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This research uses a multifaceted approach to develop an MPA/MPP curriculum to support a data science track within the existing MPA/MPP programs by identifying the core and elective areas needed.

The approach includes (1) identifying a suitable structure for MPA/MPP programs which can allow the program to develop its capacity to train students with the data science and general public administration skills to solve public policy problems and leave explicit space for local experimentation and modification; (2) defining bridging modules and required modules for the MPA/MPP programs; and (3) developing of data science track thought to make suggestions for the inclusion of suitable data science modules into the data science track and benchmarking the data science modules suggested with the best practices developed by other professional bodies. The authors review 46 NASPAA-accredited MPA/MPP programs from 40 (or 22.7%) schools to identify the suitable required modules and some potential data science and analytics courses that MPA/MPP programs currently provide as electives.

The proposal includes a three-course (six–nine credits, not counted in the program but as prerequisites) bridging module, a nine-course (27 credits) required module and a five-course (15 credits) data science track/concentration.

This work can provide a starting point for the public administration education community to develop graduate programs focusing on data science to cater to the needs of both public managers and society at large.

Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques.

A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance.

MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower.

In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.

The Remain in Mexico Policy, also known as the Migrant Protection Protocols (MPP), forced asylum-seeking children into seemingly untenable situations and conditions across the United States’ South Texas border. This case study examines stories told by the children of Tent Zero and their parents about the conditions and experiences they faced as a result of MPP. Their stories reveal that the conditions asylum-seeking children at the South Texas border faced have been severe and include, but are not limited to, a lack of adequate food and water, extreme weather, inadequate sanitation, and a lack of appropriate or consistently available schooling. Prior research has not fully investigated how educators can and should work with students who have experienced such severe conditions, which often result in compounded emotional suffering. By examining the effects that the MPP, which was instituted during the Trump administration, has had on youth and families staying in Matamoros, Mexico, as a result of this policy, this case study not only provides a glimpse into the lived experiences of asylum-seeking children and families at the South Texas Border, but also provides insight and guidance for educational policymakers, educators, and communities where these youth transition to and continue in school in the US. As a starting point, all of the asylum-seeking children who participated in this study expressed their interest in and longing to go to school demonstrating both the practical and symbolic importance of formal education in asylum-seeking communities like those at Tent Zero.

The purpose of this paper is to construct a market price predictor (MPP) for forestland properties by applying a sales comparison approach (SCA) with several value-related characteristics obtainable from the property-specific sales line declarations.

An SCA-based predictor was designed for appraising and valuing forestland properties with varying quantitative features that impact their overall value. Using a two-stage classification procedure, representative reference sales (i.e. comparables) are objectively and commensurately selected for the subject using location and forest characteristics as classifiers.

The new SCA-based MPP is a stable and reliable tool applicable for pricing forestland properties in any location when data from comparables are available.

A systematic and spatio-temporally continuous data collection procedure is a prerequisite for obtaining appropriate data for the SCA-based appraisal and valuation techniques, including the MPP model presented in this study.

The MPP model is suitable for the practical appraisal and valuation of forestland properties.

It is expected that by applying the MPP model for the appraisal and valuation of forestland properties, positive societal contributions will be achieved through the intensification of the forestland property market.

The MPP model provides an objective alternative to the adding-value technique, which is the most commonly applied tool to appraise forestland properties in Finland. It is also offers an assumption-free alternative to the income approach.

The purpose of this paper is to understand the risk‐return profile of merchant power plants (MPPs) as compared to power plants with off‐take agreements in the Indian context.

Information from the literature was analyzed to identify major risks associated with MPPs. Literature pertaining to risk analysis of capital investments and simulation technique to analyze risks was studied. Financial models for a power plant with off‐take agreements and that for an MPP were developed and risks have been analyzed by incorporating uncertainties. The risk analysis was performed with the application of stochastic simulations using the Monte Carlo technique.

The results indicate that risk‐return profile differs significantly for a MPP as compared to a power plant with off‐take agreements. The model indicates that equity internal rate of return (IRR) for MPP ranges between 49.33 and 0.43 per cent with mean IRR values ranging between 28.86 and 13.53 per cent for different scenarios. The mean equity IRR for a comparative power plant with off‐take agreements is 14.83 per cent.

The project financial models are developed using typical values for the Indian context. The robustness of the results can be improved by considering project‐specific variables in the financial model. Owing to limited availability of past data, power price fluctuation scenarios have been generated based on expert opinion. When additional data on power price become available, these could be incorporated in the simulation analysis.

MPP is a new concept for India. There is very little research in this area. This paper makes an attempt to understand the financial risk and return from MPPs in more detail.

In most maximum power point tracking (MPPT) methods described in the literature, the optimal operating point of the PV systems is estimated by linear approximations. These approximations can reduce considerably the performances of the PV systems. This paper seeks to provide comparative analyses of different MPPT methods used in photovoltaic (PV) systems and proposes a new approach that uses a nonlinear expression of the optimal voltage in combination with perturbation and observation (P&O) methods.

First, an analytical model for determining the nonlinear PV optimal operating point is detailed and each equation is explained. Second, a combination of the new method with P&O method is proposed to reduce the PV losses.

The simulation results showed that the approach improves clearly the tracking efficiency of the maximum power available at the PV modules output. The implementation of this new method will improve PV systems energy production rate and its long‐term storage in hydrogen form.

The simulation results showed that the new approach improves the MPP's tracking efficiency of the PV system on average at 92 percent. The implementation of the developed approach in a PV system with hydrogen storage increased the energy transfer from PV modules to the electrolyzer.

This paper proposes a new approach to determine the maximum power point (MPP) from the measurement of the open circuit voltage of PV modules. A nonlinear expression of the optimal voltage was developed and is used in combination with P&O methods. The proposed approach largely improves the performance of the MPP tracking of the PV systems.

The distribution of state property to the private sector has always been and will continue to be intensely political. Relinquishing hiring, production, investment, and other enterprise decisions constitute a significant loss of potential rents to those who exercise control rights in state-owned enterprises. Additionally, the large transfer of wealth that privatization on a large-scale entails, combined with the potential for unemployment, loss of access to enterprise-based social services (which were substantial in state-socialist economies) threatens to undermine public support for privatization and reform in general.

The purpose of this paper is to present a novel approach to the determination of the maximum power point (MPP) in the photovoltaic system using genetic algorithm (GA). The optimization is realised on two types of photovoltaic (PV) modules: monocrystalline and polycrystalline solar modules, with the same rated peak power (400 Wp) but different electrical output data.

The proposed algorithm is a nature-based algorithm that uses genetic operators such as reproduction, crossover and mutation to realise the search through the investigated area of solutions. To determine the MPP of the PV modules, a two-diode model of a PV cell is used. Based on the input electrical data for the analysed PV module, as well as the mathematical model of the PV, the algorithm can estimate the current and voltage at the MPP for given solar irradiation and cell temperature. The analysis is made for several different irradiations, but in work, the results are presented for irradiations of: 100, 500 and 1,000 W/m² and cell temperatures of 0, 25 and 40 °C.

From the presented results and performed analysis, it can be concluded that GA gives adequate results for both modules and for all working conditions. From the obtained results, it can be concluded that the optimization algorithm performs better when applied to the monocrystalline module works better especially in conditions with larger cell temperature, in comparison with the performance of the optimization algorithm applied to the polycrystalline module. On the other hand, the optimization algorithm applied to the polycrystalline module works better for the other working scenarios with smaller cell temperatures.

From the performed analysis, it can be concluded that the use GA as an optimization tool for the determination of the MPP can be successfully implemented. In addition, to improve the overall performance of the PV system, it is also necessary to forecast the weather conditions of the location where the PV system would be installed to forecast the cell temperature and the solar irradiation. This is necessary to choose the right PV module and inverter for the given location.

An optimization technique using GA as an optimization tool has been developed and successfully applied in the determination of the MPP for a PV system. The results are compared with the analytically determined values as well as with the values given by the producer, and they show good agreement.

To analyze the operating performance of a fuzzy logic control (FLC) based solar energy conversion modular system controlled by a digital signal processor (DSP) microcontroller.

A range of published works relevant to the solar energy conversion modular systems are evaluated and their limitations are indicated in the first section of the paper. The circuit diagram of the panel‐boost converter system is described in the second section. In the third section, a neural network model is suggested for the photovoltaic panel and the model is created in the MATLAB/SIMULINK and then combined with other blocks existing in the system. The design of the FLC method is described in section 4. The simulation and experimental results corresponding to the control of the duty‐cycle of the converter to set the operating point of the solar panel at the maximum power point (MPP) are given in sections 5 and 6, respectively. Section 7, summarizes the results and conclusions of the study.

The paper suggests a simple dc‐dc boost converter controlled by FLC method. The proposed converter model can be used to obtain maximum power from a photovoltaic panel.

In preparing this paper, the resources books existing in the library of our university and the resources relative to the solar energy conversion and FLC published in English language and reachable through the internet were researched.

The paper suggests a neural network model for a solar panel, which can be used in the simulation of the solar energy panel‐boost converter system. The solar energy panel‐boost converter system proposed in this study can be used by the researchers who are working in the solar energy conversion area.

The suggestion of a neural network model for a solar panel and creation of this model in the MATLAB/SIMULINK environment provides researchers to simulate and to analyze the performance of the solar energy panel‐boost converter system using the MATLAB/SIMULINK simulation program. In addition, since the control approach proposed in this paper does not require the information on temperature and solar irradiance that affect the maximum output power, can effectively find the MPP of the solar panel.

Russia's size – both in terms of population and geography, spanning 11 time zones, 89 oblasts (states or regions) and autonomous republics and its privatization program, encompassing some 100,000 small-scale enterprises, 25,000 medium to large firms, and 300 or so of its largest firms, made its privatization program the largest sale/transfer of assets conducted among the transition economies, with the possible exception of China. Comparisons by many of the program's critics, and there are many, to Poland, Hungary, or the Czech republic are invidious, especially the latter two countries whose populations are similar to just that of greater Moscow.