Source Allocation Research Articles

Peningkatan Ekonomi Kreatif dalam Pemanfaatan BUMDes Melalui Pelatihan Penyusunan Laporan Keuangan Sederhana di Desa Cinta Mulya Kecamatan Sidomulyo, Lampung Selatan

The purpose of this service activity is to help village officials, in this case BUMDes, to study government regulations regarding the budget and the use of village fund funding allocations. So that village officials understand government rules regarding the allocation of use and sources of funds to be used by the village. And this activity also helps MSMEs to learn to promote their business services/products through marketing content with attractive designs and using web content as their promotional media. It is hoped that this activity will be a positive contribution to Cinta Mulya village, especially village officials and MSME actors in carrying out their functions and roles in managing internal and state-owned finance.

Lower and upper bounds for scheduling energy-consuming tasks with storage resources and piecewise linear costs

This paper considers the problem of scheduling a set of time- and energy-constrained preemptive tasks on a discrete time horizon. At each time period, the total energy required by the tasks that are in process can be provided by two energy sources: a reversible one and a non-reversible one. The non-reversible energy source can provide an unlimited amount of energy for a given period but at the expense of a time-dependent piecewise linear cost. The reversible energy source is a storage resource. The goal is to schedule each task preemptively inside its time window and to dispatch the required energy to the sources at each time period, while satisfying the reversible source capacity constraints and minimizing the total cost. We propose a mixed integer linear program of pseudo-polynomial size to solve this NP-hard problem. Acknowledging the limits of this model for problem instances of modest size, we propose an iterative decomposition matheuristic to compute an upper bound. The method relies on an efficient branch-and-price method or on a local search procedure to solve the scheduling problem without storage. The energy source allocation problem for a fixed schedule can in turn be solved efficiently by dynamic programming as a particular lot-sizing problem. We also propose a lower bound obtained by solving the linear programming relaxation of a new extended formulation by column generation. Experimental results show the quality of the bounds compared to the ones obtained using mixed integer linear program.

Factors Related to the Length of Stay for Patients With Schizophrenia: A Retrospective Study.

BackgroundThe length of stay (LOS) of patients with schizophrenia has been a wide concern of researchers. Reasonable management of the LOS to achieve a balance between quality of treatment and efficient medical source allocation has become a significant issue in clinical work in psychiatry. Figuring out the factors related to the LOS of schizophrenia patients can help optimize its management by the hospital.MethodThe essential information of patients was obtained from the electronic medical record system. The variables were divided into the following kinds: demographic, clinical, and biochemical. Univariate analysis and multivariate analysis were conducted to find the potential factors related to the LOS of schizophrenia patients. Receiver operating characteristic analyses were conducted to evaluate the accuracy of judging the LOS of the regression model.ResultA total of 1,160 patients with schizophrenia were enrolled in our research. Our results demonstrated that the status of unmarried (single, separated, divorced, or widowed) and the abnormality of thyroid-stimulating hormone (TSH) were risk factors for the longer LOS of schizophrenia patients. The area under the curve was 0.576, which meant that the regression model had a certain predictive value.ConclusionTo our knowledge, this research is the first study to analyze the effect of various factors, including the biochemical index, on the LOS of a single type of mental disorder. Marital status and TSH were proven to be related to the LOS of schizophrenia patients. The results of this study provided reference factors of LOS for clinical psychiatry, which will be helpful to the management of hospitalization and in optimizing the allocation of medical sources.

Characterization of Nitrated Aromatic Compounds in Fine Particles from Nanjing, China: Optical Properties, Source Allocation, and Evolutionary Processes

Characterization of Nitrated Aromatic Compounds in Fine Particles from Nanjing, China: Optical Properties, Source Allocation, and Evolutionary Processes

Renewable energy source allocation in electrical distribution system using water cycle algorithm

Achieving maximum potential benefits through appropriate allocation of distributed generation (DG) in electrical distribution systems is yet a challenging issue. Since the inappropriate allocation of DG adversely affects the distribution system. The present paper introduces a novel metaheuristic optimization method for solving the allocation of renewable energy sources (RES) in a practical distribution system. The algorithm has been effectively implemented on a practical Indian 28 – bus distribution system to allocate wind turbines (WT) and solar photovoltaic (SPV) arrays to achieve technical benefits of power loss minimization and bus voltage profile improvement. The simulation results obtained by the present method are compared with existing literature and it is found to be better.

An Energy-Efficiency Multi-Relay Selection and Power Allocation Based on Deep Neural Network for Amplify-and-Forward Cooperative Transmission

In this letter, an energy-efficiency (EE) resource allocation strategy is investigated for the amplify-and-forward (AF) protocol used to forward data to the destination. First, we formulate an EE optimization problem of multi-relay AF system, which correlates the power of source with the power of each relay. Then, a deep neural network (DNN)-based model is constructed to implement the joint power allocation of source and multiple relays. In particular, we adopt the modified rectified linear unit (ReLU) as the activation function in the output layer of the DNN model to hinder relays’ negative output power weightings, thereby to simultaneously realize the selection of relays and their power allocation. The simulation results show that our scheme enables better performance in term of the EE of system compared with the conventional strategies and convolutional neural network (CNN).

Benders decomposition approach to solve a Bi-objective cell formation in dynamic conditions

Cellular Manufacturing System (CMS) is an alternative to production systems based on process and product layout that combines the advantage of high throughput rate of flow shop system with the flexibility of job shop layout. CMS design has four steps: 1) Cell formation: grouping the parts with similar geometric design features and processing requirements in part families to use similarities to build, manufacture and assemble machines in machine lines and cells, 2) group layout: placing machines in each cell that includes intra-cell and inter-cell layout, 3) group scheduling: part family scheduling, and 4) source allocation: allocation of tools and primary resources and human force. Dynamic CMS (DCMS) can be divided into smaller periods, each of which is different from the previous one as these periods have various demands and compositions. The study presented a mixed-integer programming model for DCMS. The model considered some significant real-world conditions. In this model, the first objective function tries to minimize the costs related to the machines purchase cost, machines variable cost, the cost of movement workers, the cost of cell formation, the cost of inter-cell and intra-cell part movement, the cost of overtime, and the cost of reconfiguration.

Evaluation of heavy metal contaminants in prepared noodles: source allocation and health risk assessment.

In recent years, special attention has been given to emission research that led to the deposition of toxicants from road traffic. Thus, it is imperative to focus on heavy metal (HM) stressors in food items, their source contribution, and health risk assessment providing insight into their spatial role at the population level. In this study, heavy metal in the street vended noodles was studied while correlating the quality of noodle with different environmental origins. The samples were prepared using acid digestion and analysed by flame atomic absorption spectrophotometer, except Hg which was analysed by direct mercury analyser. The results showed that some heavy metals like Cr, Pb, Mn, Cd, and Hg exceed their permissible limits established by the international legislation for food products. In the noodle samples, the concentration of heavy metal ranged from < 0.1 to 0.904mg/kg for Pb, < 0.09 to 0.843mg/kg for Ni, < 0.004 to 0.201mg/kg for Cd, < 0.0001 to 0.004mg/kg for Hg, < 0.01 to 1.388mg/kg for Cu, < 0.015 to 8.049mg/kg for Mn, and < 0.02 to 16.514mg/kg for Cr. Noodle samples vended on high traffic density streets are directly associated with increased HM content due to atmospheric deposition from the surrounding. Source apportionment study determines that HM contamination belongs to the same source of origin, except Cr. Based on the cluster analysis, these samples fall into three major groups that were further validated by the canonical discriminant function. Health risk prediction by Monte Carlo simulation revealed an elevated non-carcinogenic health hazard risk to consumers with a hazard index (HI) shift from 71 to 75%. Health hazard analysis showed that consumers of high traffic density street vended food are at higher risk of developing health-related issues. This study is important to evaluate the health risk of the population exposed to heavy metals due to ingestion of street vended food.

Systematic Approach for Synthesizing Carbon–Hydrogen–Oxygen Networks Involving Detailed Process Simulations

Carbon–hydrogen–oxygen symbiotic networks (CHOSYNs) provide a systematic multiscale framework for integrating multiple hydrocarbon processing plants. Earlier approaches have focused on high-level performance benchmarking using atomic and stoichiometric targeting techniques, which are subsequently detailed to identify the implementation strategies for allocation and processing. These sequential approaches are very powerful for the cases involving benchmarks that are independent of the system details (e.g., minimum usage of raw materials, minimum discharge of wastes, maximum revenue). For objectives involving detailed process information (e.g., capital cost minimization), there is a need to include the system details early enough in optimization. This paper proposes a systematic approach for incorporating detailed process simulations throughout the various stages of synthesizing a CHOSYN. The proposed approach involves the use of computer-aided process simulators and simultaneous optimization for synthesizing a CHOSYN. Critical data are generated and used at each design stage. For existing facilities, targeting uses the simulated data on available resources and sinks (e.g., flow rate, composition, pressure, and temperature). Process synthesis is used to generate a set of candidate new units and plants to be added, while detailed simulation is used to size these plants and identify the specific needs for flows and compositions. The proposed approach is implemented for a case study that involves five existing plants and a set of seven candidate plants that can induce symbiosis. The results show final configurations with the selection of new plants, the reduction of raw material, the allocation of internal and external sources, and the details of process information, capital and operating costs, and flow rate, pressure, and temperature of the exchangeable streams across the network.

Optimal allocation of renewable energy source and charging station for PHEVs

Renewable energy forms have a comparatively less impact on the environment when compared with non-renewable sources. Renewable energies like wind power, biomass, hydropower, solar power, and geothermal energies are preferred for production as they do not run out quickly. Owing to the stochastic nature of renewable energy sources (RES) and Plug-in hybrid electric vehicles (PHEVs) load demand, large-scale penetration of these resources from the power system affects the network performance like reduce power quality, increase power loss, and voltage deviation. These issues can be addressed by the optimum planning depending upon the variables outcome in RES to satisfy the extra demand affected by PHEV charging. At the same time, designing a proper and effective energy management strategy in PHEV can be considered an optimization problem and can be resolved by the use of metaheuristic algorithms. With this motivation, this paper presents a novel deep learning with metaheuristic optimization based allocation of RES and charging stations for PHEVs. The proposed model depends upon model predictive control (MPC) where the actual battery state of charge (SOC) is concerned with real time energy management. Moreover, the black widow optimization (BWO) algorithm is used for the optimal allocation of RES and charging stations. The BWO algorithm derives objective functions using different parameters of the charging stations. In addition, based on the MPC model, deep stacked autoencoder (DSAE) is applied for the prediction of near-future velocity. The experimental results stated that the presented model has resulted in improved efficiency of the proposed model over the other methods.

An optimisation approach for spatial allocation of energy sources to district heating networks

District heating networks (DHN) combined with low-carbon heat sources are a promising way to reduce greenhouse gas emissions from heating. However, few works have addressed the problem of allocating localised thermal energy supplies to DHN heating demands considering the spatial proximity constraint of transporting heat energy. The work improves an existing spatiotemporal analysis method by introducing an adapted form of Hitchcock transportation problem and linear programming to solve the optimal allocation problem in network of supplies and demands. The new method is compared with the original method and is found to improve the accuracy of estimating the allocable industrial excess heat supply in a Swiss case study. The method could be applied to diverse thermal sources, such as industrial excess heat, geothermal, lakes and rivers, etc.

Reactive Power Planning and Total Transfer Capability Enhancement using Facts and Capacitor Banks. (Dept. E)

In view of the continuous annual increase in demand, reactive power planning (RPP) is considered one of the most significant problems to address a major challenge of the secure power system operation. In this paper, a multi-objective genetic algorithm (MOGA) for RPP is proposed, with the goals of cost minimization of power losses, new reactive power (VAR) sources, and maximizing the Total Transfer Capacity (TTC). Different optimization factors are taken into account, including generator voltages, transformer tap changers, and various operating constraints. A fuzzy min-max approach is used to identify the optimum compromise option. Studies are being conducted to compare capacitor banks, flexible ac transmission systems (FACTS), or both as a new VAR support source to improve the system performance. Moreover, the optimal allocations of switchable VAR sources are not determined in advance; instead, they are treated as control variables to improve the techno-economic operation of the network. The effectiveness of the proposed algorithm is examined on the IEEE 30-bus test system where felicitous results have been acquired. From the results, the total annual cost is decreased from 3.671×106 $ before adding new VAR sources to a range between 2.02×106 and 2.486×106 $ depending on the selected type of VAR source. While the transfer capacity is increased from 458.37MW to a range between 483.084 and 539.055 MW.

Sources Allocation on Risk Performance of Egyptian Insurance Companies

The main objective of current paper was to investigate the relationship between sources allocations measured by (external sources to total assets- free investment and allocated investment to total assets) on risk performance measured by financial risk (standard deviation of return on equity) business risk (standard deviation of return on assets) using ARDL and GARCH model, using a sample of 19 Egyptian insurance companies over a 21 year period form 1999 – 2019, the findings indicate that There is a significant negative linear relationships between the independent variable in terms of capital structure and assets structure on dependent variable standard deviation of return on equity (Y1) at a Significant level less than (0.05). Also there is a significant positive linear relationship between the independent variable in terms of assets structure and dependent variable standard deviation of return on assets (Y2) at a significant level less than (0.001). with regard to ARCH and GARCH result shows the There is a significant positive effect of the ARCH term, measured as the lag of the squared residual from the mean equation, and the GARCH term, Last period’s forecast variance, at a significant level less than (0.05). It means that the high volatility in the conditional variance of standard deviation of return on assets y2. also The sum of the two parameters of: (α+β) in the GARCH model (1, 1) whether the random error are distributed according to normal distribution approaches the positive one, which indicates that the two conditions of non-negative variance, and the variance is not inflated are satisfied, and that Indication of the continuity of volatility shocks in the standard deviation of return on assets (Y2).

Joint Optimal Allocation of Electric Vehicle Charging Stations and Renewable Energy Sources Including CO2 Emissions

In the coming years, several transformations in the transport sector are expected, associated with the increase in electric vehicles (EVs). These changes directly impact electrical distribution systems (EDSs), introducing new challenges in their planning and operation. One way to assist in the desired integration of this technology is to allocate EV charging stations (EVCSs). Efforts have been made towards the development of EVCSs, with the ability to recharge the vehicle at a similar time than conventional vehicle filling stations. Besides, EVs can bring environmental benefits by reducing greenhouse gas emissions. However, depending on the energy matrix of the country in which the EVs fleet circulates, there may be indirect emissions of polluting gases. Therefore, the development of this technology must be combined with the growth of renewable generation. Thus, this proposal aims to develop a mathematical model that includes EVs integration in the distribution system. To this end, a mixed-integer linear programming (MILP) model is proposed to solve the allocation problem of EVCSs including renewable energy sources. The model addresses the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. Moreover, an EV charging forecast method is proposed, subject to the uncertainties related to the driver's behavior, the energy required by these vehicles, and the state of charge of the EVs. The proposed model was implemented in the AMPL modelling language and solved via the commercial solver CPLEX. Tests with a 24-node system allow evaluating the proposed method application.

Regional differences and threshold characters of the impact of energy use efficiency on energy scarcity in China

The improvement of the technical efficiency of energy use has been widely seen as an important way to alleviate energy scarcity. However, the energy rebound effect demonstrates the vulnerability and unsustainability of alleviating energy scarcity relying on technology. Under the background of the consistent development of energy factor market, it is of great importance to explore the paths and differences of energy source allocation constrained by technology and cost to promote the sustainable use of energy. This study analyzed the regional differences and threshold characteristics in the impact of the technical and cost efficiency of energy use on energy scarcity using the instrumental variable-two-stage least square method, based on the panel data of Chinese provinces from 2003 to 2017. The results showed that: ① Technology and price were the critical forces of the technical and cost efficiency of energy use. They changed the degree of use of surplus production factors by affecting the complementary and replacement demand relationship between energy and non-energy factors. Meanwhile, the impact of energy use efficiency on energy scarcity showed regional and time features due to the different technology capabilities and different levels of market development in different regions and different time. ② The alleviation of energy scarcity at the national level relied on the improvement of energy cost efficiency. At the regional level, the alleviation of energy scarcity in central China came from the improvement of the technical efficiency of energy use. However, the alleviation of energy scarcity in the eastern and western regions was due to the improvement of energy cost efficiency. ③ Constrained by the economic development, openness, science and technology input and the industrial development structure, the impact of the technical and cost efficiency of energy use on energy scarcity demonstrated a nonlinear feature. And structural mutation occurred after passing a specific ‘inflection point’, which showed a significant threshold feature. Therefore, energy price should play an important role in energy use and it is necessary to pay attention to the regional differences and time characteristics of energy economic development to promote the sustainable development of energy resources.

From drag to brag: The role of government grants in enhancing built heritage protection efforts in New Zealand's provincial regions

The protection of heritage buildings is essential because they signpost the narration of a community's past into the future. Investment in the protection of heritage buildings through incentives in the form of government grants provides a lifeline for the future preservation of built heritage, which can boost an urban area's local economy. This study sought to evaluate the distribution of heritage buildings in New Zealand, examine the allocation of significant government funding sources for protecting the heritage buildings, and explore the implications of this allocation on future built heritage protection efforts in New Zealand's provincial regions.An analysis of existing relevant documents revealed that while the per capita distribution of heritage buildings was highest in New Zealand's provincial regions, major urban centres received the highest share of government funding. Findings from key informant interviews identified three major themes as implications of the current incentive allocation on built heritage protection efforts in New Zealand's provincial regions: (i) disproportionately low allocation of government grants to provincial regions; (ii) lack of sophistication among property investors in provincial regions; and (iii) “emergency solution” mindset of government funding regulators.These findings imply that though government heritage grant systems are the most extensive non-regulatory incentives for the protection of built heritage in New Zealand, the provincial regions currently struggling with so many underutilised and derelict historical buildings may not be able to keep up with their preservation efforts if they continue to receive a lesser proportion of the available grant. As a recommendation, the central government can assist by directing more discretionary grants to provincial regions to encourage future efforts towards protecting their heritage buildings.

Organic Nutrient Source Allocation and Use in Smallholder Farming Communities: What Are We Missing?

Organic nutrient sources (ONS) are managed as a key resource by smallholder farmers to maintain the productivity of soils. Recycling of ONS by applying them to soils is a globally dominant strategy of ecological nutrient management. Understanding how ONS produced on-farm are allocated and what drives farmer decision making around their use is critical for sustainable nutrient management in smallholder agroecosystems. Using focus group discussions and a survey of 184 farming households, we studied socio-economic, socio-cultural, and environmental drivers of ONS allocation and use at the farm scale in three contrasting agroecological zones of western Kenya. Farm typologies of ONS management were also developed using cluster analysis based on resource endowment and the connectedness of farmers, management norms, and interaction with extension. Our findings suggest that the more resource endowed a farmer is, the more ONS are allocated to the main plot within the farm. We also observed that farmers preferred allocating more resources to plots that were considered more fertile. Land tenure had an important influence, in that main plots not owned by farmers were more likely to retain ONS such as crop residues. Management of residues is dependent on farmer gender, for instance, female farmers tended to burn legume residues in particular, which is notable since these higher quality residues are often considered key to sustainable soil nutrient management. Farm typologies featured different allocation patterns of ONS and were associated with resource endowment and farmer networks, including external ties to extension agents and internal ties to other farmers. Finally, there was a strong overarching influence of agroecological zone that often escapes characterization on the allocation of ONS. As research and development organizations continue to engage with smallholder farmers to reduce the burden of global food insecurity, the insights gained by this research will allow better anticipation of drivers and obstacles to improved nutrient management in these farming landscapes and communities.

Effects of inhomogeneous ground-level pollutant sources under different wind directions

Effects of source inhomogeneity on pollutant dispersion from a cubic building array are investigated as a function of the external wind direction. Using building-resolving large-eddy simulation, it is found that the results depend strongly on the source location and source uniformity inside a near-field region defined by a radius of homogenisation (RAD) based on the spatial autocorrelation of the pollutant concentration. The sensitivity of the RAD to the source location changes abruptly around 30° and is greatly reduced for wind angles between 30 and 45°, in agreement with velocity statistics and the mean horizontal streamlines. The optimal source allocation, which is a proxy for emissions from time-dependent traffic, also changes around 30°. This work clarifies the relationship between inhomogeneous velocity and pollutant statistics and may be applied to the formulation of traffic control policy.

Comparison of source apportionment approaches and analysis of non-linearity in a real case model application

Abstract. The response of particulate matter (PM) concentrations to emission reductions was analysed by assessing the results obtained with two different source apportionment approaches. The brute force (BF) method source impacts, computed at various emission reduction levels using two chemical transport models (CAMx and FARM), were compared with the contributions obtained with the tagged species (TS) approach (CAMx with the PSAT module). The study focused on the main sources of secondary inorganic aerosol precursors in the Po Valley (northern Italy): agriculture, road transport, industry and residential combustion. The interaction terms between different sources obtained from a factor decomposition analysis were used as indicators of non-linear PM10 concentration responses to individual source emission reductions. Moreover, such interaction terms were analysed in light of the free ammonia / total nitrate gas ratio to determine the relationships between the chemical regime and the non-linearity at selected sites. The impacts of the different sources were not proportional to the emission reductions, and such non-linearity was most relevant for 100 % emission reduction levels compared with smaller reduction levels (50 % and 20 %). Such differences between emission reduction levels were connected to the extent to which they modify the chemical regime in the base case. Non-linearity was mainly associated with agriculture and the interaction of this source with road transport and, to a lesser extent, with industry. Actually, the mass concentrations of PM10 allocated to agriculture by the TS and BF approaches were significantly different when a 100 % emission reduction was applied. However, in many situations the non-linearity in PM10 annual average source allocation was negligible, and the TS and BF approaches provided comparable results. PM mass concentrations attributed to the same sources by TS and BF were highly comparable in terms of spatial patterns and quantification of the source allocation for industry, transport and residential combustion. The conclusions obtained in this study for PM10 are also applicable to PM2.5.

Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.