Dynamic Approach Research Articles

Fair and Efficient Ridesharing: A Dynamic Programming-based Relocation Approach

Recommending routes by their probability of having a rider has long been the goal of conventional route recommendation systems. While this maximizes the platform-specific criteria of efficiency, it results in sub-optimal outcomes with the disparity among the income of drivers who work for similar time frames. Pioneer studies on fairness in ridesharing platforms have focused on algorithms that match drivers and riders. However, these studies do not consider the time schedules of different riders sharing a ride in the ridesharing mode. To overcome this shortcoming, we present the first route recommendation system for ridesharing networks that explicitly considers fairness as an evaluation criterion. In particular, we design a routing mechanism that reduces the inequality among drivers and provides them with routes that have a similar probability of finding riders over a period of time. However, while optimizing fairness the efficiency of the platform should not be affected as both of these goals are important for the long-term sustainability of the system. In order to jointly optimize fairness and efficiency we consider repositioning drivers with low income to the areas that have a higher probability of finding riders in future. While applying driver repositioning, we design a future-aware policy and allocate the areas to the drivers considering the destination of requests in the corresponding area. Extensive simulations on real-world datasets of Washington DC and New York demonstrate superior performance by our proposed system in comparison to the existing baselines.

Resilience compass navigation through manufacturing organization uncertainty – A dynamic capabilities approach using mixed methods

In uncertain manufacturing environments, tools that help companies and supply chains navigate unexpected events and promote sustainability are crucial. However, the application of resilience in manufacturing organizations is limited, often focusing on supply chain resilience. This study used mixed methods to minimize subjectivity in manufacturing resilience. A quantitative Content Validity Index (CVI) helped develop the 'resilience compass' assessment tool, while empirical applications in six manufacturing organizations demonstrated its usefulness. The study identifies relationships between 54 resilience practices, 11 dynamic capabilities, and three stages: anticipation, coping, and adaptation, providing decision-makers with actionable insights to enhance manufacturing resilience.

Bibliometric Insights into Mental Health Challenges and Innovations in Contemporary Society

This bibliometric analysis examines the evolution and current state of mental health research from a comprehensive, data-driven perspective. Using VOSviewer for visual mapping, this study highlights key terms, thematic developments, and influential authors within the field over several decades. The analysis reveals a strong ongoing focus on core issues such as "mental illness," "anxiety," and "depression," alongside a notable rise in the integration of technological innovations like telepsychiatry and mobile health applications. The study also identifies significant authorship networks that suggest robust collaborative efforts, which are crucial for advancing research and implementing effective mental health interventions. The findings point towards an increasing recognition of the impact of social and environmental factors on mental health, suggesting new directions for future research and policy-making. This study underscores the need for a dynamic approach to mental health research, one that adapts to emerging challenges and leverages technological advances to improve access to care and treatment outcomes.

Solving transport equations on quantum computers—potential and limitations of physics-informed quantum circuits

AbstractQuantum circuits with trainable parameters, paired with classical optimization routines can be used as machine learning models. The recently popularized physics-informed neural network (PINN) approach is a machine learning algorithm that solves differential equations by incorporating them into a loss function. Being a mesh-free method, it is a promising approach for computational fluid dynamics. The question arises whether the properties of quantum circuits can be leveraged for a quantum physics-informed machine learning model. In this study, we compare the classical PINN-ansatz and its quantum analog, which we name the physics-informed quantum circuit (PIQC). The PIQC simulations are performed on a noise-free quantum computing simulator. Studying various differential equations, we compare expressivity, accuracy and convergence properties. We find that one-dimensional problems, such as the linear transport of a Gaussian-pulse or Burgers’ equation, allow a successful approximation with the classical and the quantum ansatz. For these examples, the PIQC overall performs similarly to PINN and converges more consistently and for Burgers’ equations even faster. While this is promising, the chosen quantum circuit approach struggles to approximate discontinuous solutions which the classical PINN-ansatz can represent. Based on this comparison, we extrapolate that the required number of qubits for solving two-dimensional problems in aerodynamics may already be available in the next few years. However, the acceleration potential is currently unclear and challenges like noisy circuits and approximations of discontinuous solutions have to be overcome.

Dynamic Modeling of a HeXe-Cooled Mobile Nuclear Reactor with Closed Brayton Cycle

Helium-xenon (HeXe)-cooled mobile nuclear reactors have promising potential in future low-carbon energy systems. However, there is currently a lack of fast and reliable tools for analyzing the complicated dynamic characteristics of such systems. In this study, we developed a comprehensive dynamic modeling approach for a HeXe-cooled nuclear power system coupled with a closed Brayton cycle (CBC). The system’s key components, including the reactor, printed circuit heat exchanger (PCHE), and turbomachinery, are lumped-modeled to capture their time-varying behavior. A step-solving algorithm that incorporates HeXe mass conservation iteration is designed. The verification results demonstrate that the dynamic program is robust and reliable, with each time step converging within 25 iterations and the HeXe mass remaining within the range of 3.755 ± 0.01 kg throughout the simulation meeting the law of mass conservation. Then, a 1500 s frozen start-up simulation for the coupled system is conducted, in which the CBC is started in the first 500 s by increasing the main shaft speed to 40% of the rated value, and then the reactor is started by inserting external reactivity between 500 and 800 s. Both the dynamic process and the steady-state performance after the start-up are analyzed. The results show that the system achieved a stable electrical output of 5.7 MWe with a thermal efficiency of 32.5%. This study lays a solid foundation for future work aimed at improving the overall efficiency and performance of HeXe-cooled nuclear power systems.

Investigation on the Optimization Strategy of DWA Algorithm for Path Planning of the USVs with the Consideration of Environmental Factors

Unmanned surface vessels (USVs) are generally subjected to wind force, wave and undercurrent action. Some unidentified objects such as underwater obstacles and submerged reefs should be monitored and evaded with converged communication, autonomous control and network system so as to achieve autonomous and automatic avoidance of danger and real-time control of flight path. Next, based on performing interference mechanics analysis on the USVs under the disturbance of wind force and wave on water surface, the relationship parameters between the bow angle, the deviation angle, safety evaluation function and the external force were obtained, and the comprehensive information of water surface environment was evaluated in combination with the dynamic window approach (DWA) algorithm. Further, the related model was established for simulation by using the simulation software MATLAB, and both reliability and stability of the modified DWA algorithm were designed and examined. Accordingly, the modified DWA algorithm can enhance the safety of the USVs in autonomous path planning and achieve rapid obstacle avoidance and autonomous path planning of the USVs. Our simulation revealed that the modified DWA algorithm can safely and rapidly correct the bow angle and the deviation angle, optimize the path trajectory and reduce the risk of roll-over of the USVs under sudden external force.

Toward understanding lipid reorganization in RNA lipid nanoparticles in acidic environments

The use of lipid nanoparticles (LNPs) for therapeutic RNA delivery has gained significant interest, particularly highlighted by recent milestones such as the approval of Onpattro and two mRNA-based SARS-CoV-2 vaccines. However, despite substantial advancements in this field, our understanding of the structure and internal organization of RNA-LNPs -and their relationship to efficacy, both in vitro and in vivo- remains limited. In this study, we present a coarse-grained molecular dynamics (MD) approach that allows for the simulations of full-size LNPs. By analyzing MD-derived structural characteristics in conjunction with cellular experiments, we investigate the effect of critical parameters, such as pH and composition, on LNP structure and potency. Additionally, we examine the mobility and chemical environment within LNPs at a molecular level. Our findings highlight the significant impact that LNP composition and internal molecular mobility can have on key stages of LNP-based intracellular RNA delivery.

Innovative solar-assisted direct contact membrane distillation system: Dynamic modeling and performance analysis

The study presents an innovative solar-assisted dual-tank direct contact membrane distillation (DCMD) system designed to enhance the operational stability and efficiency of solar-powered desalination. The proposed system integrates a dual thermal storage tank configuration, allowing for continuous operation by alternating between two tanks that store pre-heated water, thereby mitigating the impact of solar energy fluctuations. The dynamic modeling approach used in this study predicts the system's performance under varying solar conditions, focusing on key parameters such as permeate flux, evaporation efficiency, and specific thermal energy consumption. The simulation results show that the system achieves an average permeate flux of 14.4 L/h m² and a thermal efficiency of 53.3 % at a hot water temperature of 60 °C, with a corresponding average specific thermal energy consumption of 1567 kWh/m³. These findings highlight a substantial improvement in both thermal efficiency and water production compared to conventional single-tank systems.The dual-tank DCMD system is particularly suited for deployment in remote or arid regions where stable and efficient freshwater production is critical. This research provides a comprehensive analysis of a novel solar-assisted desalination technology, contributing to the advancement of sustainable water resources management by providing a reliable and scalable solution that can maintain high operational efficiency even in remote areas with variable solar conditions.

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

Assessment of regional official development programs on solid waste management via system dynamics approach considering their GHGs emissions

Assessment of regional official development programs on solid waste management via system dynamics approach considering their GHGs emissions

On Assessing a Potential Reuse of the Indonesian Post-Operation Offshore Oil/Gas Pipelines as A Cold-Water Pipe for an Ocean Thermal Energy Conversion (OTEC) System: A Thermal and Fluid Dynamic Perspective

One of the main issues arising in offshore oil and gas decommissioning is associated with significant required costs. Research assessing the potential reuse of the post-operation offshore oil and gas pipeline (POGP) in Indonesia for the Ocean Thermal Energy Conversion (OTEC) system offer double the potential cost reductions in POGP decommissioning and OTEC development. In the context of OTEC development, research on increasing system efficiency makes a significant effort by modifying working fluids and usually by employing the assumption that the temperature of the cold seawater at the surface outlet of the Cold-Water Pipe (CWP) is practically the same as that at the inlet of the CWP at a depth of about 900 m. Unfortunately, this was not the case when the POGP was to be reused for the OTEC system because there was difficulty in applying additional insulation to the existing subsea pipelines. The temperature change in the cold seawater is even more critical considering that oil and gas operations at a depth of 900 m could be associated with more than 60 km of pipeline length to reach an onshore system for a typical seabed bathymetry. This paper assesses the potential reuse of the POGP as a CWP for the Ocean OTEC system. The temperature distribution within CWP is analyzed using a thermal and fluid dynamics approach. The results show that the reuse of POGP for the OTEC system could offer an excellent opportunity for capital cost reductions because the POGP has a remaining service life of over 20 years. However, the results of the analysis of heat and mass transfers show that the temperature change in cold seawater at CWP is about 3 to 6 oC, depending on the technical scenarios of the pipelines. The POGP used in the OTEC system could be suitable for a 20 kW OTEC system with practically no cost of pipelines, which usually accounts for a significant investment cost.

Beyond Illumination: Stakeholder Perspectives on Urban Lighting Master Planning for Chiang Mai Old City, Thailand

Chiang Mai Old City, a cultural heritage site and major tourist destination in Thailand, has significant cultural capital but lacks a well-designed urban lighting system, limiting its nighttime development potential. This issue arises from the absence of an urban lighting master plan, a crucial tool for guiding the city’s growth. The challenge lies in reconciling the diverse perspectives of stakeholders to create a comprehensive lighting master plan that meets shared goals. This research proposes a system dynamics approach to analyze stakeholder complexity. A qualitative, multi-stage method was employed, through in-depth interviews and focus groups with 60 stakeholders from three groups: government, professionals, and end users, to prioritize critical factors. Causal Loop Diagrams (CLDs) were used to illustrate the interrelations among those factors, leading to alternative scenarios for the lighting master plan’s development. The findings demonstrate that safety and security is the top priority, followed by cultural and economic factors. Eleven reinforcing loops and two balancing loops are proposed through CLD visualization. This framework highlights the importance of a participatory process, advocating for a systematic and holistic approach where all stakeholders with diverse perspective collaborate side-by-side in the development of the urban lighting master plan for Chiang Mai Old City.

Enhanced modeling of vehicle-induced turbulence and pollutant dispersion in urban street canyon: Large-eddy simulation via dynamic overset mesh approach

This study presented a novel application of large-eddy simulation (LES) with a dynamic overset mesh approach to simulate vehicle-induced turbulence in a two-dimensional street canyon. The simulation incorporated moving vehicle entities to emulate two-way traffic, with each vehicle equipped with a pollutant source to simulate pollutant dispersion. Comprehensive long-term statistical analyses were conducted to compare the simulated turbulence with those produced by the conventional approach (where vehicle-induced momentum was not considered) and the quasi-steady method (where vehicle motion was simplified as momentum sources). The results revealed that the presence of moving vehicle entities significantly distorted the primary circulation within the canyon, altering the transport pathways of both lateral momentum and air pollutants. The motion of vehicle entities also induced a substantial amount of turbulence, resulting in different pollutant removal mechanisms at the top of the canyon. The ensemble-average analysis revealed a downwash followed by an upwash within a cycle of vehicle movement, which largely contributed to momentum and pollutant transport. These findings underscored the need for considering the moving entities in LES approaches to enhance vehicle-induced turbulence modeling. Other factors influencing the simulation were discussed, aiming to guide more accurate and reliable turbulence modeling in urban environments.

Repurposed pharmacotherapy: targeting cathepsin L with repurposed drugs in virtual screening.

Proteolytic enzymes are closely associated with cancer and are important in different phases, including tumor growth, angiogenesis, and metastasis. Despite efforts to target matrix metalloproteases (MMPs), clinical trials have often resulted in various side effects such as musculoskeletal pain, joint stiffness, and tendinitis, making them less optimal for chronic cancer treatment. Thus, there is a need for the identification of other protease targets that would provide different approaches towards the management of cancer. Of these targets, Cathepsin L (CatL) is a lysosomal cysteine protease that has been identified as a therapeutic target that is implicated in cancer development and metastasis. In this study, we performed an integrated approach of virtual screening and molecular dynamics (MD) simulations to identify the potential inhibitors of CatL from a library of drugs that have been used for different treatments. Towards this goal, we performed virtual screening of the DrugBank database and found two repurposed drugs, Irinotecan and Nilotinib, against CatL based on their docking profiles, favorable docking scores, and specific interaction with the CatL binding pocket. MD simulations of the Irinotecan and Nilotinib bound structures with CatL were carried out, and the analysis showed that both these compounds could function as CatL inhibitors as the protein-ligand interactions were stable for 300 ns. This study highlights the robustness of these drugs bound to CatL and indicates that they could be repurposed for the treatment of cancer. These findings endorse the use of computer-based approaches for the identification of new inhibitors, and the present study will be a useful resource for future experimental research towards the targeting of CatL in cancer therapeutics.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Dynamic Path Planning Method for Unmanned Surface Vessels in Complex Traffic Conditions of Island Reefs Waters

Unmanned Surface Vehicles (USVs) operating in complex traffic conditions in island reef waters often require different types of algorithms. Therefore, selecting a dynamic path-planning algorithm with strong adaptability has become a new challenge. This paper proposes a dynamic adaptive path planning algorithm for USVs, incorporating an improved Dynamic Window Approach (DWA) with fuzzy logic and the International Regulations for Preventing Collisions at Sea (COLREGS). The algorithm is designed by integrating three key aspects: evaluation function, fuzzy control, and COLREGS. First, to enable USVs to approach the target point more safely and quickly during navigation, an additional target point attraction sub-function is introduced, extending the original evaluation function. Furthermore, to ensure robust dynamic path planning for USVs across various water environments, such as narrow channels, reef-laden waters, and open seas, fuzzy logic is integrated with the improved DWA algorithm. Since USVs must comply with COLREGS during navigation, the algorithm incorporates these regulations, enhancing the DWA algorithm with fuzzy logic to ensure compliance. Finally, simulation experiments validate the proposed algorithm, demonstrating that the planned paths are safer and more stable, ensuring the safe navigation of USVs in compliance with COLREGS.

Dynamic location model for designated COVID-19 hospitals in China.

In order to effectively cope with the situation caused by the COVID-19 pandemic, cases should be concentrated in designated medical institutions with full capability to deal with patients infected by this virus. We studied the location of such hospitals dividing the patients into two categories: ordinary and severe. Genetic algorithms were constructed to achieve a three-phase dynamic approach for the location of hospitals designated to receive and treat COVID-19 cases based on the goal of minimizing the cost of construction and operation isolation wards as well as the transportation costs involved. A dynamic location model was established with the decision variables of the corresponding 'chromosome' of the genetic algorithms designed so that this goal could be reached. In the static location model, 15 hospitals were required throughout the treatment cycle, whereas the dynamic location model found a requirement of only 11 hospitals. It further showed that hospital construction costs can be reduced by approximately 13.7% and operational costs by approximately 26.7%. A comparison of the genetic algorithm and the Gurobi optimizer gave the genetic algorithm several advantages, such as great convergence and high operational efficiency.

A Mathematical Model for the Combination of Power Ultrasound and High-Pressure Processing in the Inactivation of Inoculated E. coli in Orange Juice.

The mathematical modeling of a combination of non-thermal technologies for E. coli inactivation is of great interest for describing the dynamic behavior of microorganisms in food, with the goal of process control, optimization, and prediction. This research focused on the design and implementation of a mathematical model to predict the effect of power ultrasound (US), high-pressure processing (HPP), and the combination of both non-thermal technologies on the inactivation kinetics of E. coli (DSM682) inoculated in orange juice. Samples were processed by US, HPP, and a combination of both technologies at varying process parameters, and a mathematical model for microbial inactivation was developed using a System Dynamics approach. The results showed that the combination of these technologies exhibited a synergistic effect, resulting in no detectable colony-forming units per mL of juice. The developed model accurately predicted the inactivation of E. coli following the combination of these technologies (R2 = 0.82) and can be used to predict microbial load reduction or optimize it based on process parameters. Additionally, combining both techniques offers a promising approach for extending the shelf life of fresh juices using non-thermal stabilization technology.

Integrating Fintech-Driven Technical Analysis into Value Investing: A Study of Moving Averages in Cryptocurrency Market Dynamics

This research explores the application of fintech-driven technical analysis, specifically focusing on moving averages within value investing. Using Coinbase as a case study, the study examines the effectiveness of moving averages in volatile market conditions where traditional metrics are less applicable. Historical stock data was exported from Yahoo Finance and analyzed using Excel, emphasizing the 30 vs. 10 moving average combination. This combination was chosen to balance short-term and long-term market perspectives. The analysis revealed that while moving averages effectively highlight trends and momentum shifts, they are lagging indicators and require supplementary analysis to account for external factors such as news and regulatory changes. Despite not being a precise predictive tool, moving averages provided valuable insights into market behavior, supporting the argument that fintech tools can complement traditional value investing strategies by offering real-time analysis and dynamic approaches to market assessment.

Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations

The coastal zone is a dynamic region that can change rapidly and significantly with respect to the morphology of the beach and incoming wave conditions. Runup forecasts may be improved by adapting a dynamic approach that allows for different runup models to be implemented in response to changes in beach state. Accurately forecasting wave runup is critical to characterize exposure to coastal hazards and provide an early warning against potential erosion and inundation. Here, we developed a decision tree model to produce a weighted ensemble of existing runup models to predict 1.25 years of runup at Duck, North Carolina, USA. We then applied the calibrated decision tree model to reproduce observed runup during the DUNEX experiment in Pea Island, North Carolina, USA. We found that the decision tree approach yielded a prediction that was comparable or greater in accuracy (i.e. higher r2, lower RMSE) than the individual runup models. We also interrogated the decision tree predictions to determine how the individual models perform relative to each other and why certain models perform better than others under the same observed wave and beach conditions. We found that the decision tree approach drew on the processes represented in the individual models in the ensemble to produce a forecast that is accurate and explainable without relying on prior knowledge of the study site(s) or requiring manual adjustments beyond the initial model training.