Placement Model Research Articles

Health information management students' work-integrated learning (professional practice placements): Where do they go and what do they do?

Background: Work-integrated learning (WIL) is integral to most health disciplines' profession-qualifying degree programs. Objectives: To analyse the categories, locales and foci of final-year (capstone), health information management professional practice (WIL) placements, 2012-2021, at La Trobe University, Australia. Method: A documentary analysis of 614 placement agency proposals, 2012-2021, interrogated multiple characteristics: agency type, placement (sub-) category (WIL model), project type, agency-required student capabilities, intended learning outcomes. Results: Public hospitals offered 50% of all placements. Medical research/health or disease screening/clinical registries offered 17.8%, incorporating 86.7% of "research-based" placements. Government department offerings were consistently stable; private hospital, primary care and community healthcare offerings declined. The majority (64.8%) of offerings were "project-based," followed by "internship" (28.7%: Health Information Service (14%) and "other" (14.7%)), research-based (4.9%) and other (1.6%). Ninety-nine (16.1%) proposals specified additional, pre-placement skills and capabilities: technical (information technologies, software applications; 58.6% of 99 proposals); working independently (49.5%); communications (written, verbal; 45.5%); targeted interest (38.4%) in "informatics and data quality," "quality and safety," "software development," "coding"; organisational and/or time management skills (29.9%); teamwork skills (20.2%); data analysis skills (18.2%); enthusiasm and/or self-motivation (15.2%). Conclusion: The project-based model for the capstone placement is ideal for preparing health information management students for complex, graduate professional work. Agencies' pre-placement expectations of students (knowledge, technical skills, soft skills) are consistent with findings from the WIL literature and align with course curricula and Australia's Health Information Manager (HIM) Profession-entry Competency Standards. Implications: The findings will strengthen the health information management profession's knowledge base of WIL and inform educators, students and agency supervisors.

Students’ experiences of placements in urban indigenous health contexts: developing a culturally responsive workforce

BackgroundA culturally responsive health workforce is essential to ensure the delivery of culturally safe health services that meet Aboriginal and Torres Strait Islander Peoples needs. In partnership with universities, placement providers play an essential role in creating opportunities for immersive experiences that enable students to develop their cultural responsiveness. This study evaluated students’ experiences of an innovative student placement model embedded within an urban Aboriginal and Torres Strait Islander Community-Controlled Health Organisation.MethodsStudents completed pre and post placement surveys administered using a web-based interface. The surveys involved five-point Likert and open-ended response items exploring students’ perceptions of their knowledge, skills development, awareness and self-development, and overall placement experience. Frequencies were calculated for the variables of interest and compared between pre and post surveys. The sign test for matched pairs was used to calculate differences between pre and post surveys, and a one-sided hypothesis test was utilised to determine if the level of agreement increased from pre to post survey. Qualitative data obtained for seven questions were thematically analysed using Groundwater Method, an Indigenous data analysis technique.ResultsBetween January 1, 2017, and June 30, 2019, 938 students from 32 disciplines were placed within the organisation and its Member services. Survey responses were received from 338 participants pre-placement, and 158 participants post-placement. The matched pre-post group contained 81 students. The results indicate significant positive changes in cultural responsiveness, skills development, awareness, and self-development when comparing pre- and post-placement responses. Students’ overall satisfaction with the quality of their placement was positively associated with their intention to work in Aboriginal and Torres Strait Islander health contexts in the future. Key pre-placement themes included competence, cultural skills, support and fear, and key post-placement themes included expertise, cultural responsiveness, learning environment and challenges.ConclusionsIndigenous-led, regionally coordinated placements in urban Indigenous health contexts can support transformative learning and the development of a culturally responsive workforce. Universities should aim to develop reciprocal relationships with Indigenous-led organisations to support students to develop their cultural responsiveness and improve the provision of culturally safe care for Aboriginal and Torres Strait Islander Peoples. Future research should explore the longer-term impacts of student placements on cultural responsiveness, attitudes, values, and behaviours, as well as the experiences of Aboriginal and Torres Strait Islander Peoples interfacing with university students on placement in urban settings.NoteWe will predominantly use the term ‘Aboriginal and Torres Strait Islander Peoples’, as opposed to ‘Indigenous’ or ‘First Nations’. When the term ‘Indigenous’ is used, it largely relates to government policy - except when referring to Indigenisation of curriculum and Indigenous Knowledges - and where ‘First Nations’ is used, it is in a global context. In addition, we use the term ‘Peoples’ to signify that Aboriginal and Torres Strait Islander Peoples are not one People or Nation, but a collective of Peoples and Nations.

Experiences of Physiotherapy and Occupational Therapy Undergraduates and Placement Educators of Blended Practice-Based Learning

Securing sufficient practice-based learning opportunities for health care students is long-standing and well documented and not unique to the United Kingdom (Beveridge & Pentland 2020; Martin et al., 2004). Increasing Allied Health Professional learners on healthcare programmes and the COVID-19 pandemic have highlighted the need for radical change and innovative ways of working to address placement capacity. Due to the capacity pressures, blended practice-based placements were implemented in partnership with clinical collaboration across the Cheshire and Merseyside region. As this placement model had not been robustly tested, a decision was made to pilot this across different settings and ethical approval was sought and granted, to fully evaluate this approach. The participants consisted of second-year physiotherapy and first- and second-year occupational therapy learners. The aims of the study were to: ascertain the value and experiences of a newly introduced blended placement-based learning through the anonymous collection of data from an online questionnaire (quantitative) and focus groups (qualitative) in response to challenges relating to placement capacity. Initial findings suggest there are benefits for learners including new skill acquisition, such as organisational and adaptability skills, the ability to influence change in patient care and improved clinical reasoning. Challenges for learners include the type and length of project not being comparable to their peers, reduced clinical time, perceived discrepancies in marking during the placement and opportunities to develop confidence. Alternative placement models are required to meet ongoing capacity demands. This work adds to the growing body of placement related literature.

“Blended Placement: A Balancing Act”

The COVID-19 pandemic exacerbated healthcare practice education placement shortages. In Ireland, four occupational therapy programmes developed the blended onsite/offsite practice education placement model to increase placement capacity. Offsite learning included projects, supervision and telehealth, while clinical contact was prioritised when onsite. The aim of this study was to investigate the experiences of practice educators (PEs) who had supervised students using this model. The study used a qualitative interpretive methodology. PEs with experience of supervising a blended placement took part in semi-structured interviews. Reflexive thematic analysis was used to explore themes in the data. PEs felt the model benefitted students’ self-directed learning and independence. The study found that PEs valued the time apart from the student to be able to manage their own workload. Some were concerned about reduced clinical contact and operational challenges. Overall PEs were positive about the model’s use beyond COVID-19. Participants in this study value ongoing use of the blended onsite/offsite placement model. The flexibility of the model for the practice educator is key to its sustainability. Information gathered in this study facilitated the development of a Blended Onsite/Offsite Model (BOOM) Planning Tool, applicable to other health and social care professions. There is a need to promote this model as part of a wider strategy to address placement capacity challenges.

An edge server placement based on graph clustering in mobile edge computing

With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. In this paper, we investigate the issue of edge server placement and user allocation to reduce transmission delay between base stations and servers, and balance the workload of individual servers. To this end, we propose a graph clustering-based edge server placement model by fully considering the constraints such as the distance, coverage area and number of channels of base stations. The model mainly consists of a two-layer graph convolutional network (GCN) component and a differentiable version of K-means clustering component, which transforms the server placement problem into an end-to-end learning optimization problem on a graph. It trains the GCN network to achieve the best clustering results with the expectation of average delay and load balancing as the loss function to obtain the edge server placement and user assignment scheme. We conducted experiments based on the Shanghai Telecom dataset, and the results show the effectiveness of our approach in both latency reduction and load balancing.

Bridging the Research-to-Practice Gap: The Individual Placement and Support Model.

The Research-to-Practice Gap often hinders the translation of effective healthcare interventions from clinical trials to routine care. Individual Placement and Support (IPS), an evidence-based practice designed to help individuals with mental health conditions achieve and maintain employment, has notably bridged this gap. Unlike many interventions that struggle with widespread implementation, IPS has successfully scaled to over 2,000 programs across all U.S. states and 30 other countries. This paper examines the strategies that have facilitated the rapid and extensive adoption of IPS, offering insights into best practices for integrating randomized controlled trial (RCT) findings into everyday clinical settings. Key factors contributing to the success of IPS include conducting RCTs in settings with real-world patients and clinicians, fostering collaboration through the International IPS Learning Community, developing comprehensive implementation materials and a dynamic fidelity scale, and engaging in regular, systematic meetings with stakeholders such as providers, advocates, and policymakers. These approaches have ensured that IPS remains adaptable, responsive to patient needs, and maintains fidelity to its core principles while promoting continuous improvement. The experience with IPS underscores the importance of integrating real-world evidence with clinical practice through ongoing collaboration among all stakeholders. The principles underpinning IPS-real-world application, stakeholder engagement, and adaptability-provide a model that could guide future efforts to close the research-to-practice gap across diverse healthcare settings and interventions.

“They already trusted us a lot”: Allied health students’ experiences of an innovative hospital, service-focussed placement model

Background A service-focussed placement model involves an innovative partnership between service and education providers to a) address service delivery needs and b) facilitate students’ competency development. Services are collaboratively redesigned such that students are positioned as collaborators in service delivery with their learning supported through evidence-informed educational design. This model was implemented in the hospital setting and was found to promote positive patient outcomes. Our study explored whether this model supports student learning. Methods Students’ perception of learning was explored using a multiple-case study design with data collected from 22 semi-structured focus groups and interviews with 64 senior year allied health students (physiotherapy n = 55; occupational therapy n = 9) across six placement settings. Inductive thematic analysis identified aspects of this placement model that facilitated and challenged students’ learning. Results A service-focussed placement model provides a quality student learning experience. Students identified the need to be prepared for independent learning. This placement model promoted their professional development and sense of autonomy through engaging them in early, active learning within a safe learning environment. Conclusions Students perceived that a service-focussed placement model equipped them for the ever-changing complexities and challenges of hospital services.

Development and Validation of a Simulation Model for Ureteral Stent Placement.

Currently, there are no validated training models for cystoscopy with ureteral stent placement. The objectives of this study were to develop and validate a novel endoscopic simulation model for training in ureteral stent placement. A low-cost, low-fidelity training model was developed to simulate ureteral stent placement. Recruited participants were divided into 3 groups: novices (postgraduate year 3/4 gynecology residents), advanced learners (urogynecology and reconstructive pelvic surgery fellows), and experts (urology residents, urogynecology faculty, and urology faculty). Construct validity was measured using de-identified video-recorded performances on the model, which were evaluated by 2 expert reviewers using validated scales (Global Operative Assessment of Laparoscopic Skills [GOALS], Global Rating Scale [GRS]) and procedure-specific metrics. The model was created using a hollow Styrofoam sphere, plastic tubing from a retropubic sling, and a silicone pacifier. Thirty-six surgeons were assessed performing the procedure using the model with cystoscopic equipment. The experts (n = 12) performed significantly better than the advanced learners (n = 17) and novices (n = 7) in total scores (max 75, median [IQR]: 75 [75-75], 61 [56.5-68.5], 45 [43-46], respectively; P < 0.001) and within each individual scale domain. Increasing experience with ureteral stent placement had a significant correlation (P < 0.001) with better performance on the model. A minimum total passing score of 63 was established. On post simulation assessment, most participants "agreed" or "strongly agreed" that the model closely approximates the feel of ureteral stent placement. This ureteral stenting simulation model is easy to construct, affordable, and reproducible. The model is valid and reliable for practicing the procedure in preparation for live surgery.

“Being, Like, Their Parents Throughout Their Lifetime”—House Parents' Perceptions of Their Role in the Family‐Style Group Care

ABSTRACTBased on the concepts in Katz and Kahn's role theory model this qualitative research examines the role perception and experience of house parents in the family‐style group care, a type of out‐of‐home placement model for at‐risk children who cannot reside with their birth families. Semistructured interviews were conducted with 18 house parents (both men and women) to gain insights into their role perception and experience. The findings revolved around three central themes: motivation for choosing the role (search for meaning, previous exposure to the role and childhood experiences); role perception (parental and administrative); and experiencing the role, in terms of positive and negative emotions. The theoretical significance of this research lies in understanding the unique role perception of house parents, distinct from the corporate world. In comprehending the role of house parents, its nature and its impact on the well‐being of individuals in this role, social workers can improve house parent training and contribute to the mental well‐being of children growing up in these settings.

An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a Silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.

Influences on improved confidence among allied health students in working with Australian Indigenous people during a rural placement: a pre-post survey study

BackgroundTogether with addressing social determinants of health, culturally safe healthcare provision is essential for closing the health outcomes gap experienced by Aboriginal and Torres Strait Islander (Indigenous) Australians. Rural placements potentially provide students of the health professions with opportunities to enhance their knowledge and skills regarding cultural safety. We used rural placements data systematically collected from allied health students, including commencement- and end-of-placement questionnaire responses, to investigate the determinants of confidence in working with Indigenous people.MethodsThe study comprised data from all students who provided survey data at both commencement and end of their first placement directly supervised by the administering University Department of Rural Health during the period 2019–2022. Five-point ordered responses to the question ‘How confident do you feel about working with Aboriginal people?’ were used to assess student and placement-related determinants of confidence (Confident/Very confident versus other) at baseline and increased confidence (≥ 1 point) during the placement using crude and adjusted multivariable robust Poisson regression.ResultsParticipating students (N = 489) were from diverse allied health disciplines (including pharmacy n = 94, 19.2%; chiropractic n= 66, 13.5%; physiotherapy n= 65, 13.3%; social work n = 59, 12.1%; and occupational therapy 58, 11.9%). Confidence in dealing with Aboriginal people was lower at commencement among females compared with males (adjusted relative risk [aRR] 0.65; 95% confidence interval [CI] 0.53–0.80), and higher among students of Australian rural origin compared with others (aRR 1.49; CI 1.22–1.83) and those who reported previous experience working with Indigenous people compared with those reporting none (aRR 1.40; CI 1.14–1.72). Placement attributes associated with increased confidence working with Indigenous people between placement commencement and end were interaction with Indigenous people within the placement (aRR 2.32; CI 1.24–4.34), placement model reflecting more structured academic supervision (aRR 1.18; CI 1.02–1.37), and placement length (aRR per additional day 1.002; CI 1.001–1.004). These associations were robust to modelling that accounted for a ceiling effect on increased confidence.ConclusionsWhile influenced by students’ demographic attributes and prior experiences, confidence of allied health students in working with Indigenous people is enhanced during rural placements, particularly through direct contact with Indigenous people.

A Hybrid Strategy Two‐Dimensional Concrete Aggregate Filling Algorithm

ABSTRACTAccurate and highly efficient construction of numerical concrete models with different gradations is important to analyze the acoustic properties of ultrasonic waves in concrete. The random aggregates‐based method is a classic approach to meso‐structural modeling of concrete and is particularly suitable for generating models with low‐density aggregates. However, when facing the simulation requirements of concrete models with large volumes, small particle sizes, and high aggregate content, traditional random aggregates‐based methods encounter challenges in satisfying aggregate packing density and modeling efficiency. In order to improve the efficiency of aggregate placement in modeling concrete meso‐models, we proposed a hybrid‐strategy point cloud placement algorithm that combines the re‐placement and sedimentation strategies. Then, the algorithm's effectiveness in improving the modeling efficiency is verified by reconstruction experiments on random aggregate models with different aggregate grades. Finally, the model's reliability and validity is further confirmed by simulation analysis of the ultrasonic attenuation characteristics of the model. The results show that the algorithm can rapidly generate more than 75% aggregate packing density concrete specimens and significantly outperforms similar algorithms regarding aggregate placement efficiency and model generation time. In addition, the model generated by the proposed algorithm can accurately simulate the attenuation characteristics of ultrasonic waves in concrete.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Taxonomy of optimization algorithms combined with CNN for optimal placement of virtual machines within physical machines in data centers

Energy management in datacenters is a major challenge today due to the environmental and economic impact of increasing energy consumption. Efficient placement of virtual machines in physical machines within modern datacenters is crucial for their effective management. In this context, five algorithms named CNN-GA, CNN-greedy, CNN-ABC, CNN-ACO and CNN-PSO, have been developed to minimize hosts’ power consumption and ensure service quality with relatively low response times. We propose a comparative approach between the developed algorithms and other existing methods for virtual machine placement. The algorithms use optimization algorithms combined with Convolutional Neural Networks to build predictive models of virtual machine placement. The models were evaluated based on their accuracy and complexity to select the optimal solution. The necessary data is collected using the CloudSim Plus simulator, and the prediction results were used to allocate virtual machines according to the predictions of the models. The main objective of this research is to optimize the management of Information Technology resources within datacenters. This is achieved by seeking a virtual machine placement policy that minimizes hosts’ power consumption and ensures an appropriate level of service for users' needs. It considers the imperatives of sustainability, performance, and availability by reducing energy consumption and response times. We studied six scenarios under specific constraints to determine the best model for virtual machines’ placement. This approach aims to address current challenges in energy management and operational efficiency.

Enhancing disturbance rejection in boost converter for CPL: A controller design approach with partial pole placement and approximate model matching

In DC microgrids, the cascade operation of the DC-DC converters leads to a constant power load fed by the boost converter, resulting in unstable behavior in addition to the non-minimum phase dynamics. An enhanced load disturbance rejection control scheme based on partial pole placement and an approximate model matching technique is proposed. This approach is inspired by the well-known direct synthesis method to achieve optimal load disturbance rejection performance. The effectiveness of the proposed control scheme has been validated through hardware implementation, demonstrating its capability in setpoint tracking and load disturbance rejection under variations in load power and input voltage. The robust stability of the suggested scheme has been investigated through the Kharitonov theorem and Monte-Carlo simulation, considering different performance matrices. The suitability of the presented scheme has been established by analyzing the comparative performance of recently reported works. The proposed controller reduces peak overshoot and settling time by ∼50%, handles system uncertainties up to 75%, and outperforms FOPID controllers tuned with particle swarm optimization, queen bee genetic algorithm, and chaos game optimization methods.

Antecedents &amp; After-Effects of Content Creator-Based Product Placement: Empirical Insights from Bangladesh

Purpose: The emergence of content creators in social media is a comparatively new phenomenon of interest which has rightfully attracted the digital marketers. As they have discovered a new way to connect with the audience it has inevitably led to the advent of content creator-based product placement. The study is designed to propose and test a creator product placement model uncovering the factors determining audiences&amp;rsquo; attitude and subsequent behavioral responses. Design/Methodology: The study employed a single-cross sectional design administering a structured questionnaire-based survey on a sample of 385 respondents. A conceptual model was developed showing the hypothesized relationships among specified variables which was validated using Structural Equation Modeling. Findings: Content preference, relatability, quality, attitude toward creator, parasocial relationship, fit between content and brand and fit between creator and brand are the significant determinants of the attitude toward creator product placement. It in turn influences attitude toward brand and subsequent purchasing intention. Practical Implication: The proposed framework will aid social media marketers to formulate effective creator marketing strategies to elicit desired behavioral response from the audience. It also enriches the existing body of literature of creator marketing. Originality/Value: The study endeavors to come up with a comprehensive model to understand the behavior of the target audience toward creator product placement which is relatively unexplored and unidentified.

Comparative Analysis of Independent Reviewer-Rated and Self-Rated Fidelity Scores in Individual Placement and Support Programs: Repeated Cross-Sectional Surveys.

Fidelity assessments can contribute to maintaining the adherence to the individual placement and support (IPS) model, which enhances vocational outcomes for individuals with mental illness worldwide. While independent reviews are standard, self-assessments could broaden the implementation of IPS. This study aimed to evaluate reviewer-rated and self-rated fidelity assessments using the Japanese version of the Individualized Supported Employment Fidelity Scale (JiSEF), and to compare the two assessment methods in terms of their correlations with employment outcomes in Japan. Over the 3-year research period, fidelity assessments were conducted by independent reviewers and trained program staff members across 26 programs, totaling 58 assessments. Analyses involved kappa statistics for item-level comparison, the intra-class correlation coefficient (ICC) and paired t-testfor the overall fidelity scores, and Pearson's correlations to examine the relationship between the fidelity scores and program-level employment outcomes. Most individual JiSEF items demonstrated fair to good reliability between reviewer-rated and self-rated assessments. The ICC for the overall JiSEF scores between the two assessment methods was 0.756, yet the distribution of self-rated scores was more scattered compared with that of reviewer-rated scores. The mean total scores from self-assessments were significantly lower than those from reviewer assessments (t = 2.072, P = 0.043). While both sets of scores correlated significantly with employment rates (r = 0.640, P < 0.001 for reviewer assessments; r = 0.325, P = 0.013 for self-assessments), the correlation was stronger for reviewer ratings (z = 2.207,P = 0.027). Self-rated fidelity assessments offer several benefits. However, since independent reviews had a more normal distribution and higher correlation with employment outcome, they should remain the priority in fidelity assessments within the Japanese IPS framework.

Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences.

Experiences of individuals with serious mental disorders in regular employment through the Individual Placement and Support model.

This study shows the perspective, meaning and satisfaction perceived by people with Serious Mental Disorders during their experiences in regular employment. A mixed qualitative-quantitative methodology was used, applying semi-structured interview as qualitative information collection tool and the Indiana Job Satisfaction Scale as quantitative tool. The study period was from January 2021 to December 2022. A purposive sampling was performed with a sample of 24 people with Serious Mental Disorders who had obtained a job through an Individual Placement and Support (IPS) program during the study period. Semi-structured interview and the Indiana Job Satisfaction Scale were applied to this Serious Mental Disorder workers' sample. The Indiana Job Satisfaction Scale was also applied to a group of 24 workers without mental disorders in the same working conditions who served as control group. The results of the analyses of the Serious Mental Disorder workers' narratives show that perception of work experience is conditioned by individual, environmental and social predictors, as well as external factors as determining variables. Quantitative results obtained by the Indiana Job Satisfaction Scale reveal levels of job satisfaction resembling those of the rest of workers without Serious Mental Disorder. These findings reinforce the significance of employment in the recovery process for individuals with Serious Mental Disorders and emphasize the importance of understanding the subjective meaning individuals attribute to their work experiences.

Multi-objective Mathematical Model for Optimal Wind Turbine Placement in Wind Farm under Uncertainty

The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90% of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II.