Allocation Methodology Research Articles

Influence of the Wood Species, Forest Management Practice and Allocation Method on the Environmental Impacts of Roundwood and Biomass

This study quantifies and compares the environmental impacts of production systems of biomass and roundwood from different wood species—maritime pine, eucalyptus, and cryptomeria. The results showed that roundwood and biomass from eucalyptus had the highest environmental impacts in most of the environmental categories. In contrast, cryptomeria products had the lowest impacts. For biomass, the impacts were higher for the forest production scenario when less biomass was produced (eucalyptus). The literature review showed that one of the main topics under study in the quantification of the environmental impacts of biomass is the allocation methodology. Thus, this study compared the environmental impacts of the various scenarios considering different methods of allocation: sub-division of processes, volume, mass, economy, and energy. The results showed that, for most scenarios, the biomass environmental impacts calculated by subdivision of processes had the highest values. In contrast, the environmental impacts of biomass calculated by economic allocation had the lowest environmental impact in most scenarios. The impacts of mass and energy allocation were similar for both products in all scenarios. Furthermore, this study showed that the system boundaries in biomass production have a strong influence on the environmental impacts and require further research.

Measuring corporate Paris Compliance using a strict science-based approach

The achievement of the Paris Agreement climate goals of well-below 2 degrees of warming requires companies to align their greenhouse gas emission reductions with this goal. To measure whether companies are compliant with the Paris targets we propose several strict conditions that any emissions allocation methodology must meet before it can be classified as Paris-Compliant. Our conditions focus on the need for a common, and early as practicable, base year for all companies and consistency with an underlying Paris-aligned decarbonisation pathway. Additionally, we propose four operationalisation requirements to ensure companies can declare they are on a Paris Compliant Pathway including calculations of their carbon budgets and re-alignment pathways. Applying example Paris-Compliant Pathways and associated metrics to ten high emission electric utility companies and ten cement companies, we find that all but one of these companies are not currently Paris-compliant, with every year of delayed action increasing their required rate of decarbonisation and hence the exposure of billions of investment dollars to transition risk. Applying this proposed method will ensure the Paris carbon budget is met and that progress can be tracked accurately - an imperative for any companies and stakeholders seeking to align their decision-making with the Paris Agreement.

Interpretation of multispectral satellite data as a tool for detecting archaeological artifacts (Navkur Plain and Karamleis Plain, Iraq)

Contemporary studies of geographical space, including archaeological research, incorporate multiple spatial digital data. Such data provide an opportunity to extend research to large areas, and to objectify studies on the basis of quantitative data thus obtained and gaining access to the hard-to-reach study area. Examples of such data are satellite images at various spatial resolutions and in a wide spectrum of electromagnetic radiation (visible, infrared, and microwave). The authors made an attempt to use satellite images to analyze the areas of probable location of the Battle of Gaugamela (the Navkur Plain and the Karamleis Plain in Iraq). The photointerpretation was performed, enhanced by the multivariate processing of the multispectral image. The aim of the work was indicating the most likely places where the camp and the battle were located based on the visual interpretation of an array of satellite data. The adopted methodology of precise allocation of interpretative values to remote sensing materials for every detected artifact provided an opportunity to accumulate an extensive amount of information. It also provided the basis for a synthetic analysis regarding the methods of image processing on the one hand and the dates of recording on the other. It turned out that the season in which the photos are recorded is very important—although the best data for analysis turned out to be the autumn data (38% of all recognized artifacts), the use of data from three seasons increased the total number of indicated artifacts by as much as about 50% (the so-called unique detections). In addition, advanced image processing (such as principal component analysis and decorrelation stretch) turned out to be important, as it increased the number of areal artifacts by 31% compared to the interpretation of only photos in natural (true) color composite and false color composite (with near-infrared). The conducted analyses have confirmed the usefulness of high-resolution satellite data for archaeological applications, and the detected and described anomalies visible in satellite images are excellent material for selecting sites for detailed field research.

Constrained economic dispatch following generation outage for hot spinning reserve allocation using hybrid grey wolf optimizer

The paper presents a methodology for hot spinning reserve allocation (HSRA) following expected disconnection of big generators during peak demand. The proposed method includes generation rescheduling, optimal reactive power allocation, and load curtailment to prevent system cascaded outages. Hybrid grey wolf optimization (HGWO) and Marine Predators Algorithm (MPA) are implemented to solve the economic HSRA problem with total costs minimization. The conversion process of HGWO is enhanced utilizing two internal loops based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The effectiveness of the optimization algorithms is conducted on a 66-bus three-area test system for cost minimization following the outage of the biggest generator in each area. The optimal solution maximizes the social benefits of participating consumers, keeping other consumers' reliability at minimum total cost. The percentages of load curtailment in the three cases are 6.9%, 8% and 6.7% of system load, respectively, where there was no solution for the optimal power flow without shedding some loads to satisfy the network constraints. HGWO and MPA are used to determine the optimal solution. The optimal solution depends on the energy price of load curtailment, system configuration as well as the cost coefficients of generators.

Human-robot activity allocation algorithm for the redesign of manual assembly systems into human-robot collaborative assembly

ABSTRACT As a key and enabling technology of Industry 4.0, there is no doubt that manufacturing enterprises will benefit from the introduction of collaborative robots (cobots) in their production processes. A large part of collaborative applications will arise from the re-design of existing manual assembly workstations. For this reason, companies need methods and guidelines for the proper adoption of these systems. Thus, this work presents a systematic methodology for the static allocation of assembly tasks between the human and the robot by considering a human-centric approach and the requirements in terms of technical feasibility, safety and ergonomics, quality, and economics. The proposed approach will support the manufacturers in overcoming technical barriers related to the proper design of collaborative solutions and in the implementation of more sustainable, high-quality, and efficient manufacturing processes starting from manual assemblies. The methodology has been proven and validated in an industrial case study. Results show how the proposed algorithm can support companies in the parallel improvement of the operator’s working conditions as well as the company performance at the same time.

Transmission Pricing Dynamically Integrated with Distribution Planning

Modern electrical networks with automatic control devices and with models that look for minimizing their costs will naturally increase the loading on buses with lower tariffs. We can identify two factors that are contributing to accelerate the network modernization. Firstly, the global targets to reduce the emission of polluting gases, which are boosting the transport electrification, a highly dynamic element. Secondly, the greater participation of clients in decision-making processes due to advances in means of measurement and communication. In this context, the article brings two innovative and direct contributions. The integration of transmission and distribution segments in a unique set of simulation, and the proposal of a novel transmission cost allocation (TCA) methodology. The integration between both segments confirms that loads responsive to tariff signals will seek buses with lower tariffs. The results show that traditional TCA methods do not respond to load variations in a coordinated manner. Therefore, the main contribution of the proposed methodology is to capture the consumption dynamics and make tariff adjustments proportional to the load variations. It preserves the tariff characteristics of traditional TCA methods and automatically induces a dynamic tariff-load balance for the entire power system. Thus, new investments in network reinforcements can be postponed, reducing the total cost paid by users in a long-term prospect.

A Comparison of Functional Fillers—Greenhouse Gas Emissions and Air Pollutants from Lignin-Based Filler, Carbon Black and Silica

The transformation from a fossil-based economy to a sustainable and circular bioeconomy is urgently needed to achieve the climate targets of the Paris Agreement, reduce air pollution and ensure a long-term competitive economy. Due to its carbonaceous and aromatic basic components, lignin has the potential for material valorization within bioeconomy. So far, lignin produced in the pulp and paper industry has mainly been used internally to generate thermal process energy, as it is difficult to extract it from biomass in a pure and unaltered form. The valorization of lignin to improve the economics of pulp mills is a current aim of the industry. Hydrothermal treatment (HTT) of a partial flow from the lignin stream to produce a functional filler for use in polymer blends is one valorization option. The environmental assessment of the lignin-based HTT filler, conducted using life cycle assessment (LCA), shows that substitution of the conventional fillers carbon black and silica could be associated with significant reductions in greenhouse gas emissions and air pollutants. Depending on the allocation methodology and the reference filler considered, approx. 5 kg CO2 eq./kg filler, 80–93% SO2 emissions, 27–79% PM emissions, and 88–98% PAH emissions can be saved.

An Empirical Risk and Return Analysis of Select Stocks in NASDAQ 100

Stock market indices are considered to be a powerful economic indicator. These indices can be classified based on the methodology of weight allocation for each stock and the rules governing the entry, retention and exit criteria of various stocks in the index. This paper presents a descriptive and an exploratory analysis carried out on the daily returns data of NASDAQ 100 (^NDX) index and shortlist of 20 stocks in the index. Random sampling was conducted at the sector level strata of all stocks that make up the index. This approach was followed to avoid selection bias and that stocks from the varied sectors are represented equally for this analysis. R-squared values and correlation coefficients were used to determine the explain-ability and relationship between the stock returns and the index returns respectively. The paper applied descriptive univariate analysis on daily returns at an individual stock level and at an aggregated sector level. Inter-relationship between stocks and the index returns was carried out by computing Pearson’s correlation coefficient across the different combinations of stocks and index return values. Linear regression was carried out identify the explain ability of the variance in the returns of from the index to the returns from the stocks. All analysis was carried out using the python and the stats-models library. As anticipated, the returns of randomly picked 20 stocks were able to explain ~85 % of the variance of the returns of index. One of the primary focus of the paper was to explore whether NASDAQ-100 index can explain the variability of the technology stocks relatively more than the stocks that belong to other sectors in its portfolio owing to the nature of most stocks that make up the index.

A soft robust methodology to devise hedging strategies in renewable energy trading based on electricity options

Supply contracts play an important role in competitive electricity markets. They help ensure supply adequacy and hedge against market power, among other benefits. However, when variable renewable energy is involved, the mixture of uncertainty in energy production with a high volatile spot market poses a risk difficult to manage. Within this context, call/put options are suitable hedging instruments to cover production imbalance with respect to supply contracts. To efficiently balance the cost/benefit of a set of energy options in a portfolio composed by renewable sources short in supply contracts, a precise definition of the probabilistic nature of the spot market price should be available. However, it is recognized that in practice agents only have an imprecise approximation of the “true” underlying process. Thus, decisions are made under ambiguity, which challenges the practical application of standard pure stochastic-programming methods for the optimal composition of electricity contracts. In this work, we propose a risk- and ambiguity-constrained portfolio allocation model that defines the optimal composition of call/put options and renewable sources to back a supply contract. A case study with realistic data from the Brazilian electricity market is presented to illustrate the applicability of the proposed portfolio allocation methodology. Comparing with two benchmark, we found an increase of roughly 20% and 42% on the average revenue and at least 40% on the average of the 5% worst-case revenue scenarios for different spot price probability distributions.

Research and Development of a Unified Methodology for Assessing the Resource Efficiency of International Digital Platform Promotion for E-Learning

Ideas related to the systematization of educational technologies are of great interest. This is due, among other things, to the principles of symmetry. On the basis of their application, the synthesis of well-established facts and principles in international education will be realized. Practical needs that are directed towards the future will also be realized. This paper is devoted to the study and creation of a methodology that makes it possible to evaluate the resource efficiency of an interethnic digital platform. The developed methodology of resource allocation for the promotion of digital educational programs is unified and can be applied in any region or country. Considering the specific legislative acts on the financial support of higher education and the need for professionals in certain areas, this method allows you to give a qualitative assessment and determine the necessary resources for the development and promotion of an interethnic digital educational platform, identifying the most appropriate areas of education depending on the region or country.

On capital allocation for a risk measure derived from ruin theory

This paper addresses allocation methodologies for a risk measure inherited from ruin theory. Specifically, we consider a dynamic value-at-risk (VaR) measure defined as the smallest initial capital needed to ensure that the ultimate ruin probability is less than a given threshold. We introduce an intuitively appealing, novel allocation method, with a focus on its application to capital reserves which are determined through the dynamic VaR measure. Various desirable properties of the presented approach are derived including a limit result when considering a large time horizon and the comparison with the frequently used gradient allocation method. In passing, we introduce a second allocation method and discuss its relation to the other allocation approaches. A number of examples illustrate the applicability and performance of the allocation approaches.

Design and implementation of a fault-tolerant controller using control allocation techniques in the presence of actuators saturation for a VTOL octorotor

AbstractFault-tolerant control systems are vital in many industrial systems. Actuator redundancy is employed in advanced control strategies to increase system maneuverability, flexibility, safety, and fault tolerability. In this paper, a fault-tolerant control scheme is proposed to make an over-actuated octorotor robust, against actuators fault and saturation. A sliding mode observer is employed to determine the actuators condition. Then, a fault-tolerant control based on the control allocation methodology is proposed to distribute the control signals between the actuators by considering their condition. In a nonlinear system, an actuator fault can lead to the saturation of other actuators and steady-state errors that can cause closed-loop instability. Hence, the proposed control scheme corrects the actuator signals in a way that their limitations are considered. Finally, experimental studies are carried out and a comparison study is provided.

Power Allocation for Reliable and Energy-Efficient Optical LEO-to-Ground Downlinks with Hybrid ARQ Schemes

Satellites in low earth orbit (LEO) are currently being deployed for numerous communication, positioning, space and Earth-imaging missions. To provide higher data rates in direct-to-user links and earth observation downlinks, the free-space optics technology can be employed for LEO-to-ground downlinks. Moreover, the hybrid automatic repeat request (HARQ) can be adopted since the propagation latency is low for LEO satellites. In this work, a power allocation methodology is proposed for optical LEO-to-ground downlinks under weak turbulence employing HARQ retransmission schemes. Specifically, the average power consumption is minimized given a maximum transmitted power constraint and a target outage probability threshold to ensure energy efficiency and reliability, respectively. The optimization problem is formulated as a constrained nonlinear programming problem and solved for Type I HARQ, chase combining (CC) and incremental redundancy (IR) schemes. The solutions are derived numerically via iterative algorithms, namely interior-point (IP) and sequential quadratic programming (SQP), and validated through an exhaustive (brute-force) search. The numerical simulations provide insight into the performance of the retransmission schemes regarding average power. More specifically, Type I HARQ has the worst output, CC has a moderate one, and IR exhibits the best performance. Finally, the IP algorithm is a slower but more accurate solver, and SQP is faster but slightly less accurate.

Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system

Recently, many concepts in technology has been changed. According to the digital transformation trends, Internet of Things (IoT) represents an interested research issue. As the IoT grows, the data and the processes will need more space. The data in cases like healthcare, smart cities, autonomous vehicles, smart agriculture, etc. needs to be analyzed and processed in real-time. Cisco refers to the dependence of edge and cloud as “The Fog”. The data can be analyzed at the fog layer to maximize data utilization. This paper presents a new Effective Prediction and Resource Allocation Methodology (EPRAM) for Fog environment, which is suitable for Healthcare applications. Resource Allocation (RA) represents a hard mission as it involves a set of various resources and fog nodes to achieve the required computations for IoT systems. EPRAM tries to achieve effective resource management in Fog environment via real-time resource allocating as well as prediction algorithm. EPRAM is composed of three main modules, namely: (i) Data Preprocessing Module (DPM), (ii) Resource Allocation Module (RAM) and (ii) Effective Prediction Module (EPM). The EPM uses the PNN to predict a target field, using one or more predictors. In order to detect the probability of the heart attack, PNN is trained using the training dataset. Then PNN will be tested using the user’s sensing data coming from the IoT layer to predict the probability of heart attack and then take the most appropriate action accordingly. The main goal of the system is to achieve a low latency while improving the Quality of Service (QoS) metrics such as (the allocation cost, the response time, bandwidth efficiency and energy consumption). Unlike other RA techniques, EPRAM employs deep Reinforcement Learning (RL) algorithm in a new manner. It also uses the PNN for the prediction algorithm. It has achieved such acceptable performance due to using deep RL and PNN. Deep RL has shown impressive promises in resource allocation. PNN generates accurate predicted target and is much faster than multilayer perceptron networks. Comparing the EPRAM with the state-of-the-art algorithms, EPRAM achieved the minimum Makespan as compared to previous LB algorithms, while maximizing the Average Resource Utilization (ARU) and the Load Balancing Level (LBL). Accordingly, EPRAM is a suitable algorithm in the case of real-time systems in FC which leads to load balancing. ERAM is effective in monitoring and predicting the status of the patient accurately and quickly.

A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.

Power system operation enhancement using a new hybrid methodology for optimal allocation of FACTS devices

The proposed research article introduces a new hybrid heuristic algorithm based on Autonomous Groups Particle Swarm and Grey Wolf optimizers (AGPSO-GWO) via multi-objective function for the allocation of the Flexible Alternating Current Transmission Systems (FACTS) controllers in power grids to minimize the active power system losses, voltage deviation, and the system operational costs. In this research work, the Static Var Compensator (SVC), Thyristor-Controlled Series Compensator (TCSC), and Unified power flow controller (UPFC) are used as FACTS controllers. A comparative analysis is conducted with other proposed heuristic optimization algorithms such as Particle Swarm Optimization (PSO), Autonomous Group Particle Swarm Optimization (AGPSO) Grey Wolf optimizer (GWO), and improved grey wolf optimization (IGWO) algorithms to confirm and validate the superiority of the proposed technique. Moreover, the proposed AGPSO-GWO has been compared with the previous existing Simulated Annealing (SA) in the literature to prove its superiority and ensure its validity. The proposed scheme has been validated and applied on 30-bus and 118-bus IEEE electric power systems. The numerical results had been carried out using MATLAB and MATPOWER packages. The simulation results show that the proposed algorithm offers the best performance among all algorithms to achieve the optimum global minimum solutions with the highest convergence rate.

Optimal Capacitor Placement for Unbalanced Distribution System using Graph Theory

In this paper, a graph theory-based allocation methodology of capacitor bank is proposed for unbalanced distribution system. A single-stage optimization procedure using electrical centrality measure concept to identify the best suitable locations for the multi-point connection of capacitor is described with its significance in this paper. The electrical centrality measure approach gives ranking of all the buses, single capacitor or multiple capacitors can be placed in unbalanced distribution system (UDS) using these indices to fulfill the reactive power demand and maintain voltage profile of the system. To evaluate the performance of the UDS due to capacitor bank connection two parameters are considered – improvement of overall voltage profile and reduction of active power loss. The contribution of the paper is – first, electrical centrality measure is used for identifying the capacitor bank location in the unbalanced distribution system and second it optimizes the size of the capacitor using two different objectives to improve the system voltage profile and reduce the active power loss. The proposed methodology is tested on a modified 123 bus unbalanced distribution system. The active power loss reduction up to 16.50% and total voltage deviation reduction up to 71.53% were achieved using the proposed method. The obtained results corresponding to the two objectives are also compared.

Evolution of Public Health Expenditure Financed by the Romanian Social Health Insurance Scheme From 1999 to 2019.

The Romanian health system is mainly public financed (80.45%) through the following sources: Social Health Insurance (65%), State and Local Authorities Budget (15.45%), while the private sources (voluntary health insurance and out of pocket) adds an additional 19.55% to the public funds. The shares of the types of expenditure reflect the importance of each sector in the overall health system, and trends in expenditure show the impact of financing on the health sector's structural changes. We analyzed the 20-year trend of the Social Health Insurance budget, from 1999 to 2019. The influences of the different allocations, subcategories, and new budget categories appearing over time were adjusted to reveal relevant trends. Of the 14 medical service categories and the stand-alone Administrative expenditure category, six expenditure categories including Hospital services, Total drugs, and Primary care showed stationary 20-year trends; five including Medical devices, Dialysis, and Homecare services showed ascendant trends; and four including Dentistry and Emergency services showed descendant trends. Stationary trends imply no structural changes in the health sector of relevant magnitude to impact the financing shares of major categories: hospitals, drugs, or primary care. Emerging trends related to the impact of different reforms were revealed only in the low share of expenditures categories. The allocation methodology and statistical analysis of the trends reveal a new perspective on the evolution of health sector in Romania.

Reliability assessment of distribution system with the integration of photovoltaic and energy storage systems

With the current focus on energy and the environment, efficient integration of renewable energies, especially solar energy into power systems, is becoming indispensable. Moreover, to fully capture solar potentials and to recognize the unique characteristics associated with solar energy in power systems reliability assessment, a profound investigation is needed. Accordingly, this paper attempts to establish a comprehensive analytical approach for modeling the reliability of a hybrid system (Photovoltaic (PV) system with Energy Storage System (ESS)). To this end, the output of the PV system is modeled by a multi-state model, and the ESS system is modeled as a two-state Markov model. Moreover, the failure of the hybrid system’s components is considered by using the Failure Modes and Effects Analysis (FMEA) method. It then continues with the integration of the PV and ESS system model and the model associated with the failure of their components, thereby allowing the hybrid system to be represented as a multi-state model in analytical studies. Also, the conditional probability approach is used and related indices have been extracted. Finally, a discussion of a methodology for optimal hybrid system allocation and sizing in distribution systems, to minimize the electrical network losses and to guarantee acceptable reliability level and voltage profile is presented. The results obtained from the implementation of the case study, validate the major features of the proposed analytical model along with optimal placement of the hybrid system more efficiently.

Deriving global carbon budgets for the Swiss built environment

In order to limit global warming, remaining carbon budgets have been defined by the IPCC in 2018. In this context translating global goals to local realities implicates a set of different challenges. Standardized methodologies of allocation can support a target-cascading process. On the other hand, local strategies and norms are not currently designed to directly respond to limited carbon budgets in a 2050 horizon. The life cycle assessment of buildings implicates an intricate cross-industry and cross-border carbon accounting. For these reasons, effective and aligned carbon targets are needed to support and guide all actors in the construction sector. This research aims at addressing these challenges by developing a new methodology of allocation of a global carbon budget at different scales using the Swiss built environment as a case study. This approach allows the assessment of current best practices in regards to limited carbon budgets. Results show misalignment of global goals with current practices at all levels and present the magnitude of effort that would be required to have a chance to limit global warming to 1.5°C.