Search results for: policy optimization

Authors: Vrajesh Rawal

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India's economic landscape has witnessed significant transformations over the past decades, characterized by shifts from agrarian to service-oriented economies. Recently, there has been a growing emphasis on transitioning towards a manufacturing-led growth model driven by factors such as demographic changes, technological advancements, and evolving global trade dynamics. These changes reflect broader efforts to enhance industrialization, boost employment opportunities, and diversify the economic base beyond traditional sectors. Within this context, this research focuses on understanding the specific drivers and dynamics behind India's shift from a predominantly service-based economy to one centered on manufacturing. It seeks to explore how political ideologies influence economic policies and shape sectoral priorities, with a particular focus on contrasting approaches between the Indian National Congress (INC) and the Bharatiya Janata Party (BJP). Additionally, the study evaluates the alignment of IMF policy recommendations with India's economic goals and priorities within the theoretical frameworks of neoliberalism and political economy theory. Despite the extensive literature on India's economic reforms and political economy, there remains a gap in understanding how political ideology influences sectoral shifts and economic policy outcomes, particularly in the context of IMF recommendations. Existing studies often focus narrowly on either political ideologies or economic reforms without fully integrating both perspectives. This research aims to bridge this gap by providing a comprehensive analysis that integrates political economy theories with empirical evidence from political speeches, government documents, and IMF reports. Through qualitative content analysis of speeches by political leaders, document analysis of key governmental documents, and scrutiny of party manifestos, this research demonstrates how political ideologies translate into distinct economic strategies and developmental agendas. It highlights the extent to which IMF policy prescriptions align with India's economic objectives and how these interactions shape broader socio-economic outcomes. The theoretical framework of neoliberalism and political economy theory provides a lens to interpret these findings, offering insights into the complex interplay between economic policies, political ideologies, and institutional frameworks in India. The findings of this study are expected to provide valuable insights for policymakers, researchers, and practitioners involved in economic governance and development planning in India. By understanding the factors driving sectoral shifts and the influence of political ideologies on economic policies, policymakers can make informed decisions to foster sustainable economic growth and development. Implementation of these insights could contribute to refining policy frameworks, enhancing alignment with national development priorities, and optimizing engagement with international financial institutions like the IMF to better meet India's socio-economic challenges and opportunities in the evolving global context.

Keywords: political economy, international politics, social science, policy analysis

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Authors: Izzati Nabiyla Risfa

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The role of civil society has become an important issue in national and international level nowadays. Government all over the world started to realize that the involvement of civil society can boost up public services and better policy making. Global Policy Forum stated that there are five good reasons for civil society to be engaged in global governance; (1) to conferring legitimacy on policy decisions; (2) to increasing the pool of policy ideas; (3) to support less powerful governments; (4) countering a lack of political will; and (5) helping states to put nationalism aside. Indonesia also keeps up with this good trend. In November 2011, Indonesian Government set up LAPOR! (means “to report” in Indonesian), an online portal for complaints about public services, which is accessible through its website lapor.ukp.go.id. LAPOR! also accessible through social media (Twitter, Facebook) and text message. This program is an initiative from the government to provide an integrated and accessible portal for the Indonesian public to submit complaints and inquiries as a means of enhancing public participation in national development programs. LAPOR! aims to catalyze public participation as well as to have a more coordinated national complaint handling mechanism. The goal of this program is to increase the role of civil society in order to develop better public services. Thus, LAPOR! works in a simplest way possible. Public can submit any complaints or report their problem concerning development programs and public services simply through the website, short message services to 1708 and mobile applications for BlackBerry and Android. LAPOR! will then transfer every validated input to relevant institutions to be featured and responded on the website. LAPOR! is now integrated with 81 Ministries, 5 local government, and 44 State Owned Enterprise. Public can also give comments, likes or share them through Facebook and Twitter to have a discussion and to ensure the completeness of the reports. LAPOR! has unexpectedly contributed to various successful cases concerning public services. So far the portal has over 280,704 registered users, receiving an average of 1,000 reports every day. Government's response rate increase time to time, with 81% of complaints and inquiries have been solved or are being investigated. This paper will examine the effectiveness of LAPOR! as a tools to increase the role of civil society in order to develop better public services in Indonesia. Beside their promising story, there still are various difficulties that need to be solved. With qualitative approach as methodology for this research, writers will also explore potential improvement of LAPOR! so it can perform effectively as a leading national complaint handling system in Indonesia.

Keywords: civil society, government, Indonesia, public services

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Authors: Shoumodip Roy, Ankit Singhania, M. K. G. Choudhury, Santanu Mallick, M. K. Agarwal, R. V. Ramna, Uttam Singh

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One of the key corporate goals of Tata Steel company is to demonstrate Environment Leadership. Decreasing specific CO₂ emission is one of the key steps to achieve the stated corporate goal. At any Blast Furnace, specific CO₂ emission is directly proportional to fuel intake. To reduce the fuel intake at G Blast Furnace, an initial benchmarking exercise was carried out with international and domestic Blast Furnaces to determine the potential for improvement. The gap identified during the exercise revealed that the benchmark Blast Furnaces operated with superior raw material quality than that in G Blast Furnace. However, since the raw materials to G Blast Furnace are sourced from the captive mines, improvement in the raw material quality was out of scope. Therefore, trials were taken with different operating regimes, to identify the key process parameters, which on optimization could significantly reduce the fuel intake in G Blast Furnace. The key process parameters identified from the trial were the Stoichiometric Oxygen Ratio, Melting Capacity ratio and the burden distribution inside the furnace. These identified process parameters were optimized to bridge the gap in fuel intake at G Blast Furnace, thereby reducing specific CO₂ emission to benchmark levels. This paradigm shift enabled to lower the fuel intake by 70kg per ton of liquid iron produced, thereby reducing the specific CO₂ emission by 15 percent.

Keywords: benchmark, blast furnace, CO₂ emission, fuel rate

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Authors: Zofia Hammerin, Goran Basic, Disa Bergnehr

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Ever since the Ottawa charter in 1986, health promotion through schools has been stressed across the globe. Both in the global and national discourse, schools are made responsible not only for providing education but also for working with pupil health and well-being. In Sweden, where the study is set, it is emphasized in national directives that promoting pupil health should be part of the school practice. Since the Swedish school system is decentralized, these directives need to be interpreted and recontextualized locally. This study aims to explore how the student comes across in Swedish local health policy documents. The data consists of 37 such documents called student health plans collected from different high schools throughout Sweden. The analysis was inspired by critical discourse analysis, and tentative results are divided into two main themes; the invisible actor and the passive recipient. The pupil is largely invisible in the documents, and the discourse instead focuses on school health service staff and, to some extent, the teachers. When the pupils are visible, they mainly come across as passive recipients of health promoting actions. Since participation, taking action, and feeling empowered are key aspects of health promotion, the findings could impact the pupils’ possibilities for health and well-being.

Keywords: health promotion, high school, student, sweden

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Authors: Mahnaz Motamedi, Mohammad Shahbazi, Shahrzad Rahimi-Naghani, Mehrdad Salehi

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Introduction: Identifying sexual health needs, developing appropriate plans, and delivering services to meet those needs is an essential component of health programs for women, men, and children all over the world, especially in poor countries. Main Subject: The aim of this study was to describe the needs of sexual health from the viewpoint of health policymakers in Iran. Methods: A qualitative study using thematic content analysis was designed and conducted. Data gathering was conducted through semi-structured, in-depth interviews with 25 key informants within the healthcare system. Key informants were selected through both purposive and snowball sampling. MAXQUDA software (version 10) was used to facilitate transcription, classification of codes, and conversion of data into meaningful units, by the process of reduction and compression. Results: The analysis of narratives and information categorized sexual health needs into five categories: culturalization of sexual health discourse, sexual health care services, sexual health educational needs, sexual health research needs, and organizational needs. Conclusion: Identifying and explaining sexual health needs is an important factor in determining the priority of sexual health programs and identification of barriers to meet these needs. This can help other policymakers and health planners to develop appropriate programs to promote sexual and reproductive health.

Keywords: sexual health, sexual health needs, policy makers, health system, qualitative study

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Authors: N. Guo, C. Xu, Z. C. Yang

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In order to ensure the high reliability of an aircraft, the accurate structural dynamics analysis has become an indispensable part in the design of an aircraft structure. Therefore, the structural finite element model which can be used to accurately calculate the structural dynamics and their transfer relations is the prerequisite in structural dynamic design. A dynamic finite element model updating method is presented to correct the uncertain parameters of the finite element model of a structure using measured modal parameters. The coordinate modal assurance criterion is used to evaluate the correlation level at each coordinate over the experimental and the analytical mode shapes. Then, the weighted summation of the natural frequency residual and the coordinate modal assurance criterion residual is used as the objective function. Moreover, the hybrid pattern search (HPS) optimization technique, which synthesizes the advantages of pattern search (PS) optimization technique and genetic algorithm (GA), is introduced to solve the dynamic FE model updating problem. A numerical simulation and a model updating experiment for GARTEUR aircraft model are performed to validate the feasibility and effectiveness of the present dynamic model updating method, respectively. The updated results show that the proposed method can be successfully used to modify the incorrect parameters with good robustness.

Keywords: model updating, modal parameter, coordinate modal assurance criterion, hybrid genetic/pattern search

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Authors: Raghunath Mahabir

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This paper provides an analysis of the existing evidence on the relationships between the migration of Venezuelans into Trinidad and Tobago, violent extremism and gang violence. Arguing that there is a dearth of reliable data on the subject matter, the paper fills the gap by providing relevant definitions of terms used, discusses the sources of data and identifies the causes for this migration and the subsequent ramifications for Trinidad and Tobago and for the migrants themselves. A simple but clear classification pointing to the nexus between migration gang violence and violent extremism is developed, following the logic of migration of criminals(gang members), the need to link with local gangs and the view that certain elements within the TnT society has become radicalized to the point where violent extremism is being displayed in different ways. The paper highlights implications for further policy debate:the imperatives for more effective communication between government officials responsible for migration and those personnel who are tasked with countering violent extremism and gang violence: promoting and executing better integration and social inclusion policies which are necessary to minimize social exclusion, and the threat of violent extremist agendas emanating from both Venezuelans and Trinidadians and generally to establish strong analytical framework grounded in stronger definitions, more reliable data and other evidence which can guide further research and analysis and contribute to policy formation.

Keywords: migration, violent extremism, gangs, Venezuela

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Yahaya Abdullahi Adadu, Canice Erunke Esidene

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The problems of socio-economic development have been a major challenge bedeviling the Nigerian post-colonial state since her political independence from Britain in October I,1960. Critics have argued that the dilemma of Nigeria’s economic survival started since the early 1970s when the agricultural sector which supposedly was the economic mainstay has been literally substituted with the gains of the oil petro-dollars coming from the foreign exchange earnings. Agriculture therefore, which used to be a major player in terms of human and national upliftment in Nigeria have been given a back seat while oil and gas has taken over the front burner in virtually every aspect of Nigeria’s national life. This study is therefore an exposition of the efforts of the Nasarawa state government in reversing the dangerous trend in which the over reliance on oil wealth has caused to persons, individuals and groups in terms of the prevailing levels of poverty and other attendant vices therein. The study focuses on the management policies of the various regimes in the state since its inception in 1996, with particular reference to the regime types-military and civilian alike in propelling the needed policy change, which could transform the economy in line with international best practices. Particular emphasis will be paid to the BADA-KOSHI agricultural scheme whose interest was to recover the lost glory of rural agriculture through series of roots and tuber expansion, and particularly such crops as yam minissetts, cassava, sweet potatoes and coco-yam, respectively. The paper covers the period between 1996 -2011, a period considered to be critical in the agricultural revolution of the state. The study adopts a theoretical approach via secondary methods of analysis for the efficient explanations of the burning issues under consideration. The paper sums up with policy recommendations and conclusion.

Keywords: poverty, agriculture, Badakoshi, rural policy management

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Authors: Daneal Rorke, Gueguim Kana

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The shift towards renewable energy sources for biofuel production has received increasing attention. However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production.

Keywords: artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves

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Authors: Mohsen Azarmjoo, Navid Sharifi, Zahra Alikhani Koopaei

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This article aims to conserve energy and optimize the performance of solar water heaters using modern modeling systems. In this study, a large-scale solar water heater is modeled using an AVR microcontroller, which is a digital processor from the AVR microcontroller family. This mechatronic system will be used to analyze the performance and design of solar collectors, with the ultimate goal of improving the efficiency of the system being used. The findings of this research provide insights into optimizing the performance of solar water heaters. By manipulating the arrangement of solar panels and controlling the water flow through them using the AVR microcontroller, researchers can identify the optimal configurations and operational protocols to achieve the desired temperature and flow conditions. These findings can contribute to the development of more efficient and sustainable heating and cooling systems. This article investigates the optimization of solar water heater performance. It examines the impact of solar panel layout on system efficiency and explores methods of controlling water flow to achieve the desired temperature and flow conditions. The results of this research contribute to the development of more sustainable heating and cooling systems that rely on renewable energy sources.

Keywords: energy conservation, solar water heaters, solar cooling, simulation, mechatronics

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Authors: Robin Molinier

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Industrial symbiosis (I.S) rely on two modes of cooperation that are infrastructure sharing and resource substitution to obtain economic and environmental benefits. The former consists in the intensification of use of an asset while the latter is based on the use of waste, fatal energy (and utilities) as alternatives to standard inputs. Both modes, in fact, rely on the shift from a business-as-usual functioning towards an alternative production system structure so that in a business point of view the distinction is not clear. In order to investigate the way those cooperation modes can be distinguished, we consider the stakeholders' interplay in the business model structure regarding their resources and requirements. For infrastructure sharing (following economic engineering literature) the cost function of capacity induces economies of scale so that demand pooling reduces global expanses. Grassroot investment sizing decision and the ex-post pricing strongly depends on the design optimization phase for capacity sizing whereas ex-post operational cost sharing minimizing budgets are less dependent upon production rates. Value is then mainly design driven. For resource substitution, synergies value stems from availability and is at risk regarding both supplier and user load profiles and market prices of the standard input. Baseline input purchasing cost reduction is thus more driven by the operational phase of the symbiosis and must be analyzed within the whole sourcing policy (including diversification strategies and expensive back-up replacement). Moreover, while resource substitution involves a chain of intermediate processors to match quality requirements, the infrastructure model relies on a single operator whose competencies allow to produce non-rival goods. Transaction costs appear higher in resource substitution synergies due to the high level of customization which induces asset specificity, and non-homogeneity following transaction costs economics arguments.

Keywords: business model, capacity, sourcing, synergies

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Authors: Zohreh Khojasteh-Ghamari, Takashi Irohara

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One of the efficient ways in supply chain risk management is avoiding the interruption in Supply Chain (SC) before it occurs. Although the majority of the organizations focus on their first-tier suppliers to avoid risk in the SC, studies show that in only 60 percent of the disruption cases the reason is first tier suppliers. In the 40 percent of the SC disruptions, the reason is downstream SC, which is the second tier and lower. Due to the increasing complexity and interrelation of modern supply chains, the SC elements have become difficult to trace. Moreover, studies show that there is a vital need to better understand the integration of risk and visibility, especially in the context of multiple objectives. In this study, we propose a multi-objective programming model to avoid disruption in SC. The objective of this study is evaluating the effect of downstream SCV on managing supply chain risk. We propose a multi-objective mathematical programming model with the objective functions of minimizing the total cost and maximizing the downstream supply chain visibility (SCV). The decision variable is supplier selection. We assume there are several manufacturers and several candidate suppliers. For each manufacturer, our model proposes the best suppliers with the lowest cost and maximum visibility in downstream supply chain. We examine the applicability of the model by numerical examples. We also define several scenarios for datasets and observe the tendency. The results show that minimum visibility in downstream SC is needed to have a safe SC network.

Keywords: downstream supply chain, optimization, supply chain risk, supply chain visibility

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Authors: Arti Bagada, Kantilal Vadalia, Mihir Raval

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Cilnidipine is practically insoluble in water which results in its insufficient oral bioavailability. The purpose of the present investigation was to formulate cilnidipine nanoparticles by nanoprecipitation method to increase the aqueous solubility and dissolution rate and hence bioavailability by utilizing various experimental statistical design modules. Experimental design were used to investigate specific effects of independent variables during preparation cilnidipine nanoparticles and corresponding responses in optimizing the formulation. Plackett Burman design for independent variables was successfully employed for optimization of nanoparticles of cilnidipine. The influence of independent variables studied were drug concentration, solvent to antisolvent ratio, polymer concentration, stabilizer concentration and stirring speed. The dependent variables namely average particle size, polydispersity index, zeta potential value and saturation solubility of the formulated nanoparticles of cilnidipine. The experiments were carried out according to 13 runs involving 5 independent variables (higher and lower levels) employing Plackett-Burman design. The cilnidipine nanoparticles were characterized by average particle size, polydispersity index value, zeta potential value and saturation solubility and it results were 149 nm, 0.314, 43.24 and 0.0379 mg/ml, respectively. The experimental results were good correlated with predicted data analysed by Plackett-Burman statistical method.

Keywords: dissolution enhancement, nanoparticles, Plackett-Burman design, nanoprecipitation

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Authors: Shaghayegh Mirshekari, Atiyeh Ghorbani

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EThe Iranian system of education has been politically influenced by the thoughts of the governing religious party. It has brought many religious books into the educational system from grade one up to graduation from high school, and therefore, teaching English as a non-Islamic language has been put aside the system, focusing on the Islamic language of Arabic. Teaching English has been widely talked about in international academia, but the Iranian educational system has not brought in any of its outcomes due to the general policy of keeping people away from international Western thoughts. Because of the increasing interest among Iranians in learning English, this language is being taught and studied in public and private schools, commercial and adult schools, language institutes, colleges, universities, and numerous homes throughout the country. Methods and techniques of teaching English, the attitude of the teachers and learners towards the language, and the availability of textbooks and other language materials are quite different in any one of the different institutions. This paper has evaluated the outcome of the Iranian educational system in teaching English in terms of their methods of teaching, as well as the policies regarding the educational system. The results show that not only has there been no progress in the system in terms of teaching English, rather there is backwardness in this regard due to the political policy of preventing people from learning English. Therefore, we see the majority of the youth not speaking English properly at the age where they need to enter the international arena.

Keywords: English, public school, language, Iran, teaching

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: Maryam Haghi, Ivan Contreras, Nadia Bhuiyan

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Chemotherapy is one of the most popular and effective cancer treatments offered to patients in outpatient oncology centers. In such clinics, patients first consult with an oncologist and the oncologist may prescribe a chemotherapy treatment plan for the patient based on the blood test results and the examination of the health status. Then, when the plan is determined, a set of chemotherapy and consultation appointments should be scheduled for the patient. In this work, a comprehensive mathematical formulation for planning and scheduling different types of chemotherapy patients over a planning horizon considering blood test, consultation, pharmacy and treatment stages has been proposed. To be more realistic and to provide an applicable model, this study is focused on a case study related to a major outpatient cancer treatment clinic in Montreal, Canada. Comparing the results of the proposed model with the current practice of the clinic under study shows significant improvements regarding different performance measures. These major improvements in the patients’ schedules reveal that using optimization techniques in planning and scheduling of patients in such highly demanded cancer treatment clinics is an essential step to provide a good coordination between different involved stages which ultimately increases the efficiency of the entire system and promotes the staff and patients' satisfaction.

Keywords: chemotherapy patients scheduling, integer programming, integrated scheduling, staff balancing

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Authors: Niringiye Fred

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Banyabunagana has always had access to land resources for grazing animals, sand mining, and farmland across the border in the Democratic Republic of Congo during the pre-colonial and colonial times, usually on an informal arrangement facilitated by kinship ties and rent transactions for these resources. However, in recent periods, the government of the Democratic Republic of the Congo (DRC) has been pursuing a policy of constructing barriers such as walls and fences so that Banyabunagana communities do not access the land on the DRC side of the border. This is happening in the background of increased and intensified demand for land use on the side of the Ugandan community. This paper will attempt to discuss the reasons behind the construction of walls, fences, and other barriers which deny access to land for Banyabunagana communities in Bunagana Parish, Muramba Sub-county- Kisoro district, Uganda. The research will attempt to answer the following main questions, among others, whether there are the factors that explain the construction of walls and fences which could limit or deny access to the informal use of land and other resources and whether policy options to ensure continued access to land and other resources for local communities.

Keywords: border, walls, fences, land resource access

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Authors: Ronal Muresano, Andrea Pagano

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Nowadays, the mathematical/statistical applications are developed with more complexity and accuracy. However, these precisions and complexities have brought as result that applications need more computational power in order to be executed faster. In this sense, the multicore environments are playing an important role to improve and to optimize the execution time of these applications. These environments allow us the inclusion of more parallelism inside the node. However, to take advantage of this parallelism is not an easy task, because we have to deal with some problems such as: cores communications, data locality, memory sizes (cache and RAM), synchronizations, data dependencies on the model, etc. These issues are becoming more important when we wish to improve the application’s performance and scalability. Hence, this paper describes an optimization method developed for Systemic Model of Banking Originated Losses (SYMBOL) tool developed by the European Commission, which is based on analyzing the application's weakness in order to exploit the advantages of the multicore. All these improvements are done in an automatic and transparent manner with the aim of improving the performance metrics of our tool. Finally, experimental evaluations show the effectiveness of our new optimized version, in which we have achieved a considerable improvement on the execution time. The time has been reduced around 96% for the best case tested, between the original serial version and the automatic parallel version.

Keywords: algorithm optimization, bank failures, OpenMP, parallel techniques, statistical tool

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Authors: Amna Tariq Shah

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Amid the pandemic, the contemporary landscape has experienced accelerated growth in small business activities and an expanding digital marketplace, further exacerbating the issue of non-compliance among self-employed individuals through aggressive tax planning and evasion. This research seeks to address these challenges by developing strategic tax policies that promote voluntary compliance and improve taxpayer facilitation. The study employs the innovative MINDSPACE framework to examine three psychosocial factors—tax communication, tax literacy, and shaming—to optimize policy responses, address administrative shortcomings, and ensure adequate revenue collection for public goods and services. Preliminary findings suggest that incomprehensible communication from tax authorities drives individuals to seek alternative, potentially biased sources of tax information, thereby exacerbating non-compliance. Furthermore, the study reveals low tax literacy among Australian and Pakistani respondents, with many struggling to navigate complex tax processes and comprehend tax laws. Consequently, policy recommendations include simplifying tax return filing and enhancing pre-populated tax returns. In terms of shaming, the research indicates that Australians, being an individualistic society, may not respond well to shaming techniques due to privacy concerns. In contrast, Pakistanis, as a collectivistic society, may be more receptive to naming and shaming approaches. The study employs a mixed-method approach, utilizing interviews and surveys to analyze the issue in both jurisdictions. The use of mixed methods allows for a more comprehensive understanding of tax compliance behavior, combining the depth of qualitative insights with the generalizability of quantitative data, ultimately leading to more robust and well-informed policy recommendations. By examining evidence from opposite jurisdictions, namely a developed country (Australia) and a developing country (Pakistan), the study's applicability is enhanced, providing perspectives from two disparate contexts that offer insights from opposite ends of the economic, cultural, and social spectra. The non-comparative case study methodology offers valuable insights into human behavior, which can be applied to other jurisdictions as well. The application of the MINDSPACE framework in this research is particularly significant, as it introduces a novel approach to tax compliance behavior analysis. By integrating insights from behavioral economics, the framework enables a comprehensive understanding of the psychological and social factors influencing taxpayer decision-making, facilitating the development of targeted and effective policy interventions. This research carries substantial importance as it addresses critical challenges in tax compliance and administration, with far-reaching implications for revenue collection and the provision of public goods and services. By investigating the psychosocial factors that influence taxpayer behavior and utilizing the MINDSPACE framework, the study contributes invaluable insights to the field of tax policy. These insights can inform policymakers and tax administrators in developing more effective tax policies that enhance taxpayer facilitation, address administrative obstacles, promote a more equitable and efficient tax system, and foster voluntary compliance, ultimately strengthening the financial foundation of governments and communities.

Keywords: individual tax compliance behavior, psychosocial factors, tax non-compliance, tax policy

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Authors: Hongshan Guo

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Ground-source heat pumps provide stable and reliable heating and cooling when designed properly. The confounding effect of the borehole depth for a GSHP system, however, is rarely taken into account for any optimization: the determination of the borehole depth usually comes prior to the selection of corresponding system components and thereafter any optimization of the GSHP system. The depth of the borehole is important to any GSHP system because the shallower the borehole, the larger the fluctuation of temperature of the near-borehole soil temperature. This could lead to fluctuations of the coefficient of performance (COP) for the GSHP system in the long term when the heating/cooling demand is large. Yet the deeper the boreholes are drilled, the more the drilling cost and the operational expenses for the circulation. A controller that reads different building load profiles, optimizing for the smallest costs and temperature fluctuation at the borehole wall, eventually providing borehole depth as the output is developed. Due to the nature of the nonlinear dynamic nature of the GSHP system, it was found that between conventional optimal controller problem and model predictive control problem, the latter was found to be more feasible due to a possible history of both the trajectory during the iteration as well as the final output could be computed and compared against. Aside from a few scenarios of different weighting factors, the resulting system costs were verified with literature and reports and were found to be relatively accurate, while the temperature fluctuation at the borehole wall was also found to be within acceptable range. It was therefore determined that the MPC is adequate to optimize for the investment as well as the system performance for various outputs.

Keywords: geothermal borehole, MPC, dynamic modeling, simulation

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Authors: Chenyi Ma

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Background: The second costliest hurricane in U.S. history, Sandy landed in southern New Jersey on October 29, 2012, and struck the entire state with high winds and torrential rains. The disaster killed more than 100 people, left more than 8.5 million households without power, and damaged or destroyed more than 200,000 homes across the state. Immediately after the disaster, public policy support was provided in nine coastal counties that constituted 98% of the major and severely damaged housing units in NJ overall. The programs include Individuals and Households Assistance Program, Small Business Loan Program, National Flood Insurance Program, and the Federal Emergency Management Administration (FEMA) Public Assistance Grant Program. In the most severely affected counties, additional funding was provided through Community Development Block Grant: Reconstruction, Rehabilitation, Elevation, and Mitigation Program, and Homeowner Resettlement Program. How these policies individually and as a whole impacted housing recovery across communities with different socioeconomic and demographic profiles has not yet been studied, particularly in relation to damage levels. The concept of community social vulnerability has been widely used to explain many aspects of natural disasters. Nevertheless, how communities are vulnerable has been less fully examined. Community resilience has been conceptualized as a protective factor against negative impacts from disasters, however, how community resilience buffers the effects of vulnerability is not yet known. Because housing recovery is a dynamic social and economic process that varies according to context, this study examined the path from community vulnerability and resilience to housing recovery looking at both community characteristics and policy interventions. Sample/Methods: This retrospective longitudinal case study compared a literature-identified set of pre-disaster community characteristics, the effects of multiple public policy programs, and a set of time-variant community resilience indicators to changes in housing stock (operationally defined by percent of building permits to total occupied housing units/households) between 2010 and 2014, two years before and after Hurricane Sandy. The sample consisted of 51 municipalities in the nine counties in which between 4% and 58% of housing units suffered either major or severe damage. Structural equation modeling (SEM) was used to determine the path from vulnerability to the housing recovery, via multiple public programs, separately and as a whole, and via the community resilience indicators. The spatial analytical tool ArcGIS 10.2 was used to show the spatial relations between housing recovery patterns and community vulnerability and resilience. Findings: Holding damage levels constant, communities with higher proportions of Hispanic households had significantly lower levels of housing recovery while communities with households with an adult >age 65 had significantly higher levels of the housing recovery. The contrast was partly due to the different levels of total public support the two types of the community received. Further, while the public policy programs individually mediated the negative associations between African American and female-headed households and housing recovery, communities with larger proportions of African American, female-headed and Hispanic households were “vulnerable” to lower levels of housing recovery because they lacked sufficient public program support. Even so, higher employment rates and incomes buffered vulnerability to lower housing recovery. Because housing is the "wobbly pillar" of the welfare state, the housing needs of these particular groups should be more fully addressed by disaster policy.

Keywords: community social vulnerability, community resilience, hurricane, public policy

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Authors: Sehrish Qayyum

Abstract:

Active military power contention is shifting to economic and cyberwar to retain hegemony. Attuned Pestle analysis confirms that structural stress of hegemon’s power loss drives a containment approach towards caging actions. Ongoing diplomatic, asymmetric, proxy and direct wars are increasing stress hegemon’s power retention due to tangled military and economic alliances. It creates the condition of catalepsy with defective reflexive control which affects the core warfare operations. When one’s own power is doubted it gives power to one’s own doubt to ruin all planning either done with superlative cost-benefit analysis. Strategically calculated estimation of Hegemon’s power game since the early WWI to WWII, WWII-to Cold War and then to the current era in three chronological periods exposits that Thucydides’s trap became the reason for war broke out. Thirst for power is the demise of imagination and cooperation for better sense to prevail instead it drives ashes to dust. Pestle analysis is a wide array of evaluation from political and economic to legal dimensions of the state matters. It helps to develop the Pacification and Security Policy Plan (PSPP) to avoid hegemon’s structural stress of power loss in fact, in turn, creates an alliance with maximum amicable outputs. PSPP may serve to regulate and pause the hurricane of power clashes. PSPP along with a strategic work plan is based on Pestle analysis to deal with any conceivable war condition and approach for saving international peace. Getting tangled into self-imposed epistemic dilemmas results in regret that becomes the only option of performance. It is a generic application of probability tests to find the best possible options and conditions to develop PSPP for any adversity possible so far. Innovation in expertise begets innovation in planning and action-plan to serve as a rheostat approach to deal with any plausible power clash.

Keywords: alliance, hegemon, pestle analysis, pacification and security policy plan, security

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Saif Ullah, Guan Zailin, Xu Xianhao, He Zongdong, Wang Baoxi

Abstract:

Assembly line balancing problem is aimed to divide the tasks among the stations in assembly lines and optimize some objectives. In assembly lines the workload on stations is different from each other due to different tasks times and the difference in workloads between stations can cause blockage or starvation in some stations in assembly lines. Buffers are used to store the semi-finished parts between the stations and can help to smooth the assembly production. The assembly line balancing and buffer sizing problem can affect the throughput of the assembly lines. Assembly line balancing and buffer sizing problems have been studied separately in literature and due to their collective contribution in throughput rate of assembly lines, balancing and buffer sizing problem are desired to study simultaneously and therefore they are considered concurrently in current research. Current research is aimed to maximize throughput, minimize total size of buffers in assembly line and minimize workload variations in assembly line simultaneously. A multi objective optimization objective is designed which can give better Pareto solutions from the Pareto front and a simple example problem is solved for assembly line balancing and buffer sizing simultaneously. Current research is significant for assembly line balancing research and it can be significant to introduce optimization approaches which can optimize current multi objective problem in future.

Keywords: assembly line balancing, buffer sizing, Pareto solutions

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Authors: Ayesha Akbar

Abstract:

Public service delivery has witnessed a significant improvement with the integration of information communication technology (ICT). It not only improves management structure with advanced technology for surveillance of service delivery but also provides evidence for informed decisions and policy. Pakistan’s public sector organizations have not been able to produce some good results to ensure service delivery. Notwithstanding, some of the public sector organizations in Pakistan has diffused modern technology and proved their credence by providing better service delivery standards. These good indicators provide sound basis to integrate technology in public sector organizations and shift of policy towards evidence based policy making. Rescue-1122 is a public sector organization which provides emergency services and proved to be a successful model for the provision of service delivery to save human lives and to ensure human development in Pakistan. The information about the organization has been received by employing qualitative research methodology. The information is broadly based on primary and secondary sources which includes Rescue-1122 website, official reports of organizations; UNDP (United Nation Development Program), WHO (World Health Organization) and by conducting 10 in-depth interviews with the high administrative staff of organizations who work in the Lahore offices. The information received has been incorporated with the study for the better understanding of the organization and their management procedures. Rescue-1122 represents a successful model in delivering the services in an efficient way to deal with the disaster management. The management of Rescue has strategized the policies and procedures in such a way to develop a comprehensive model with the integration of technology. This model provides efficient service delivery as well as maintains the standards of the organization. The service delivery model of rescue-1122 works on two fronts; front-office interface and the back-office interface. Back-office defines the procedures of operations and assures the compliance of the staff whereas, front-office equipped with the latest technology and good infrastructure handles the emergency calls. Both ends are integrated with satellite based vehicle tracking, wireless system, fleet monitoring system and IP camera which monitors every move of the staff to provide better services and to pinpoint the distortions in the services. The standard time of reaching to the emergency spot is 7 minutes, and during entertaining the case; driver‘s behavior, traffic volume and the technical assistance being provided to the emergency case is being monitored by front-office. Then the whole information get uploaded to the main dashboard of Lahore headquarter from the provincial offices. The latest technology is being materialized by Rescue-1122 for delivering the efficient services, investigating the flaws; if found, and to develop data to make informed decision making. The other public sector organizations of Pakistan can also develop such models to integrate technology for improving service delivery and to develop evidence for informed decisions and policy making.

Keywords: data, e-governance, evidence, policy

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Authors: Apurva Patil, Sujatha Srinivasan

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Reducing actuator loads is important for applications in which human effort is required for actuation. The potential benefit of applying spring balancing to rehabilitation devices which work against gravity on a nonhorizontal plane is well recognized, but practical applications have been elusive. Although existing methods provide exact spring balance, they require additional masses or auxiliary links, or all the springs used originate from the ground, which makes the resulting device bulky and space-inefficient. This paper uses a method of static balancing of mechanisms with conservative loads such as gravity and spring loads using non-zero-free-length springs and no auxiliary links. Application of this method to a manually operated swimming pool lift mechanism which lowers and raises the physically challenged users into or out of the swimming pool is presented here. Various possible configurations using extension and compression springs as well as gas spring in the mechanism are compared. This work involves approximate spring balancing of the mechanism using minimization of potential energy variance. It uses the approach of flattening the potential energy distribution over the workspace and fuses it with numerical optimization. The results show the considerable reduction in actuator torque requirement with practical spring design and arrangement. Although the method provides only an approximate balancing, it is versatile, flexible in choosing appropriate control variables that are relevant to the design problem and easy to implement. The true potential of this technique lies in the fact that it uses a very simple optimization to find the spring constant, free length of the spring and the optimal attachment points subject to the optimization constraints. Also, it uses physically realizable non-zero-free-length springs directly, thereby reducing the complexity involved in simulating zero-free-length springs from non-zero-free-length springs. This method allows springs to be attached inside the mechanism, which makes the implementation of spring balancing practical. Because auxiliary linkages can be avoided, the resultant swimming pool lift mechanism is compact. The cost benefits and reduced complexity can be significant advantages in the development of this user-actuated swimming pool lift for developing countries.

Keywords: gas spring, rehabilitation device, spring balancing, swimming pool lift

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Authors: Abdelli Soulaima, Belhadj Besma

Abstract:

The paper examines the choice of the central bank toward the movements of the nominal exchange rate and evaluates its effects on the volatility of the output growth and the inflation. The novel hybrid method of the dynamic stochastic general equilibrium called the DSGE-VAR is proposed for analyzing this policy experiment in a small scale open economy in particular Tunisia. The contribution is provided to the empirical literature as we apply the Tunisian data with this model, which is rarely used in this context. Note additionally that the issue of treating the degree of response of the central bank to the exchange rate in Tunisia is special. To ameliorate the estimation, the Bayesian technique is carried out for the sample 1980:q1 to 2011 q4. Our results reveal that the central bank should not react or softly react to the exchange rate. The variance decomposition displayed that the overall inflation volatility is more pronounced with the fixed exchange rate regime for most of the shocks except for the productivity and the interest rate. The output volatility is also higher with this regime with the majority of the shocks exempting the foreign interest rate and the interest rate shocks.

Keywords: DSGE-VAR modeling, exchange rate, monetary policy, Bayesian estimation

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Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

Abstract:

To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

Procedia PDF Downloads 86