align with the evolving demands of the digital landscape.

A novel dynamic batching energy management strategy for multi-microgrid networks with enhanced privacy

This article presents a novel energy management approach termed Dynamic Batching EMS (DBEMS) designed specifically for day-ahead scheduling within multi-microgrid (MMG) networks. The prime aim of this strategy is to curtail operational costs within the MMG network, fortify the privacy of individual microgrids, and optimize the utilization of available resources, surpassing existing methodologies like centralized, decentralized, and nested management strategies. The emphasis is chiefly on orchestrating cost-effective operations while bolstering data privacy for each microgrid within the complex MMG framework. The efficacy of the proposed strategy is evaluated across both grid-connected and islanded modes, focusing on a network comprising four microgrids. The model formulation is executed utilizing the Mixed Integer Linear Programming method. In the islanded mode, optimization is conducted for scenarios both inclusive and exclusive of Battery Energy Storage Systems (BESS). Comparative assessments are made against centralized and nested strategies, considering parameters including operational costs, privacy enhancements, load-shedding, computation time, and computational burden.

A Survey of Computationally Efficient Graph Neural Networks for Reconfigurable Systems

Graph neural networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demand substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on deep neural networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.

Consensus-oriented linguistic multi-criteria group sorting method incorporating dynamic trust management

Multi-criteria group sorting (MCGS) concerns the multi-person process of evaluating alternatives and assigning them to predetermined ordered categories under certain criteria. Identifying class thresholds and coordinating conflicting opinions are two primary challenges within this process. Taking into account the 2-tuple linguistic preferences of decision-makers (DMs), we propose a consensus-oriented sorting method that incorporates dynamic trust management for linguistic MCGS. The main contributions and innovations are as follows. (a) A group consensus maximization model is constructed to optimize class thresholds and initial individual sorting results. (b) Integrating familiarity-based trust and similarity-based trust, the dynamic comprehensive trust among DMs is established. On this basis, a feedback mechanism is designed, in which the bi-objective programming is utilized to generate adjustment recommendations. (c) The final consensual group sorting result is obtained by maximizing the similarity of group views. (d) The feasibility of the proposed method is demonstrated using a case of sorting fresh agricultural products. Using sensitivity analysis and comparative analysis, the proposed method is verified to be rational and effective. A comparison based on real datasets and a qualitative comparison further demonstrate the validity and characteristics of the proposed method.

Effectiveness in Implementing Office Administration Interconnection Policy through the Srikandi Application at the Tanjungpinang City Council Secretariat

This research is entitled "Effectiveness in Implementing Office Administration Interconnection Policy Through the SRIKANDI Application at the Tanjungpinang City Council Secretariat." The aim of this research is expected to be able to provide comprehensive information in implementing the office administration interconnection program through the SRIKANDI Application at the Tanjungpinang City Council Secretariat. Meanwhile, the benefits of the research are expected to provide information to the readers, among other things, theoretically, this research is expected to provide better knowledge and understanding in the field of Public Administration, especially in dynamic archive management in government and non-government institutions or agencies. The practical benefit is to increase knowledge in implementing effective and correct applications in accordance with existing regulations. In this research, researchers have used data collection techniques in the form of interviews, observation and documentation (Moleong, 2011). Meanwhile, in this research, researchers have also used the Grindle model of public policy implementation approach theory (in Winarno, 2002) which details Digital Policy Implementation in two criteria. First, Digital Policy Content which includes the interests of the target group, types of benefits, degree of change, location of decision making, program implementation and resources involved. Second, the Implementation Environment which includes power, interests, characteristics of institutions and authorities as well as compliance and responsiveness. The research results provide an illustration that the implementation of the SRIKANDI application still has a number of obstacles, especially when accessing the application, such as limited internet connection access and other resource limitations. Apart from a number of problems above, the potential for implementing E-Government through the SRIKANDI application is considered to be able to overcome the challenges of effective, efficient and needs- oriented government administration, the implementation of online digital applications and web browsers can be automated and can reduce the need for time-consuming manual work and resources.

Improving the maritime supply chain resilience: the role of firms’ dynamic knowledge management and organizational innovation

PurposeThis study aims to explore the influence of dynamic knowledge management capabilities on organizational innovation and supply chain resilience in maritime shipping companies. Furthermore, this study investigates the moderating role of growth-oriented strategies and the mediating role of organizational innovation.Design/methodology/approachData were collected from 76 maritime shipping companies in South Korea. The research hypotheses were tested using structural equation modeling.FindingsThis study demonstrates that effective dynamic knowledge management capabilities in maritime shipping companies significantly enhance organizational innovation and boost the resilience of their supply chains. Organizational innovation positively mediates the relationship between dynamic knowledge management and supply chain resilience. Moreover, a company’s growth-oriented strategy positively moderates the relationship between dynamic knowledge management and organizational innovation.Originality/valueBased on the existing literature, this study develops the concept of dynamic knowledge management and validates its impact on organizational innovation and the resilience of maritime supply chains. Furthermore, unlike previous studies, this study focuses specifically on maritime supply chains. Through a survey of 76 maritime companies in South Korea, the study validates relevant hypotheses and draws conclusions. This contributes to expanding and enriching existing research while offering meaningful insights for relevant enterprises.

Optimal Portfolios for Large Investors in Housing Markets Under Stress Scenarios: A Worst-Case Approach

AbstractThe study focuses on constructing a mathematical housing market threatened by a major catastrophic event or crash. It incorporates the worst-case scenario portfolio optimization problem as introduced in Korn and Wilmott (Int J Theor Appl Finance 5(02):171–187, 2002) into housing markets. The standard stochastic models for housing markets assume a geometric Brownian motion and neglect sudden housing price falls during crash times. However, the size, timing, and frequency of crashes have to be included in such models. By incorporating the worst-case portfolio optimization problem into housing markets, this study introduces a methodology to construct portfolios for large investors that are robust and resilient to extreme housing market conditions. The worst-case portfolio optimization approach can be used in housing markets to incorporate stress scenarios, minimize potential losses, utilize mathematical techniques, and manage housing investment risk effectively. This study provides valuable insights for large investors seeking to construct housing portfolios prioritizing downside protection and minimizing losses in extreme housing market conditions. Utilizing numerical illustrations, it provides insights into portfolio construction, demonstrating the effectiveness of adjusting portfolios to mitigate downside risks during housing market crises. The results highlight dynamic portfolio management’s significance in safeguarding wealth when housing prices undergo significant fluctuations.

Correlation Between Radiological Grading and Clinical Scoring in Knee Osteoarthritis Patients.

Osteoarthritis is characterized by mechanical joint abnormalities, primarily involving articular cartilage and subchondral bone degradation. Diagnosis of knee osteoarthritis relies on American College of Rheumatology criteria, and severity assessment utilizes the Western Ontario and McMaster Universities Osteoarthritis Index score and Kellgren and Lawrence grading. Despite reported associations, discrepancies persist in the correlation between Western Ontario and McMaster Universities Osteoarthritis Index and Kellgren and Lawrence grade. Descriptive cross-sectional study was conducted at the National Trauma Centre, over six months with consecutive convenience sampling. Demographic and clinico-radiological data were collected. Mean, SD and correlation coefficient was calculated. Consecutive convenient sampling yielded 80 participants meeting American College of Rheumatology criteria, aged 52-81 years, predominantly female (67.50%). Significant positive correlations were identified between age, Western Ontario and McMaster Universities Osteoarthritis Index score, and KL grade. Results showed most cases with Kellgren and Lawrence grade III and strong correlations (r=0.73, p=0.00) between total Western Ontario and McMaster Universities Osteoarthritis Index scores and Kellgren and Lawrence grading. Notably, pain, stiffness and physical functions individually exhibited a significant possitive correlation with Kellgren and Lawrence grading. The study affirms age-related influences on osteoarthritis, emphasizes female predilection, and underscores the importance of assessing both clinical and radiological parameters. In conclusion, this study supports Western Ontario and McMaster Universities Osteoarthritis Index efficacy in dynamic disease assessment and management, especially in settings where radiological examinations may be impractical, thus establishing Western Ontario and McMaster Universities Osteoarthritis Index as a versatile tool for systematic monitoring and intervention in knee osteoarthritis.

Extraction of offshore aquaculture areas from Gaofen-2 remote sensing imagery based on deep learning

ABSTRACT Extracting offshore aquaculture areas from high-resolution remote sensing images is significant for rational planning and dynamic management of aquaculture. However, the complex and changeable characteristics of aquaculture areas will lead to difficulty during high-resolution image processing. In this paper, an improved U-Net deep learning model is proposed to extract offshore aquaculture areas from high-resolution images. This model is improved by the pyramid pooling module (PPM) and up-sampling structure based on the original U-Net model. PPM and the up-sampling structure can better mine semantic and position information in high-resolution images, reduce the interference and adhesion of information in images, and improve the extraction accuracy of unclear aquaculture areas. Based on Gaofen-2 remote sensing image data of Rongcheng Bay in the eastern waters of Weihai City, Shandong Province, the extraction of offshore aquaculture areas is studied. The results show that the F1 score and MIoU of the proposed network model are 93.63% and 87.93%, respectively. Compared with several commonly used deep learning models, the improved U-Net model has a better extraction effect.

NFS-29. DEVELOPMENTAL SCREENING AND MONITORING IN NEUROFIBROMATOSIS-1

Abstract BACKGROUND Pilot data showed developmental screening at Neurofibromatosis-1 (NF1) appointments is feasible to identify those needing neuropsychology or other referrals. Outcomes for this larger sample, many re-screened and/or seen for a neuropsychology visit, allow us to describe evolving clinical management. METHODS Screenings are offered annually (ages 2-17) at NF1 clinic appointments by the psychosocial team. Forty-eight early childhood patients (ages 2-5) were screened (54% male, 66.7% White,) while 35 were ineligible (e.g., recent neuropsychology visit) or experienced logistical barriers to screening. These groups did not differ in race/ethnicity, sex, glioma status, or childhood opportunity index. Parents completed well-validated measures including the PEDS: Developmental Milestones – Assessment Level (PEDS:DM), Modified Checklist for Autism in Toddlers, and/or an ADHD Rating for preschoolers. RESULTS On initial screens, 73.9% showed a delay of 25% or more on at least one PEDS:DM domain. Measures often aligned with clinical impressions (81.3%); however, when findings were contextualized with record review, there were false positives (8.3%) and negatives (10.4%). Screening identified emerging concerns in attention, fine-motor, and academic skills. Nearly half (47.9%) of patients had services at the time of screening, and new/modified recommendations were offered for 70.8%. One third were referred to our Education Coordinator or Social Worker for educational advocacy, support through Early Intervention/school evaluation, or outpatient referrals. Twenty-two (45.8%) were referred to Neuropsychology and 18 have now been seen (consultation, targeted testing, or comprehensive evaluation) to confirm or rule-out neurodevelopmental diagnoses (e.g., autism, ADHD, language or global delay) and further guide educational or intervention management. Follow-up screens (N = 21) have monitored developmental progress, previously subclinical symptoms rising to a significant level, and need for re-evaluation or additional services. CONCLUSIONS Multipronged systematic screening results in referrals for diagnostic clarity, guides transition from Early Intervention to school services, and supports dynamic management of changing concerns.