Allocation Methodology Research Articles

CG‐FlexBeamOpt: Advanced solution methodology for high throughput GEO satellite beam laydown and resource allocation

SummaryModern satellite communication systems are designed to serve dispersed users with changing operational requirements. Allocating resources to meet these requirements becomes highly complex when the size, location, and allocated frequency of satellite beams are flexible. We developed solution techniques that generate feasible beam patterns and frequency allocations to maximize the total demand met across all users. In our approach, an integer program selects an optimal set of beams from a heuristically generated pool of feasible candidate beams. The challenge is in how to efficiently build a pool of good quality candidate beams from exponentially many possible solutions. An innovative column generation‐style heuristic to generate mathematically justifiable beams is presented to address this challenge. We also derived two other heuristic candidate beam generation algorithms to compare and contrast performance and robustness characteristics of different algorithmic choices. We tested the performance of our three new approaches on 12 operational instances that vary in user distribution, user numbers, and demand distribution. While the methods performed differently under varying operational scenarios, the column generation‐based methods provided the best trade‐off between computation time and solution quality in most cases. We further tested our two best performing algorithms for scalability. Our column generation‐based methods were able to provide good quality solutions with up to 400 beams and 5,000 users. Our work provides valuable insights for real‐life implementation: an end‐user of our system can select the solution approach based on their business need, computational (time) budget, and the desired solution quality.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.

Dynamic Resource Allocation for AI/ML Applications in Edge Computing: Framework Architecture and Optimization Methods

This scholarly paper introduces an extensive architectural framework and optimization strategies designed specifically for dynamic resource allocation in edge computing environments, with a focus on AI/ML applications. The rise of edge computing presents a viable solution for managing the computational complexities of AI/ML tasks by utilizing resources in proximity to data sources. Nevertheless, effective resource allocation encounters significant hurdles due to the diverse and ever-changing nature of edge environments. In addressing these challenges, the paper introduces an innovative framework that integrates dynamic resource allocation methodologies with the unique requirements of AI/ML applications. This framework encompasses a range of optimization techniques customized to efficiently distribute resources, taking into account factors such as workload attributes, resource availability, and latency limitations. Through extensive simulations and evaluations, the study showcases the effectiveness of the proposed approach in enhancing resource utilization, reducing latency, and bolstering overall performance for AI/ML workloads within edge computing scenarios.

Бюджетно-финансовое положение субъектов Российской Федерации: среднесрочные тенденции в региональном разрезе

The article is devoted to the changes in the fiscal and financial situation of Russian regions in 2020–2022. During this period, the regions faced a new challenge in the form of a pandemic. The author gives a quantitative assessment of the changes that have occurred, analyzes the features of support measures and the regional policy being implemented. The article examines changes in the parameters of regional budget revenues, budget deficits and debt burden. In addition, the author identifies a group of regions with maximum changes in per capita budget revenues and the factors that caused these changes. The author pays special attention to measures in the field of debt policy, methodology for equalization grant allocation, as well as demographic characteristics affecting regional expenditures. The article presents proposals for possible further improvement of the system of interbudgetary fiscal relations, including approaches to the provision of unconditional gap-filling financial support.

Data-driven two-stage robust optimization dispatching model and benefit allocation strategy for a novel virtual power plant considering carbon-green certificate equivalence conversion mechanism

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure.

Enhancing production monitoring: A back allocation methodology to estimate well flow rates and assist well test scheduling

Production flow rates are crucial to make operational decisions, monitor, manage, and optimize oil and gas fields. Flow rates also have a financial importance to correctly allocate production to fiscal purposes required by regulatory agencies or to allocate production in fields owned by multiple operators. Despite its significance, usually only the total field production is measured in real time, which requires an alternative way to estimate wells’ production. To address these challenges, this work presents a back allocation methodology that leverages real-time instrumentation, simulations, algorithms, and mathematical programming modeling to enhance well monitoring and assist in well test scheduling. The methodology comprises four modules: simulation, classification, error calculation, and optimization. These modules work together to characterize the flowline, wellbore, and reservoir, verify simulation outputs, minimize errors, and calculate flow rates while honoring the total platform flow rate. The well status generated through the classification module provides valuable information about the current condition of each well (i.e. if the well is deviating from the latest well test parameters), aiding in decision-making for well testing scheduling and prioritizing. The effectiveness of the methodology is demonstrated through its application to a representative offshore oil field with 14 producing wells and two years of daily production data. The results highlight the robustness of the methodology in properly classifying the wells and obtaining flow rates that honor the total platform flow rate. Furthermore, the methodology supports well test scheduling and provides reliable indicators for well conditions. By utilizing real-time data and advanced modeling techniques, this methodology enhances production monitoring and facilitates informed operational decision-making in the oil and gas industry.

Optimal Power Allocation in Optical GEO Satellite Downlinks Using Model-Free Deep Learning Algorithms

Geostationary (GEO) satellites are employed in optical frequencies for a variety of satellite services providing wide coverage and connectivity. Multi-beam GEO high-throughput satellites offer Gbps broadband rates and, jointly with low-Earth-orbit mega-constellations, are anticipated to enable a large-scale free-space optical (FSO) network. In this paper, a power allocation methodology based on deep reinforcement learning (DRL) is proposed for optical satellite systems disregarding any channel statistics knowledge requirements. An all-FSO, multi-aperture GEO-to-ground system is considered and an ergodic capacity optimization problem for the downlink is formulated with transmitted power constraints. A power allocation algorithm was developed, aided by a deep neural network (DNN) which is fed channel state information (CSI) observations and trained in a parameterized on-policy manner through a stochastic policy gradient approach. The proposed method does not require the channels’ transition models or fading distributions. To validate and test the proposed allocation scheme, experimental measurements from the European Space Agency’s ARTEMIS optical satellite campaign were utilized. It is demonstrated that the predicted average capacity greatly exceeds other baseline heuristic algorithms while strongly converging to the supervised, unparameterized approach. The predicted average channel powers differ only by 0.1 W from the reference ones, while the baselines differ significantly more, about 0.1–0.5 W.

Attribution of Global Warming Potential impacts in a multifunctional metals industry system using different system expansion and allocation methodologies

PurposeIn order to reach a more circular economy, materials previously classified as waste can be upgraded and turned into valuable co-products, with associated environmental benefits. The generation of co-products raises many questions around the multifunctionality issue from a life cycle perspective. This article explores the attribution of Global Warming Potential (GWP) impacts for an ironmaking process, HIsarna, which additionally produces two co-products: zinc-rich process dust and slag, suitable for the zinc and cement sectors, respectively.MethodsA wide range of LCA allocation methodologies are applied to attribute impacts between the main product, hot metal, and the two co-products. These include system expansion, physical allocation, economic allocation and zero burden allocation. Each method attributes a different GWP to each co-product. Additionally, different perspectives are explored to consider the most suitable methods according to the co-product user and the co-product producer. For instance, it might be in the co-product user’s interest that the co-product GWP was minimised, and lower than other material inputs performing a similar function. Conversely, the co-product producer may be incentivised to lower its primary product’s GWP by attributing the greatest possible burden to the co-products.Results and discussionThe GWP impacts for zinc-rich process dust range from 0 to 3.71 kg CO2 eq. per kg. At the higher end, the GWP of zinc-rich dust would be higher than that of primary zinc concentrate. A similarly wide range is applicable for slag, 0 to 1.27 kg CO2 eq. per kg. This impacts the final GWP applied to HIsarna hot metal, which has an initial GWP of 1.72 kg CO2 eq. per kg but could decrease to 1.17 kg CO2 eq. per kg depending on the allocation methods employed. This would be a substantial reduction of over 30%, larger than many decarbonisation options that are predicted to provide. This scenario would also heavily burden the co-products and could be in conflict with interests of a co-product user seeking to utilise low emissions feedstocks as part of a decarbonisation strategy.ConclusionsThe reduction in GWP impact attributed to hot metal with the different approaches highlights the relevance of harmonizing the allocation methods used for co-products. The appropriateness of each of the approaches for attributing GWP impacts has been explored, offering insights as to how the benefits of such systems could be assessed and attributed in the future as circularity strategies and valuable co-products become more prevalent.

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

Evidence-based knowledge management: a topic modeling analysis of research on knowledge management and analytics

Purpose Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics. Design/methodology/approach A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature. Findings The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media. Originality/value Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.

Application of IoT network for marine wildlife surveillance

Abstract Every day of the week, wireless communication is almost all around us. The Internet of Things (IoT) is a standard protocol used to describe the rapidly advancing technology in which almost every electronic device is or may be connected to the Internet. These electronic gadgets constantly provide data signals to the gateways, which satellites such as those in Low Earth Orbit may transmit. Because of these networks’ limited resources and the IoT, these transactions must be completed with the least amount of latency and data loss possible. We also analyze the performance implications of implementing RF-based powering for such a network. The techniques presented in this paper may benefit the scientific community and industry in general when it comes to the dynamic distributed parameter allocation methodology for IoT network devices. We will also discuss how research on animals and the natural environment has been impacted by IoT breakthroughs, in particular, animal sensors’ limits and incapacity to broadcast from everywhere. Our analysis illustrates the most effective data transmission technique and establishes the bounds of these restrictions. Furthermore, the physics of the RF channel plays a critical role in the uncertainty of the channel as well as the amount of energy harvested. By employing simulation based on the physics of the RF channel, the article shows the performance of the system considering both the uncertainty of data arrival as well as the variability of the channel. The findings of the simulation show that the devices consume less energy overall as the signal-to-noise ratio rises. Furthermore, a timing factor of 10–15% is shown to be effective in maintaining a constant mean rate and increasing the energy efficiency of the system.

Current Situation in Chinas Organ Transplantation Market and Analysis on the Main Allocation Methodologies: Taking Kidney Transplantation as an Example

Organ transplantation is a surgical operation to rescue a patient who has organ failure by implanting the other people's healthy organ into the patients body accordingly to save and prolong the patient's life. This is also one of the focuses of today's researches. Organ transplantation started relatively late in China, but since then, organ transplantation made a great progress in China in just a few years. But in the meanwhile, the organ transplantation market in China still faces various problems. This paper uses a large number of data, such as the number of people waiting for organ transplantation in different countries at different times and the distribution data of qualified organ transplant institutions in various regions of China, to analyze the problems faced by China in organ transplantation. It is found that organ transplantation in China, especially kidney transplantation, faces the problems of large demand gap and regional inequality. To meet these challenges, this paper explains four principles of allocation and then introduces two different allocation mechanisms as a reference for improving organ allocation in China.

The multi-objective optimization framework: A step towards minimizing life-cycle costs and energy consumption of carbon fibre automotive structures

Design of a sustainable and economical composite structure is challenging due to the often contradicting design drivers such as energy consumption and cost. Therefore, predicting the environmental and economic impact in the early stages of product design results in the significant reduction of energy consumption and cost. Consequently, the impact of contrasting objectives on an optimized carbon fibre reinforced polymer (CFRP) demonstrator is evaluated in this study. This evaluation is performed by integrating a predictive life-cycle assessment and costing framework in a multi-objective optimization methodology. The varying design configurations include designs with and without stiffeners. Furthermore, the impact of end-of-life allocation on the optimized design is also evaluated in this study. The results show a marked difference between the energy consumption of the various optimized designs with energy optimization producing the most efficient solution. However, the mass of the demonstrator increases by 7–9 kg across the different design configurations when the closed loop allocation model is implemented. Consequently, the results show the importance of selecting an appropriate EOL allocation methodology in evaluating the energy consumption of a product. The results also show that the parameters driving the energy consumption (mass and material configuration) and cost (complexity and manufacturability) are varied and often contrasting. The differences in mass, cost, and energy optimized designs further reinforces the importance of coupling cost and energy evaluation in design of sustainable products. Finally, the study demonstrates the need for a holistic life-cycle based assessment in early stage sustainable product design.

Allocation of PV Systems with Volt/Var Control Based on Automatic Voltage Regulators in Active Distribution Networks

This paper presents an optimal allocation methodology of photovoltaic distributed generations (PVDGs) with Volt/Var control based on Automatic Voltage Regulations (AVRs) in active distribution networks considering the non-dispatchable mode of PVDG operation. In the proposed methodology, an intelligent coordinated Var control is activated via controlling the AVR tap position and the Var injection of PV inverters to achieve a compromise between reducing active and reactive power losses and enhancing voltage quality in a distribution network. Also, the scheduled power factor mode of operation is investigated for the PV inverters. Added to that, the proposed allocation methodology is handled on the basis of hourly loading variation under simultaneous control modes of PV inverters and AVR. Moreover, the impacts of the specified number of PVDGs are assessed on the distribution system’s performance. A recent effective optimizer of the slim mold algorithm (SMA) is dedicated to solving the proposed optimization framework. The simulation implementations are executed on a practical distribution network of the Kafr Rabea area related to South Delta Electricity Company in Egypt. Also, the application is conducted for a large-scale distribution network from the metropolitan area of Caracas. The proposed methodology provides superior performance in minimizing the active and reactive power losses and improving the voltage profile.

Life cycle assessment of a marine biorefinery producing protein, bioactives and polymeric packaging material

Abstract Purpose Algal research has been dominated by the use of marine biomass (mainly microalgae) as feedstock in the production of second-generation biofuels, albeit with limited economic success. A promising alternative strategy is the valorisation of seaweed (macroalgae), with the cascaded extraction of its high-value components, as well as lower-value components further downstream, under the ‘biorefinery concept’. The goal of this study was to assess the environmental performance of one such marine biorefinery situated in the UK. Methods Attributional life cycle assessment (LCA) was conducted on a hypothetical marine biorefinery coproducing fucoidan, laminarin, protein and alginate/cellulose packaging material (target product), from cultivated Saccharina latissima. The functional unit was the production of 1 kg of packaging material. A total of 6 scenarios were modelled, varying in coproduct management methodology (system expansion, mass allocation or economic allocation) and applied energy mix (standard or green energy). Sensitivity analysis was also conducted, evaluating the systems response to changes in allocation methodology; product market value; biomass composition and transport mode and distance. LCA calculations were performed using OpenLCA (version 1.10.3) software, with background processes modelled using the imported Ecoinvent 3.6 database. Environmental impacts were quantified under ReCiPe methodology at the midpoint level, from the ‘Heirarchist’ (H) perspective. Results and discussion The overall global warming impacts ranged from 1.2 to 4.52 kg CO2 eq/kg biopolymer, with the application of economic allocation; 3.58 to 7.06 kg CO2eq/kg with mass allocation and 14.19 to 41.52 kg CO2eq/kg with system expansion — the lower limit representing the instance where green electricity is used and the upper where standard electricity is employed. While implementing the green energy mix resulted in a 67% reduction in global warming impacts, it also incurred a 2–9 fold increase in overall impacts in the categories of terrestrial acidification, human non-carcinogenic toxicity, land-use and terrestrial ecotoxicity. Economic allocation resulted in burden shifting most favourable to the packaging material pathway. Conclusions This study demonstrates that the road to environmental optimisation in marine biorefineries is fraught with trade-offs. From the perspective of LCA — and by extension, the eco-design process that LCA is used to inform — when evaluating such product systems, it serves to strike a balance between performance across a broad spectrum of environmental impact categories, along with having consideration for the nature of energy systems incorporated and LCA methodological elements. Graphical Abstract

Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Listed Real Estate as an Inflation Hedge Across Regimes

AbstractThis paper examines the inflation-hedging capability of listed real estate (LRE) companies in the US from 1975 to 2023, and in three other economies—the UK, Japan, and Australia—from 1990 to 2023. By using a Markov switching vector error correction model (MS-VECM), we identify that the short-term hedging ability moves towards being negative or zero during turbulent periods. In stable periods, LRE provides good protection against inflation. In the long term, LRE offers a good hedge against expected inflation and shows a superior inflation hedging ability than stocks. Additionally, we identify inflation-hedging portfolios by minimizing the expected shortfall. This inflation-hedging portfolio allocation methodology suggests that listed real estate stocks should play a significant role in investor portfolios.

Analysis of Some Communication-Related Queuing System Modeling and Performance

The paper presents research into the analysis of queuing systems in the context of communication, with the goal of creating precise models and evaluating their efficacy. The importance of queueing systems cannot be overstated in fields as disparate as telecommunications, computer networks, and the service sector. Analyzing how these systems function allows us to better allocate resources, boost performance, and provide a better experience for end users. The main goals are to create mathematical models that precisely capture the queuing behavior and to assess the effectiveness of these models in terms of metrics like queue length, waiting time, and system utilization. To do so, it is necessary to perform an extensive analysis of the relevant literature and theoretical frameworks. The properties of communication-related systems are examined, and many queuing models, including the M/M/1, M/M/c, and M/G/1, are modified accordingly. In addition, simulation methods are used to verify the accuracy of the suggested models and assess how they would fare in practical applications. The research yields useful information on the dynamics of queuing systems used in the field of communication. They show how changes to the arrival rate, service rate, and number of servers affect the system's most important performance indicators. Different scheduling policies, routing tactics, and resource allocation methodologies are also investigated to determine their impact on system performance.

Swarm Robotics: A Survey from a Multi-Tasking Perspective

The behaviour of social insects such as bees and ants has influenced the development of swarm robots. To enable robots to cooperate together, swarm robotics employs principles such as communication, coordination, and collaboration. Collaboration among multiple robots can lead to a faster task completion time compared to the utilisation of a single, complex robot. One of the key aspects of swarm robotics is that control is distributed uniformly across the robots in the swarm, which boosts the system’s resilience and fault tolerance. Through the use of the robots’ embodied sensors and actuators, this distributed control often facilitates the emergence of collective behaviours through the interaction of the robots with one another and with the environment. The purpose of this survey is to examine the reasons behind the lack of utilisation of swarm robots in multi-tasking applications, which will be accomplished by studying previous research works in the field. We examine the literature from the perspective of multi-tasking: we pay particular attention to concepts that contribute to the progress of swarm robotics for multi-tasking applications. To do this, we first examine the different studies in multi-tasking swarm robotics, covering platforms, multi-tasking scenarios, sub-task allocation methodologies, and performance metrics. We then highlight several swarm robotics related disciplines that have significant effect on the development of swarm robotics for multi-tasking problems. We propose two taxonomies: the first categorises works based on the characteristics of the scenarios being handled, whereas the second taxonomy categorises works based on the swarming strategies utilised to achieve multi-tasking capabilities. We finish with a discussion of swarm robots’ existing limitations for real-world multi-tasking applications, as well as recommendations for future research directions.

Comparison of crystalloid and colloid co-load combined with norepinephrine prophylaxis on post-spinal anesthesia hypotension during cesarean delivery: a randomized sequential allocation dose-finding study.

Fluid loading is an essential component of treatment for reducing the incidence of post-spinal anesthesia hypotension and is necessary to maintain intravascular volume, perfuse tissues, and control spinal anesthesia hypotension after sympathetic blockade. We performed a randomized sequential allocation dose-finding study to compare the effects of 10 mL/kg crystalloid and 6% hydroxyethyl starch (130/0.4) co-load on the ED90 of prophylactic norepinephrine infusion for preventing post-spinal anesthesia hypotension during cesarean delivery. Eighty patients were randomly allocated to receive either a 10 mL/kg crystalloid (Crystalloid Group, n = 40) or 6% hydroxyethyl starch (130/0.4) (Colloid Group, n = 40) co-load combined with prophylactic norepinephrine infusion during spinal anesthesia for cesarean delivery. The first patient received an initial prophylactic norepinephrine infusion rate of 0.025 μg/kg/min. Subsequent patients received a 0.005 μg/kg/min gradient dose of prophylactic norepinephrine. This dose was administered as a gradient based on its effectiveness for preventing post-spinal anesthesia hypotension (defined as SBP < 80% of baseline value) and determined by the up-and-down sequential allocation methodology. The primary study outcome was the ED90 of prophylactic norepinephrine infusion. Secondary outcomes included the incidence of post-spinal anesthesia hypotension, bradycardia, hypertension, Apgar scores, and umbilical artery blood gas values were also measured. The ED90 values of prophylactic norepinephrine infusion for preventing post-spinal anesthesia hypotension during cesarean delivery were 0.063 μg (95% CI: 0.050 to 0.064) and 0.062 μg (95% CI: 0.045 to 0.064) using isotonic regression analysis, and 0.068 μg (95% CI: 0.056 to 0.353) and 0.060 μg (95% CI: 0.050 to 3.590) using probit regression analysis in the Crystalloid Group and Colloid Group, respectively. The secondary outcomes were comparable between the two groups. The administration of a 10 mL/kg 6% hydroxyethyl starch (130/0.4) does not provide additional benefits compared to crystalloid co-load in reducing the ED90 of prophylactic norepinephrine infusion for preventing post-spinal anesthesia hypotension during cesarean delivery.