Placement Model Research Articles

A Robust Sparse Sensor Placement Strategy Based on Indicators of Noise for Ocean Monitoring

A well-performing data-driven sparse sensor deployment strategy is critical for marine monitoring systems, as it enables the optimal reconstruction of marine physical quantities with fewer sensors. However, ocean data typically contain substantial amounts of noise, including outliers (incomplete data) and inherent measurement noise, which heightens the complexity of sensor deployment. Therefore, this study optimizes the sparse sensor placement model by establishing noise indicators, including small noise weight and large noise weight, which are measured by entropy to minimize the prediction bias. Building on this, a robust sparse sensor placement algorithm is proposed, which utilizes the block coordinate update (BCU) iteration method to solve the function. During the iterative updating process, the proposed algorithm simultaneously updates the selection matrix, reconstruction matrix, and noise matrix. This allows for effective identification and mitigation of noise in the data through evaluation. Consequently, the deployed sensors achieve superior reconstruction performance compared to other deployment methods that do not incorporate noise evaluation. Experiments are also conducted on datasets of sea surface temperature (SST) and global ocean salinity, which demonstrate that our strategy significantly outperforms several other considered methods in terms of reconstruction accuracy while enabling autonomous sensor deployment under noisy conditions.

A three-level robust scenario-based model for resilience-oriented placement of remote-controlled switches in distribution networks

This paper introduces an innovative strategy to improve the resilience of Distribution Networks (DN) following natural disasters. The approach involves a multi-stage model for the strategic placement of Remote-Controlled Switches (RCS) to expedite fault isolation and service restoration. The model integrates various resilience enhancement techniques, such as network reconfiguration and microgrid formation, throughout the pre-disaster, disaster progression, and post-disaster phases. To mitigate uncertainties related to line failures and ensure planning robustness, a novel three-level Robust Scenario-based Model (RSM) is introduced. The RSM optimizes the strategic RCS placement to enhance System Performance Criteria (SPC) in response to anticipated line failure scenarios, considering individual line vulnerabilities and disaster severity. Subsequently, the model identifies the most critical failures from each predicted scenario based on RCS placements and then recalibrates the placements to enhance DN resilience against both anticipated and critical failures. The RSM is formulated as a mixed-integer linear programming problem to maximize SPC by allocating resources optimally to serve maximum weighted loads while adhering to structural and operational constraints. The effectiveness and validity of the proposed model are demonstrated through case studies conducted on a modified IEEE 33-bus test system and a modified real DN.

Decision‐chain integrity and sustainment of the individual placement and support model: An embedded single case study in Finland

AbstractThe individual placement and support (IPS) model is an evidence‐based practice proven effective in aiding individuals with mental disorders to achieve and maintain competitive employment. This study examines how policy and structural factors are perceived to influence the model's sustainment as routine practice after a national development project in Finland. We aim to identify critical dimensions related to decision‐making at the national, regional, and local organisational levels. We used a qualitative single‐case approach, analysing 21 stakeholder interviews and nine administrative documents. We conducted abductive thematic analyses. We report the results in compliance with the COREQ guideline. Our analysis identified three main external contextual influences affecting service sustainment: (1) System‐Level Planning—the coherence of formulation and execution of facilitative strategies across different administrative levels; (2) Political Decision Making—the recognition of inception points of policy change and the ability to set the IPS model on a broader policy agenda; and (3) Stakeholder Engagement and Collaboration—the need for mutual goals and collaborative platforms. Strategic planning, governance decisions, and stakeholder involvement affect local‐level decisions on the IPS model's sustained implementation. This research introduces the concept of ‘Decision‐Chain Integrity’, highlighting the alignment of strategies and their implementation across different levels of administration as a pivotal factor in producing sustained change in the service system.

“This should be a compulsory placement for all nursing students”: An evaluation of pre-registration nursing students’ perceptions of learning on a mental health clinical placement

AimTo conduct a longitudinal exploration of pre-registration nursing students’ perceptions of their learning on an immersive mental health clinical placement grounded in learning from people with a lived experience of mental illness (otherwise known as consumers). BackgroundEnriching mental health clinical learning environments are crucial for positive mental health nursing outcomes. Though non-traditional clinical placement (i.e. non-hospital-based) models effectively increase student learning in a range of domains, little is known about the specific features of contemporary non-traditional placement settings that may be supporting student learning over time. DesignA survey design in the form of a standardised evaluation tool with additional qualitative response questions was used to examine nursing students’ perceptions of learning whilst on a non-traditional clinical placement over a 5-year period. Non-traditional placement settings are alternative placement options to traditional inpatient/community mental health settings. The TREND Statement Checklist was adhered to. MethodsSecond- and third-year students studying a Bachelor of Nursing (N = 753) from eight Australian Universities completed a Student Placement Feedback Survey between 2019 and 2023. Data were collected via an evaluation survey including 7-items (rated on a 5-point agreement scale) and three free-response questions. Quantitative and qualitative responses were analysed over all observations and compared between the five years of student evaluations. ResultsAcross five years, the immersive mental health placement was consistently rated by students as a highly valuable learning experience. Utilizing a Multivariate Analysis-of-variance (MANOVA) for the quantitative component revealed that student ‘learning from lived experience’ remained uniformly high and steady throughout 2020–2023. This was despite disrupted learning that ceased face-to-face tuition caused by the COVID-19 pandemic. An increase in ‘student enthusiasm for nursing’ was identified after the return to face-to-face learning. Qualitative analysis identified a greater need for preparedness prior to attending the placement and wellbeing support amongst students. ConclusionsOver the five years, pre-registration nursing students report clinical skill improvement and enhanced knowledge following the immersive mental health placement alongside an increased desire for further skill development. Learning from people’s lived experience of mental illness and specialised facilitators was valuable for student learning outcomes. Increased support is needed for student mental health vulnerabilities and wellbeing ahead of clinical placements. Further research is recommended on the aspects of non-traditional clinical placements that may be protective for student learning.

Paramedic student and preceptor experiences of a clinical facilitator model during ambulance clinical placements: A qualitative study

Introduction: Ambulance service clinical placements are fundamental to paramedicine student education, but the quality and safety of these placements can be highly variable. Inspired by positive results from a nursing facilitator model, this study reports on a collaboration between an Australian university and ambulance service that introduced a paramedic clinical facilitator for undergraduate paramedic students during their ambulance clinical placements. This article describes the experiences of a clinical facilitator model for paramedicine students and their preceptors during the study period. Methods: This study follows an exploratory qualitative research methodology. After implementation of the paramedic clinical facilitator model, two focus groups with paramedicine student participants and two semi-structured interviews with their paramedic preceptors were conducted. Intimate observations were recorded in a reflexive logbook by the facilitator, which was kept for data analysis. Purposive sampling was used to recruit participants, and thematic analysis was used to code data and conceptualise themes. Results: Three broad overarching themes were conceptualised from the data: 1) increased educational opportunities, 2) improved clinical placement management and 3) greater student support and welfare. Both paramedicine students and paramedic preceptors felt that the paramedic clinical facilitator model improved the quality of ambulance clinical placements for undergraduate paramedicine students. Conclusion: This study suggests that a paramedic clinical facilitator model improved the safety and quality of ambulance clinical placement experience for paramedicine students and preceptors. Universities and ambulance services could consider implementing a paramedic facilitator model for ambulance clinical placements in their local contexts.

Simulation-driven design of smart gloves for gesture recognition

Smart gloves are in high demand for entertainment, manufacturing, and rehabilitation. However, designing smart gloves has been complex and costly due to trial and error. We propose an open simulation platform for designing smart gloves, including optimal sensor placement and deep learning models for gesture recognition, with reduced costs and manual effort. Our pipeline starts with 3D hand pose extraction from videos and extends to the refinement and conversion of the poses into hand joint angles based on inverse kinematics, the sensor placement optimization based on hand joint analysis, and the training of deep learning models using simulated sensor data. In comparison to the existing platforms that always require precise motion data as input, our platform takes monocular videos, which can be captured with widely available smartphones or web cameras, as input and integrates novel approaches to minimize the impact of the errors induced by imprecise motion extraction from videos. Moreover, our platform enables more efficient sensor placement selection. We demonstrate how the pipeline works and how it delivers a sensible design for smart gloves in a real-life case study. We also evaluate the performance of each building block and its impact on the reliability of the generated design.

Probability analysis of maximum occupation in models of particle placement to cells

Probability analysis of maximum occupation in models of particle placement to cells

Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults

Mediolateral gait stability can be maintained by coordinating our foot placement with respect to the center-of-mass (CoM) kinematic state. Neurological impairments can reduce the degree of foot placement control. For individuals with such impairments, interventions that could improve foot placement control could thus contribute to improved gait stability. In this study we aimed to better understand two potential interventions, by investigating their effect in neurologically intact individuals. The degree of foot placement control can be quantified based on a foot placement model, in which the CoM position and velocity during swing predict subsequent foot placement. Previously, perturbing foot placement with a force-field resulted in an enhanced degree of foot placement control as an after-effect. Moreover, timed muscle vibration enhanced the degree of foot placement control whilst the vibration was applied. Here, we replicated these two findings and further investigated whether Q1) timed muscle vibration leads to an after-effect and Q2) whether combining timed muscle vibration with force-field perturbations leads to a larger after-effect, as compared to force-field perturbations only. In addition, we evaluated several potential contributors to the degree of foot placement control, by considering foot placement errors, CoM variability and the CoM position gain (βpos) of the foot placement model, next to the R2 measure as the degree of foot placement control. Timed muscle vibration led to a higher degree of foot placement control as an after-effect (Q1). However, combining timed muscle vibration and force-field perturbations did not lead to a larger after-effect, as compared to following force-field perturbations only (Q2). Furthermore, we showed that the improved degree of foot placement control following force-field perturbations and during/following muscle vibration, did not reflect diminished foot placement errors. Rather, participants demonstrated a stronger active response (higher βpos) as well as higher CoM variability.

Accurate Surge Arrester Modeling for Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte Carlo–Particle Swarm Optimization Algorithm

The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.

Supported employment interventions with people who have severe mental illness: Systematic mixed-methods umbrella review.

Primary and review studies show that supported employment interventions showed promise in assisting people with severe mental illness (SMI) in achieving successful employment and health-related outcomes. This umbrella review synthesises evidence from across review studies on supported employment interventions for individuals with SMI, to identify key findings and implementation challenges in relation to five key outcomes: (1) employment, (2) quality of life, (3) social functioning, (4) clinical/service utilisation, and (5) economic outcomes. A systematic search of eleven databases and registers (CINAHL, Cochrane, EmCare, JBI EBP, ProQuest, PsycINFO, PubMed, Scopus, and Web of Science, and Prospero and Campbell) was conducted to identify meta-analyses and systematic reviews on supported employment interventions for individuals with SMI, peer reviewed and published in English. Quality assessment and data extraction were performed using standardised Joanna Briggs Institute (JBI) tools. A mixed-methods synthesis approach was employed to integrate both quantitative and qualitative evidence. The synthesis of 26 review studies primarily focused on the Individual Placement and Support (IPS) model among various supported employment interventions. Overall, combining supported employment with targeted interventions such as neurocognitive therapy and job-related social skill training showed a positive effect on employment (including job retention) and non-employment outcomes (e.g., health, quality of life, social functioning) relative to standard forms of supported employment for people with SMI. Contextual factors (intervention fidelity, settings, systemic barriers) were important considerations for intervention implementation and effectiveness. Significant overlap of primary studies across 26 review studies exposed considerable variations in interpretation and conclusions drawn by authors, raising questions about their reliability. High volume of overlap reporting from the USA on IPS interventions in review studies is likely to have biased perceptions of effectiveness. There is no one-size-fits-all solution for supporting individuals with SMI in obtaining and maintaining employment. Tailoring strategies based on individual needs and circumstances appears crucial to address the complexity of mental health recovery. We propose creating centralised registries or databases to monitor primary studies included in reviews, thus avoiding redundancy. This umbrella study was registered with PROSPERO (No. CRD42023431191).

Effects of a competence-based approach for clerkship teaching under alternating clinical placements: An explanatory sequential mixed-methods research

Background It is unclear whether alternating placements during clinical clerkship, without an explicit emphasis on clinical competencies, would bring about optimal educational outcomes. Methods This is an explanatory sequential mixed-methods research. We enrolled a convenience sample of 41 eight-year programme medical students in Sun Yat-sen University who received alternating placements during clerkship. The effects of competence-based approach (n = 21) versus traditional approach (n = 20) to clerkship teaching were compared. In the quantitative phase, course satisfaction was measured via an online survey and academic performance was determined through final scores on summative assessment. Then, in the qualitative phase, students were invited for semi-structured interviews about their learning experiences, and the transcripts were used for thematic analysis. Results Quantitative findings showed that students in the study group rated high course satisfaction and performed significantly better in their final scores compared with those in the control group. Qualitative findings from thematic analysis showed that students were relatively neutral about their preference on placement models, but clearly perceived, capitalised, and appreciated that their competencies were being cultivated by an instructor who was regarded as a positive role model. Conclusion A competence-based approach to clerkship teaching resulted in better course satisfaction and academic performance, and was perceived, capitalised, and appreciated by students.

Assessment of a Second-Order Multi-Problem Factor Model Involving Sexual Partners, Marijuana Use, and Post-Arrest Placement Among Persistent Male Adolescent Delinquents

ABSTRACT Problem-behavior theory suggests problem behaviors (e.g. substance use and delinquency) cluster among adolescents. This study explored a second-order problem behavior model of marijuana use, sexual partners, and post-arrest juvenile justice system placement among male adolescents over three waves. Results confirmed a second-order latent factor of problem behaviors, with male adolescents persistently involved in the juvenile justice system experiencing problems with marijuana use, risky sexual behavior, and increased severity of post-arrest placement. Further, older, Hispanic, African American, and urban persistent male delinquents were more likely to demonstrate continued problem behaviors. We discuss implications for future research and practice.

Experiences of online music therapy placements in response to COVID-19 in Melbourne, Australia: Pedagogical considerations for supporting student learning

ABSTRACT Introduction At the beginning of the COVID-19 pandemic, student placements in the Master of Music Therapy at the University of Melbourne were rapidly redesigned to create alternative online opportunities. Two online placement models; a simulated practicum (or SimPrac) and telehealth placement; were arranged. With little previous literature to draw upon, data was gathered to explore student and supervisor experiences of online placements and develop understanding of pedagogical considerations to inform future planning. Methods Quantitative data were collected via an adapted Satisfaction with Simulation Experience Scale (SSES) and qualitative data via open-ended survey questions and semi-structured focus group interviews. Results SSES results indicated high satisfaction from all cohorts with the online placement experience. Qualitative analysis led to the development of four pedagogical considerations for supporting student learning: Respond to Context with Creativity and Flexibility; Scaffold Student Learning; Foster Peer Relationships; and Supplement Online Learning. Discussion Findings aligned with recent scholarship in the wider field of Work Integrated Learning (WIL) and indicate a need for questioning prevailing modes of music therapy placement delivery in line with the contemporary context. While online placements were not always understood to provide authentic music therapy experiences, this perspective may need further contemplation given the ongoing need for online music therapy service provision.

Pass or fail: Teachers’ experience of assessment of postgraduate critical care nursing students’ competence in placement. A qualitative study

BackgroundLearning in placement is essential to postgraduate critical care nursing students’ education. Assessment of students’ competence in placement is important to ensure highly qualified postgraduate critical care nurses. The placement model applied in Norway involves students being assessed by a preceptor in practice and a teacher from the university. The teacher has a more distant role in placement, and the aim of this study was to explore how the teachers experience the assessment of postgraduate critical care nursing students’ competence in placement. Additionally, to explore the content of assessment documents used for postgraduate critical care nursing placement education in Norway.MethodsThis study has a qualitative design with main data collection from individual interviews with 10 teachers from eight universities and colleges in Norway. Additionally, we performed a document analysis of assessment documents from all 10 universities and colleges providing postgraduate critical care nursing education in Norway. We followed the Consolidated Criteria for Reporting Qualitative Research.ResultsThe teachers experienced the assessment of postgraduate critical care nursing students’ competence in placement as important but complex, and some found it difficult to determine what critical care nursing competence is at advanced level. A thematic analysis resulted in one main theme: “Teacher facilitates the bridging between education and practice.” Furthermore, three themes were identified: “Assessment based on trust and shared responsibility”; “The teacher’s dual role as judge and supervisor”; and “A need for common, clear and relevant assessment criteria”.ConclusionsTeachers have a key role in placement as they contribute to the bridging between education and practice by providing valuable pedagogical and academic input to the assessment process. We suggest that more teachers should be employed in joint university and clinical positions to enhance the collaboration between practice and education. Clear and relevant assessment criteria are essential for providing assessment support for both students and educators. Education and practice should collaborate on developing assessment criteria. Further, there is a need to collaborate on developing, both nationally and internationally, common, clear, relevant and user-friendly assessment tools.

Finite element model updating and response prediction of a frame structure based on optimal sensor placement

This paper proposes an approach for finite element (FE) model updating and response prediction of frame structures based on optimal sensor placement (OSP), which integrates sensor placement optimization, mode expansion, and model updating techniques. Firstly, sensor optimization layout and modal testing analysis are conducted on a bolted laboratory frame structure. The covariance-driven stochastic subspace identification (SSI-COV) method identifies the real-world structure’s natural frequencies, modal damping, and mode shapes. Secondly, the complete mode shapes are expanded using the measured incomplete modal data from the limited number of sensors. Thirdly, a multi-objective function based on frequency and mode shapes is established to adjust the parameters of the FE model. This ensures that the updated model accurately represents the dynamic properties of the actual structure within a specific frequency range. Finally, the Rayleigh damping of the frame structure is estimated, and the damping matrix is assembled to enhance the accuracy of dynamic response prediction in the updated model. By comparing the response prediction results of the updated FE model with and without considering the updated damping effects to the measurement data of the real-world structure, it is demonstrated that the proposed method considering updated damping effects can more effectively predict the structural response.

CRAMP: Clustering-based RANs association and MEC placement for delay-sensitive applications

With advancements in networking technology and ubiquitous computing, there has been a significant increase in the number of edge devices and delay-sensitive applications. To facilitate efficient processing, mobile edge computing (MEC) technology provides resources through MEC servers, which are deployed at the radio access networks (RANs) of 5G networks. However, these MEC servers possess a limited amount of resources, making their effective management of these resources a critical challenge. This is due to the uneven distribution of resource utilization, where some resources become overutilized while others remain underutilized. Addressing the issue above while simultaneously satisfying user requirements for delay-sensitive applications poses a significant challenge at the edge. In this paper, we propose a clustering-based efficient RANs association and MEC server placement model to tackle this challenge. Our primary objective is to minimize MEC server deployment costs while ensuring that the delays of these applications are effectively managed. We propose a greedy algorithm called the clustering-based radio access networks association and mobile edge computing placement (CRAMP) algorithm, which determines the optimal location of MEC servers to associate with RANs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches regarding cost efficiency and delay management.

Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.

Towards definitive categories for online video game money laundering

PurposeThis paper aims to investigate how best to classify money laundering through online video games (i.e. virtual laundering). Currently, there is no taxonomy available for scholars and practitioners to refer to when discussing money laundering through online video games. Without a well-defined taxonomy it becomes difficult to reason through, formulate and implement effective regulatory measures, policies and security controls. As such, efforts to prevent and reduce virtual laundering incidence rates are hampered.Design/methodology/approachThis paper proposes three mutually exclusive virtual laundering categorizations. However, instead of fixating on the processes undergirding individual instances of virtual laundering, it is argued that focusing on the initial locale of the illicit proceeds provides the appropriate framing within which to classify instances of virtual laundering. Thus, the act of classification becomes an ontological endeavour, rather than an attempt at elucidating an inherently varied process (as is common of the placement, layering and integration model).FindingsA taxonomy is proposed that details three core virtual laundering processes. It is demonstrated how different virtual laundering categories have varied levels of associated risk, and thus, demand unique interventions.Originality/valueTo the best of the authors’ knowledge, this is the first taxonomy available in the knowledge base that systematically classifies instances of virtual laundering. The taxonomy is available for scholars and practitioners to use and apply when discussing how to regulate and formulate legislation, policies and appropriate security controls.

A MILP model for optimal switch placement to enhance power distribution system performance in normal and hurricane conditions

Severe hurricanes can inflict significant damages in the order of several times the common network failures on both the distribution companies and consumers. Network reconfiguration and condition-based switching are the common actions for enhancing distribution network performance in both normal and emergency conditions. In this paper, a novel approach is presented for simultaneous improvement in resiliency, reliability, and power losses. A practical strategy is proposed by considering the coordination between load restoration and repair of damaged lines in optimal switch placement problems. The use of both remote-controlled switches (RCS) and manual switches (MS) as well as considering practical constraints related to line repair and load restoration leads to the increased number of decision variables and consequently the degraded performance of meta-heuristic methods to obtain the global optimum solutions, especially for larger scale networks. Therefore, the proposed approach uses the exact method of MILP to solve the optimization problem. In this context, the uncertainty related to damage status and repair time of lines during High-Impact Low-Probability (HILP) events is the main challenge in linear modeling of the problem for which new methods are provided in the study. The programming is coded in MATLAB and tested on a modified IEEE 33-bus network.

Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning

The transition to sustainable transportation is imperative in mitigating environmental impacts, with electric vehicles (EVs) at the forefront of this shift. Despite their environmental benefits, the global adoption of EVs is curtailed by challenges such as nascent battery technology, high costs, and insufficient charging infrastructure. This study addresses the optimizing electric vehicle charging station (EVCS) locations as a critical step toward enhancing EV adoption rates. Thus, establishing efficient charging stations is critical to meet the increasing demand. By integrating location modeling with demand forecasts and market penetration, we propose a comprehensive approach to determine optimal locations and capacities for EVCS. Firstly, review existing literature, highlighting the significance of facility location models in optimizing EV charging infrastructure and identifying gaps in addressing demand and market penetration. Our methodology uses a genetic algorithm to solve the p-median problem for location selection and Arena 14 simulation software to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that our model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. This study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system.