Resource Allocation Control Research Articles

An integrated optimization strategy for mission planning and control resource allocation for long-endurance unmanned aerial vehicle

PurposeWith burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.Design/methodology/approachWe consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.FindingsNumerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.Originality/valueDrawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models

Study regionThe Kaidu River Basin originates from the southern slope of the Tienshan Mountains in the Xinjiang Uygur Autonomous Region, China. Study focusAccurate runoff simulation and prediction significantly affect flood control, drought resilience, and water resource allocation decisions. This study establishes the GR4J-6 model (modèle du Génie Rural à 4 paramètres Journalier-6, including a snowmelt module) and integrates it with the LSTM (Long Short-Term Memory) model to construct the hybrid GR4J-6-LSTM model and enhance the simulation accuracy of snowmelt runoff. A case study is conducted in the Kaidu River Basin to demonstrate the applicability of these models in cold and arid regions. The accuracy of the GR4J-6, LSTM, and GR4J-6-LSTM models is evaluated using Nash-Sutcliffe efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Squared Error (RMSE) metrics. In addition, the contributions of each feature variable in the models are analyzed using the SHapley Additive exPlanations (SHAP) method to enhance the reliability of the results. New hydrological insights for the regionThe GR4J-6 model demonstrated good applicability in the Kaidu River Basin, with NSE, KGE, and RMSE values of 0.69, 0.79, and 39.39 m3/s during the validation period, respectively. The hybrid model GR4J-6-LSTM exhibited the highest comprehensive accuracy among all the models, with NSE, KGE, and RMSE values of 0.84, 0.87, and 28.79 m3/s, respectively. In the LSTM model, temperature and precipitation were found to significantly influence the simulated runoff, indicating that higher temperature and precipitation lead to increased runoff. In the GR4J-6-LSTM model, Tmin (minimum temperature) and the hydrological feature variable Qsim exhibited a strong positive correlation with simulated runoff, as Tmin and Qsim increased, they promoted stronger flow production. This study provides a framework for runoff simulation in snowmelt river basins, offering a reference for projecting extreme hydrological events under climate change.

Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey

The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.

Leveraging variational autoencoders and recurrent neural networks for demand forecasting in supply chain management: A case study

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

Vaccination and transportation intervention strategies for effective pandemic control

The COVID-19 pandemic highlighted the pivotal role of transportation-related controls and vaccination in mitigating widespread infections. However, the substantial costs associated with these interventions necessitate a nuanced equilibrium between control measures and infection risks. This study, driven by the recognition of spatial heterogeneities in mobility networks and epidemic vulnerability, presents an optimal control framework for two control measures—vaccine distribution and transportation restrictions—within a metapopulation structure. The overarching goal is to minimize the total costs derived from the implementation of control measures and health and opportunity loss from infection. Our findings highlight the effectiveness of transportation control and vaccine distribution in reducing both total costs and infection rates. Notably, the efficacy of these control measures exhibits regional variations, and the simultaneous implementation of both measures emerges as the most effective and economically viable strategy. The synergy effect between vaccine distribution and transportation restrictions is also a key observation in our simulations, showcasing their complementary roles in pandemic control. By considering the spatial nuances of mobility networks and infection vulnerability, this framework provides a flexible and region-specific strategy to optimize the allocation of resources for pandemic control, ultimately striking a balance between efficacy and economic feasibility.

Joint Beam Direction Control and Radio Resource Allocation in Dynamic Multi-Beam LEO Satellite Networks

Joint Beam Direction Control and Radio Resource Allocation in Dynamic Multi-Beam LEO Satellite Networks

Control Period Adaptation and Resource Allocation for Joint Uplink and Downlink in NB-IoT Networks

Control Period Adaptation and Resource Allocation for Joint Uplink and Downlink in NB-IoT Networks

Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure.

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system's robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.

Design and On-Orbit Performance of the Payload Rack Thermal Management System for China Space Station Experimental Lab Module

An efficient and reliable spacecraft payload thermal management method is one of the key problems to build China Space Station (CSS). Aiming at the outstanding characteristics and difficulties of the payloads rack in the experimental lab module of CSS, in the aspects of thermal control interface, boundary constraint, scale quantity, space layout, diversity of temperature control, operation mode and working life, etc., a two-stage thermal management scheme based on single-phase fluid loop is proposed and constructed. A fluid drive unit is designed to drive the fluid circulation and health management of the module thermal control bus (TCB). In view of the different characteristics of the payload racks, three different types of second-stage thermal control systems are proposed, and different thermal control terminals are designed, in order to solve the problem of thermal control resource allocation and health management in payload racks. A method based on “pump bypass” and “valve resistance ratio” is used to control the flow rate of the thermal management system. Based on the overall scheme of the thermal management system and the developed flight products, on-orbit verification and data analysis are carried out. Flight verification shows that the key parameters of the fluid loop in the thermal management system on orbit are basically consistent with the ground data; the maximum deviation is 1.84%. The pressure control accuracy of the bypass system reaches ±2%, and the active heat transfer efficiency of the integrated thermal management system is over 83%, which can effectively meet the thermal control requirements of the two-stage payloads in the module and rack. After on-orbit flight verification, the designed thermal control system works well on orbit and exhibits good stability.

Modeling COVID-19 spread using multi-agent simulation with small-world network approach

BackgroundThe rapid global spread of COVID-19 has seriously impacted people’s daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems.MethodsWe propose a spatiotemporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents’ social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved.ResultsExperimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating 1 hundred times. Additionally, the model simulates and compares different “city closure” scenarios. The results showed that implementing a ‘lockdown’ 10 days earlier would lead to the peak number of infections occurring 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections.DiscussionOur methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Molecular Epidemiology of Plasmodium falciparum Infections Using PCR-based Assays in Jos, Nigeria-cross-sectional Study

Background: Malaria remains a significant health threat globally, with Plasmodium falciparum being the predominant and lethal parasite in Africa. Nigeria is still faced with ongoing cases of asymptomatic malaria, hindering effective control measures.
 Aim: The aim was to generate epidemiological data that will provide good background and guide strategies for driving malaria control efforts, research, and resource allocation in the region.
 Place and Duration of Study: This study was conducted in Jos, Plateau State, Nigeria, where the samples were originally collected within about 16 months between October 2019 and January 2021.
 Methodology: A cross-sectional molecular epidemiological study was conducted using 136 microscopically screened 2 plus (++) and above positive malarial whole blood samples obtained in EDTA bottles from two hospitals in Jos, Plateau State, Nigeria. The DNA extraction was performed according to the manufacturer's instructions using Zymo Research extraction kits. Plasmodium genus and Plasmodium falciparum were detected in the samples using the PCR method and gel electrophoresis.
 Results: In the results, using PCR techniques, 47.8% (65/136) of the total malaria-positive samples collected were confirmed for the presence of the Plasmodium genus. Out of these 65 positive samples, 63 were found to be Plasmodium falciparum.
 Conclusion: This study demonstrates that Plasmodium falciparum remains the predominant malaria species in Jos, Plateau State, comprising approximately 96.9% (63/65) of the malarial cases. This indicates that only about 3% of malaria cases affecting the residents of Jos, Plateau State might be caused by the other four species of malaria parasites (Plasmodium vivax, Plasmodium malariae, Plasmodium ovale, and Plasmodium knowlesi).

Sipongi System: Navigating and fostering collaboration in Indonesia

The Sipongi System is essential in dealing with forest and land fires because this system provides real-time data that empowers stakeholders and communities to proactively overcome fire dangers. Its advantages are seen in its ability to provide detailed information regarding weather conditions, wind patterns, water levels in peatlands, air quality, and responsible work units. This data facilitates efficient decision-making and resource allocation for fire prevention and control. As an embodiment of Collaborative Governance, the Sipongi System actively involves various stakeholders, including government institutions, local communities, environmental organizations and the private sector. This cooperative approach fosters collective responsibility and accountability, improving fire management efforts. The Sipongi approach is critical in reducing forest and land fires in Indonesia by providing real-time data and a collaborative governance model. This results in faster response times, more effective fire prevention and better resource allocation. Although initially designed for Indonesia, the adaptable nature of the system makes it a blueprint for addressing similar challenges in other countries and regions, tailored to specific needs and environmental conditions. Qualitative research methods underlie this study, including interviews with key stakeholders and analysis of credible sources. Government officials, community leaders, environmental experts and organizational representatives were interviewed to comprehensively examine the mechanisms of the Sipongi System and its impact on forest and land fire management in Indonesia. Future research should explore the application of Sipongi Systems and collaborative governance in various contexts by conducting comparative studies across countries and ecosystems. Additionally, assessing the long-term impact and sustainability of the Sipongi System is critical to evaluating its effectiveness over time.

Energy efficient multi-carrier NOMA and power controlled resource allocation for B5G/6G networks

Energy efficient multi-carrier NOMA and power controlled resource allocation for B5G/6G networks

Reinforcement-Learning-Based Proactive Control for Enabling Power Grid Resilience to Wildfire

Industrial electric power grid operation subject to an extreme event requires decision-making by human operators under stressful conditions. Decision making using system data informatics under adverse dynamic events, especially if forecasted, should be supplemented by intelligent proactive control. Power transmission system operation during wildfires requires resiliency-driven proactive control for load shedding, line switching, and resource allocation considering the dynamics of the wildfire and failure propagation to minimize the impact on the system. However, the possible number of line and load switching in an extensive industrial system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use pre-planned or greedy algorithms. In this work, we model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. Our approach allows the controller to provide setpoints for all generation fleets in the power grid. We evaluate our approach utilizing the IEEE test system mapped onto a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load outage during an extreme event. It reduces power flow through lines that are to be de-energized, and adjusts the load demand by increasing power flow through other lines.

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.

Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. Wealso aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

Integrated Computation Offloading, UAV Trajectory Control, Edge-Cloud and Radio Resource Allocation in SAGIN

Integrated Computation Offloading, UAV Trajectory Control, Edge-Cloud and Radio Resource Allocation in SAGIN

Modeling and Optimal Control for Resource Allocation in the Epidemic Monitoring of a Multi-group Population

Modeling and Optimal Control for Resource Allocation in the Epidemic Monitoring of a Multi-group Population

Joint Power Control and Resource Allocation for Optimizing the D2D User Performance of Full-Duplex D2D Underlying Cellular Networks

D2D communication is a promising technology for enhancing spectral efficiency (SE) in cellular networks, and full-duplex (FD) has the potential to double SE. Due to D2D’s short-distance communication and low transmittance power, it is natural to integrate FD into D2D, creating FD-D2D to underlay a cellular network to further improve SE. However, the residual self-interference (RSI) resulting from FD-D2D and interference arising from spectrum sharing between D2D users (DUs) and cellular users (CUs) can restrict D2D link performance. Therefore, we propose an FD-D2D underlying cellular system in which DUs jointly share uplink and downlink spectral resources with CUs. Moreover, we present two algorithms to enhance the performance experience of DUs while improving the system’s SE. For the first algorithm, we tackle an optimization problem aimed at maximizing the sum rate of FD-DUs in the system while adhering to transmittance power constraints. This problem is formulated as a mixed-integer nonlinear programming problem (MINLP), known for its mathematical complexity and NP-hard nature. In order to address this MINLP, our first algorithm decomposes it into two subproblems: power control and spectral resource allocation. The power control aspect is treated as a nonlinear problem, which we solve through one-dimensional searching. Meanwhile, spectral resource allocation is achieved using the Kuhn–Munkres algorithm, determining the pairing of CUs and DUs sharing the same spectrum. As for the second algorithm, our objective is to enhance the individual performance of FD-DUs by maximizing the minimum rate among them, ensuring more uniform rate performance across all FD-DUs. In order to solve this optimization problem, we propose a novel spectral resource allocation algorithm based on bisection searching and the Kuhn–Munkres algorithm, whereas the power control aspect remains the same as in the first algorithm. The numerical results demonstrate that our proposed algorithm effectively enhances the performance of DUs in an FD-D2D underlying cellular network when compared to the sum rate maximization design.

RSV Pneumonia Mortality: Hematological Malignancies As a Silent Amplifier - Insights from the National Inpatient Sample 2020

Background: RSV is a prevalent cause of acute lower respiratory infections, primarily in young children, but can also lead to severe pneumonia in adults, especially the elderly and immunocompromised. Hematological malignancies, a group of neoplastic disorders affecting blood, bone marrow, and lymph nodes, can render patients more susceptible to infections and complications. The aim of this study is to evaluate the impact of hematological malignancies on inpatient mortality, hospital length of stay, and hospitalization costs and charges in adult patients during RSV-related hospitalizations, using retrospective data from the National Inpatient Sample (NIS) 2020. Methods: In this retrospective cohort study, we used NIS data for 2020. International Classification of Diseases, Tenth Revision, with Clinical Modification (ICD-10-CM) codes were utilized to query the NIS database and identify adults (Age ≥18 years) hospitalized with RSV pneumonia. Our population of interest was subsequently divided into two groups based on the presence or absence of unremitted hematological malignancies. Inpatient mortality served as the primary outcome, with secondary outcomes being hospital length of stay, hospitalization charges, and costs. A multivariate logistic regression analysis was conducted using STATA MP 16.1, incorporating various hospital and patient-level factors. The Charlson comorbidity index was utilized to account for the burden of comorbidities in the analysis. Results: The total population of adult patients admitted in 2020 with RSV pneumonia was 13,900. From this, 980 individuals (7.1%) were diagnosed with a hematological malignancy, of which 43% were female. Conversely, 93% of individuals did not have a hematological malignancy, and, females accounted for 59% of this group. Patients with a hematological malignancy had a mean age of 66, while those without had a mean age of 69. In comparison to those without hematological malignancy, patients with this malignancy demonstrated a higher all-cause inpatient mortality rate for PSV pneumonia (12.24% versus 5.23%). When hospital and patient-level factors were taken into consideration, it was noted that RSV pneumonia patients with hematologic malignancy had a higher odds of all-cause inpatient mortality, with an adjusted odds ratio (aOR) of 1.99 (95% Confidence Interval (CI) 1.21- 3.28). Moreover, these patients had a longer hospital stay (12.2 days) compared to those without hematological malignancy (7.5 days), which reflects an adjusted mean difference of 3.8 days (95% CI 1.7-1.9). Overall, patients with hematological malignancies incurred higher mean hospitalization charges and costs, with an adjusted mean difference of USD 29,044 (95% CI 11,928 - 36,008) and USD 105,210 (95% CI 30,947 - 133,730), respectively. Clinical Implications: This study highlights the increased risk of inpatient mortality and resource utilization in RSV-related hospitalizations for patients with hematological malignancies. These findings emphasize the need for timely detection, prophylaxis, preference for antiviral treatment, and the inclusion of the RSV pneumonia vaccine in the prevention strategy. The results also inform healthcare administrators and policymakers about the importance of effective infection control measures, resource allocation, and public health initiatives to protect this vulnerable population.