Proper Allocation Research Articles

Optimal Allocation of BESS in Distribution Network Based on Improved Equilibrium Optimizer

The battery energy storage system (BESS) can accommodate the uncertainties of renewable energy sources (RESs) and load demand. Proper allocation of the BESS in the distribution network (DN) can bring cost-effectiveness and enhance system stability. To realize the reliable and economic operation of BESS in the DN, a multi-objective optimization model for optimal BESS allocation is established, which aims at minimizing the annual overall cost of the whole system, including life cycle cost (LCC), power loss cost, peak-shaving cost, tie-line fluctuation penalty, and voltage deviation penalty. Then, a novel implementation of the improved equilibrium optimizer (IEO) algorithm is proposed to solve the optimal BESS allocation scheme. In order to verify the effectiveness of the proposed method, the simulation experiment based on the IEEE 33-bus test system is performed. Simulation results prove that the IEO algorithm is capable of rapid stable convergence and efficient searching for optimum in the multidimensional space. By the end of the iteration, the annual overall cost of the whole system records a minimum value of $1.8692e+06 every year. A meticulous techno-economic analysis demonstrates that the obtained BESS allocation scheme not only effectively ensures cost-effectiveness of BESS but also significantly reduces power loss, load peak-valley difference, tie-line power fluctuation, and voltage deviation.

What should be offered for free and what for premium in a freemium service? a two-stage approach of Kano & path analysis to the design of freemium services

ABSTRACT The freemium model, a business model that provides basic functions for free and makes a profit by providing advanced functions at a premium, has been widely adopted in various industries. To acquire enough customers and generate revenues from the freemium model, the proper allocation of free and premium functions is essential, but little research has been done in this direction. This study proposes a two-stage approach that combines the Kano model and the technology acceptance model (TAM) as a conceptual design method of freemium services. First, Kano analysis is performed to divide the functions of services into three types, must-be, one-dimensional, and attractive functions. The must-be functions are regarded as free functions, and low and high-level one-dimensional functions are classified as free and premium functions, respectively. Then, we perform path analysis based on TAM to identify additional premium functions that affect willingness to pay. We allocate free and premium functions of personal cloud service using the proposed approach and compare the results with the current functions of Dropbox, One Drive, and Google Drive. The proposed two-stage approach is expected to be practically used for designing service concepts based on the freemium business model in various industries.

CRIMES AGAINST WILD ANIMALS IN THE WEST OF SANTA CATARINA, BRAZIL: AN ANALYSIS FROM ASSESSMENTS AND SEIZURES CARRIED OUT BY THE ENVIRONMENTAL MILITARY POLICE OF CHAPECÓ

Several reports carried out by public agencies are related to conducts that altogether may constitute animal trafficking, which, along with other anthropic practices, contribute to the process of decline in animal species. Our objective was to collect data on environmental crimes related to fauna in the area of jurisdiction of the Environmental Military Police of Chapecó (EMPC), in the state of Santa Catarina_Brazil. For that, a documentary survey was carried out in the archives of that organ from January/1999 to December/2017. Environmental crimes related to fauna comprised 196 complaints, with 56 of them resulting in assessments, and 1,016 animals were seized in 33 municipalities. Chapecó was the municipality with the largest number of cases (97 assessments/539 animals seized), but the per capita values are below of 56.2% of the surveyed municipalities. Out of the total number of animals seized, 980 were birds, 28 were mammals, six were lizards and two were turtles. Most of the animals seized were released into nature (752). It is likely that the data do not represent the actual amount of wild animals kept in captivity or hunted in the region because of the difficulty of the inspection and/or structure for proper allocation of the animals.

Multi Agent System Based Risk Allocation Model for Public-Private-Partnership Type Projects (RAMP3)

The performance of the Public Private Partnership (PPP) projects depends on the efficiency of the risk allocation strategies between the public and private parties. Therefore, a multi agent system-based Risk Allocation Model for PPP projects (RAMP3) was developed to determine the proper risk allocation decisions between the public and private parties within the study. The methodology of RAMP3 involves i) identification of risks by agents, ii) assessment of each risk’s importance and impact, iii) communication of agents to negotiate on risk allocation decision and iv) determination of strategies and utility functions to be used in negotiation process. Focus of the study is presenting the steps of negotiation process of agents using economic theory and Zeuthen bargaining strategy. RAMP3 was validated on two real PPP projects and results show that the higher risk value of an agent gets, agent’s utility due to counter agent in that concession round lowers. Preliminary findings also show that risk is allocated to the party that has a higher risk acceptability in negotiation process. The RAMP3 will enable project parties to determine the appropriate risk allocation strategies by considering the effects of emerging risks in terms of time delay, cost overrun and conflict and provide contract success. The model can also be used as a decision support system by public partner for performing an efficient and accurate risk allocation.

Dynamic Pricing for Intelligent Transportation System in the 6G Unlicensed Band

The use of an unlicensed band has significantly boosted the capacity of cellular technology via LTE in unlicensed band, license assisted access, and new radio in unlicensed band. Likewise, cellular vehicle to everything in the shared band is also gaining momentum for intelligent transportation systems. Nevertheless, the cellular operator has to wisely decide the proper allocation of this unlicensed band as well as its licensed band, to its users. As the cellular operator cannot guarantee the quality of service in the unlicensed band, motivating the users to offload into the unlicensed band is one of the challenging tasks for the operator. In this paper, we propose an economical approach to encourage users to offload in the unlicensed band while maximizing the utility function for the users and revenue for the operator. Under the proposed scheme, fairness with legacy WiFi users operating in common channel is considered. We investigate the interaction between the operator and the user using a Stackelberg game. We derive the best response function for both operator and user to maximize its utility under complete information, such as service contract and usage pattern. However, it is not always practical to know the comprehensive knowledge of the user in a highly dynamic environment. Thus, a various multi-armed bandit algorithms are used and compared to drive convergence towards an optimal solution. Simulation results have been presented to compare and verify the performance of our proposed scheme.

Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

To efficiently complete a complex computation task, the complex task should be decomposed into sub-computation tasks that run parallel in edge computing. Wireless Sensor Network (WSN) is a typical application of parallel computation. To achieve highly reliable parallel computation for wireless sensor network, the network's lifetime needs to be extended. Therefore, a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the network. This paper proposes a task model and a cluster-based WSN model in edge computing. In our model, different tasks require different types of resources and different sensors provide different types of resources, so our model is heterogeneous, which makes the model more practical. Then we propose a task allocation algorithm that combines the Genetic Algorithm (GA) and the Ant Colony Optimization (ACO) algorithm. The algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be extended. The experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

EPH129 Blue Cross and Blue Shield of Louisiana's COVID-19 Outbreak Simulation- Deterministic SEIR Modeling

Blue Cross and Blue Shield of Louisiana (BCBSLA) has been aggregating, analyzing, and forecasting COVID-19 projections for hospitalization, death, and facility capacity. The analytics are routinely shared with Louisiana health partners and assist in policy making and proper allocation of crucial medical resources.

Facility Layout Design of New Warehouse and Implementation of Multi-Criteria Decision Making for Product Allocation at PT XYZ

As the company's business grows, there product distribution network will increase. This will cause distribution costs to be increase. Therefore, the company decided to build a new warehouse in order to increase the efficiency of distribution costs. This new warehouse will use two storage systems, those are racking system and floor storage, so it need to design a good layout so that the space provided can be used effectively and efficiently. A dedicated storage policy is used to make workers easier to find products so that the time of product retrieval is faster. Another thing to consider is the proper allocation of products because this can affect the total distance of product movement which can also affect the time of product retrieval. In this study, multi-criteria decision making (MCDM) will be used to determine which product will be the main priority to be placed near the inbound/outbound (I/O). Furthermore, we will consider supporting facilities so that warehousing activities can run well where the preparation of each facility will use the activity relationship chart (ARC).

Community-Based Violence Against Women (VAW) Desks in the Philippines: A Multi-Level Assessment

This study is a multi-level assessment of community-based Violence Against Women (VAW) desks status: (1) VAW system (policies and legislation); (2) entity (VAW desk setup), and (3) individual (VAW desk officers) levels’ capacities in Calamba City, Laguna, Philippines. Data were gathered through a survey of 105 VAW desk officers and key informant interviews of five local officials. In addition, document reviews of national and local policies and reports on VAW were also employed in this study. Findings suggested that policies on establishing barangay-level VAW desks were properly enforced in Calamba City. However, the research results also showed a lack of equipment and resources to address VAW cases properly. In addition, VAW desk officers’ age and educational background limit their capacity to implement their responsibilities effectively, for there are no clear qualifications needed to be a desk officer. There is also no clear local system on the orientation of roles and responsibilities of the desk officers. Thus, the following recommendations: (1) national or local policy amendment to include the basic qualifications of desk officers; (2) maximize the role of the local group of desk officers in capacity-building and advocacy while partnering with civil society; (3) proper fund allocation of the city to ensure full functionality of VAW desks; and (4) strengthen feedback system and enforcement of rewards and penalties to motivate proper functioning of the desk offices. The paper contributes to local governance perspectives, specifically on multi-level coordination, to effectively address VAW and provide gender and development (GAD) support services.

Conflict-Based Search for the Virtual Network Embedding Problem

In emerging network virtualization architectures, service providers will be able to create many heterogeneous virtual networks and offer customized end-to-end services by leasing shared resources from infrastructure providers. The Virtual Network Embedding (VNE) problem is central to such technology. It involves the proper allocation of CPU and bandwidth resources available in a physical substrate network to meet the demands of multiple virtual networks. Combinatorially, the VNE problem is a problem in resource management that is NP-hard to solve. In this paper, we present a novel version of the Conflict-Based Search (CBS) algorithm for solving the VNE problem. Our approach, called VNE-CBS, is inspired by the success of the CBS framework in the Multi-Agent Path Finding domain. We successfully address the unique challenges in applying the CBS framework to the VNE problem, and, in doing so, we pave the way for overcoming a crucial issue in Internet ossification via heuristic search methods. On the theoretical front, we show that, unlike many existing algorithms, our algorithm is complete and optimal. On the experimental front, we show that our algorithm significantly outperforms other state-of-the-art methods on various benchmark instances for both the offline and online versions of the VNE problem.

Local and Regional Management Approaches for the Redesign of Local Development: A Case Study of Greece

The efforts of different local and regional administrations are increasingly seen as very important drivers of the economic vitality and well-being of territories in a world where subnational government levels continue to gain levels of authority. This study sought to investigate the efficacy of revamping local and regional development through local and regional management practices with a key focus on Greece. A mixed-methods research design was utilized; data were collected using a questionnaire from 200 entrepreneurs and investors from the local community in Kozani and through interviews with six local government leaders in Kozani city, Greece. The study shows that effective local–regional management practices normally function within the constraints provided by local contextual circumstances and traits as well as the possibilities offered by them. In this case, localized techniques may be used in areas with varying degrees of ex ante favorability, economic structures, and different resource endowments, as well as capacities that are essential for regional and local development. Any barriers that may affect the effectiveness of local–regional management practices can be eliminated through proper decision making, the proper allocation of resources, and participatory leadership at the local government level.

Conscious Exploration of Alpha-Cuts in the Parametric Solution of the School Bus Routing Problem with Fuzzy Walking Distance.

Combinatorial optimization problems allow for modeling multiple situations in which proper allocation of resources is needed. For some real-world problems, the use of fuzzy elements in the models allows for incorporating certain levels of uncertainty to better approximate such real-world situations. One way to solve combinatorial optimization problems with fuzzy elements is the parametric approach, where it is necessary to define how to explore different relaxation levels using alpha-cuts. Researchers tend to select such alpha-cuts uniformly. The current investigation proposes a novel strategy for selecting alpha-cuts in the School Bus Routing Problem with fuzzy students' maximum walking distance. This proposal bases its foundations on the number of student-bus stop pairs available according to the different levels of relaxations allowed. Results demonstrate how the proposed strategy gives attractive solutions with more diverse trade-offs, contrasted with other methods in the literature. Furthermore, it decreases the computational cost for those instances where the maximum relaxation does not provide new pairs of students-bus stops.

VALIDATION OF A NONINVASIVE METHOD TO ESTIMATE SUBENDOCARDIAL OXYGEN SUPPLY AND DEMAND BALANCE

Objective: Evaluation of balance between subendocardial oxygen supply and demand is useful in order to assess the risk of myocardial ischemia. The subendocardial viability ratio (SEVR, also known as Buckberg index) evaluated by invasive recordings of left ventricular and aortic pressure curves represents a valid method to estimate the degree of myocardial perfusion relative to left ventricular workload. However, routine clinical use of this parameter requires a noninvasive estimation and the demonstration of its reliability. Design and method: Arterial applanation tonometry allows a noninvasive estimation of SEVR as the ratio of the areas directly beneath the central aortic pressure curves obtained during diastole (myocardial oxygen supply) and during systole (myocardial oxygen demand). However, this traditional method does not account for the intraventricular diastolic pressure and proper allocation to systole and diastole of left ventricular isometric contraction and relaxation, respectively, resulting in an overestimation of the SEVR values. These issues are considered in a novel method for SEVR assessment tested in this study. SEVR values were estimated with carotid tonometry by traditional and new method and compared with those evaluated invasively in 51 undergoing an cardiac catheterization for coronary angiography. Results: The traditional method provided significantly higher SEVR values than the reference invasive SEVR: average of differences ± SD = 44 ± 11%. The noninvasive new method showed a much better agreement with the invasive determination of SEVR: average of differences ± SD = 0 ± 8%. The new method for SEVR estimation showed a good repeatability between consecutive assessments (coefficient of variation 7.8%). Conclusions: A noninvasive estimation of SEVR by carotid applanation tonometry is feasible. All the main factors determining myocardial supply and demand flow should be considered in order to provide a valid estimate, in particular intraventricular diastolic pressure, left ventricular isometric contraction and relaxation.

IRDA: Incremental Reinforcement Learning for Dynamic Resource Allocation

Resource allocation problems often manifest as online decision-making tasks where the proper allocation strategy depends on the understanding of the allocation environment and resource workload. Most existing resource allocation methods are based on meticulously designed heuristics which ignore the patterns of incoming tasks, so the dynamics of incoming tasks cannot be properly handled. To address this problem, we mine the task patterns from the large volume of historical allocation data and propose a reinforcement learning model termed IRDA to learn the allocation strategy in an incremental way. We observe that historical allocation data is usually generated from the daily repeated operations, which is not independent and identically distributed. Training with the partial dataset can make the strategy converged already, thereby wasting the remaining data. To improve the learning efficiency, we partition the whole historical big dataset into multi-batch datasets, which forces the agent continuously to ‘explore’ and learn on the distinct state spaces. We apply the proposed method to handle baggage carousel allocation at Hong Kong International Airport (HKIA). The experimental results show that IRDA improves the baggage carousel resource utilization by around 51.86% compared to the current baggage carousel allocation system at HKIA.

Impact of the COVID-19 pandemic on cancer incidence and mortality

Impact of the COVID-19 pandemic on cancer incidence and mortality

Task allocation and coordination process in distributed agile software development: an ontology based approach.

Distributed agile software development (DASD) has gained much popularity over the past years. It relates to Agile Software Development (ASD) being executed in a distributed environment due to factors such as low development budget, emerging software application markets and the need for more expertise. DASD faces a number of challenges with respect to coordination and communication issues. Task allocation in such an environment thus becomes a challenging task. Adopting proper task allocation strategy is crucial to overcome challenges and issues in DASD. Various studies highlight the challenges being faced by DASD and have proposed solutions in the form of framework or models. Knowledge models in the form of ontologies can help to solve certain issues and challenges by providing a proper representation of data that is shareable among distributed teams. Several ontologies with respect to task allocation exist. However, ontologies incorporating factors and dependencies influencing task allocation process in DASD are limited. An ontology representing the knowledge related to task allocation and coordination is important for proper decision making in organizations. Based on an in-depth literature review and a survey conducted among professionals in industry, this paper proposes an ontology, OntoDASD, that incorporates relevant factors and dependencies to be considered in task allocation and coordination process in DASD environment. The ontology facilitates team coordination through effective communication and task allocation by defining the concepts to share knowledge and information in an appropriate way. OntoDASD has been properly evaluated and validated by professionals in the field.

Navigating Neurotrauma Management in Pakistan.

Madam, neurotrauma is defined as an injury to the brain and/or spinal cord. A WHO, sponsored study estimated that the annual global incidence of neurotrauma is approximately 500–800 per 100 000 and it accounts for about 11.8% of total global disability-adjusted life years1. In Pakistan, the annual incidence of head injured patients admitted to numerous neurosurgical centres was 50/100,000 population per year2. The lifetime medical treatment per neurotrauma case is estimated to range from US$600 000 to US$1.8 million. Neurotrauma management incorporates prehospital care lasting minutes to hours which encompasses accurate on site diagnosis and systematic treatment or during the e shifting, to an organized, well-equipped health care management, in-hospital care for hours to weeks which comprises surgical and non-surgical intervention including imaging, neuro-monitoring and critical care and finally post-acute care lasting weeks to years and denotes any form of rehabilitative interventions to enable and empower patients to have an increased quality of life1. Each component of management requires trained personnel, specific equipment, and protocols3. However, Pakistan lags in various aspects. As of a last study in 2001, there were only 35 neurosurgical centres and about 1000 neurosurgical beds to accommodate a population of 130 million2. Of the 23 Karachi hospitals studied in 2020, only 57% were well-equipped and accredited [Trauma capacity score (TCS) 67%] to perform elementary resuscitation steps to secure early stability. A sizeable number,57%, s (TCS<34%) lacked space to accommodate head, neck, and spinal injuries; 65% s were partly inadequate (TCS<67%) in diagnosis, intensive care and safety4. With low medical insurance across the country2. Inadequate availability of essential medicines was also reported in 43% of hospitals4. An organized trauma care system must be introduced to mitigate trauma mortality for local needs. It should encompass enabling those at injury site to become first responders3, an ambulance service equipped with communication systems and with properly trained personnel that can commence life-saving measures such as IV fluid resuscitation3. Trauma centres should have a referral network and hospitals that provide optimal traumatic care should be identified beforehand to transfer injured patients to this specialized facilitys4.A protocol also needs to be established that assists local doctors in immediate referral to trauma centers3. Proper allocation of resources and mobilization of staff and doctors timely can improve neurotrauma outcomes significantly. Continuous...

Decision Making For Recruitment and Promotion Policies Using Linear Programming

Optimizing decision is one of the procedures used for maximization of benefit with minimization of cost. To achieve a meaningful development in any organization, proper allocation of funds is needed with minimized cost. There would be no meaningful development in any organization that will not involve recruitment and promotion. The method used in this study for optimizing a manpower recruitment and promotion is linear programming approach, proposing a model and applying the model on data collected from hypothetical data for optimizing recruitment and promotion for N-grade manpower system. The output from the research was able to identify the total costs of recruitment is 17,433,800 (seventeen million, four hundred and thirty-three thousands, eight hundred naira) with 22 (twenty-two recruited staff. The total costs for promotion is 10,297,800 (ten million, two hundred and ninety-seven thousands, eight hundred naira) with 22 (twenty-two) staff promoted. From the model formulated, it can be applied by other organization considering recruitment and promotions of staff with a minimized cost for the manpower system when faced with limited available resources.

Assessing the earthquake systemic vulnerability of the urban centres in the South-East region of Romania. The tale of Galați and Brăila Cities, Romania

Earthquakes are one of the most destructive natural phenomena in the world, their impact being of particular severity in urban areas. Failures relating to proper allocation of resources, to the identification of optimal routes between the affected urban areas and relief centres, and to the mitigation of potential earthquake-triggered destructive phenomena emerge as systemic vulnerability sources. This paper aims to assess the seismic systemic vulnerability of the 6 administrative centres in the South-East region of Romania, by proposing a weighted composite index (Earthquake Vulnerability Index, ESVI) that integrates indicators referring to the accessibility of emergency services centres, the capacity of the local medical infrastructure and secondary danger sources. The validation of ESVI relies on Sensitivity Analysis and Multi-Criteria Decision-Making (MCDM) methods. Moreover, in order to illustrate the utility of an assessment of this type, a comparative case study of Galați and Brăila Cities is presented. This paper shows how the integration of GIS tools and techniques may improve vulnerability assessments, especially when they are used in conjunction with MCDM methods. ESVI and its integrated maps point out the most vulnerable urban centres and the hotspots of vulnerability within them, allowing for advanced regional and local scale planning of emergency interventions.

Fuzzy expert systems for prediction of ICU admission in patients with COVID-19

The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with COVID-19 cases. As medical resources are limited, deciding on the proper allocation of these resources is a very crucial issue. Besides, uncertainty is a major factor that can affect decisions, especially in medical fields. To cope with these issues, we use fuzzy logic (FL) as one of the most suitable methods in modeling systems with high uncertainty and complexity. We intend to make use of the advantages of FL in decisions on cases that need to treat in ICU. In this study, an interval type-2 fuzzy expert system is proposed for the prediction of ICU admission in COVID-19 patients. For this prediction task, we also developed an adaptive neuro-fuzzy inference system (ANFIS). Finally, the results of these fuzzy systems are compared to some well-known classification methods such as Naive Bayes (NB), Case-Based Reasoning (CBR), Decision Tree (DT), and K Nearest Neighbor (KNN). The results show that the type-2 fuzzy expert system and ANFIS models perform competitively in terms of accuracy and F-measure compared to the other diagnosis systems.