11370 research outputs found
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Evolutionary learning driven load forecasting and demand response management model for smart grid
Full text linkThe smart meter data generated from the customers' usage help utility providers to manage and control the electricity distribution among the customers for reliable services in the smart grid. Demand response management is one of the important applications of the grid that helps smart meter customers to potentially reduce their consumption and balance the load in the entire grid. For the demand response management in the smart grid, a multi-objective problem (MOP) is formulated considering three objectives, i.e., power scarcity, wastage and load factor. These objectives are computed by the newly proposed load forecasting neural network that precisely predicts upcoming loads on the demand side. The weights of the neural network are optimized by hybrid learning, which comprises evolutionary and Adam optimization algorithms. Furthermore, MOP is efficiently solved by the non-dominated sorting genetic algorithm (NSGA-III). The experimental results show the superiority of the proposed work over the state-of-the-art works in terms of load forecasting and management. The forecasting accuracy of the proposed work is achieved up to 96.30% and root mean squared error up to 0.1367 on the Open Energy Data Initiative dataset provided by the National Renewable Energy Laboratory of the United States
A comprehensive review of robotics advancements through imitation learning for self-learning systems
Full text linkIn recent years, robotics and artificial intelligence (AI) have witnessed significant growth, particularly in self-learning systems. This paper examines the remarkable progress made in this area, with a particular focus on the utilisation of imitation learning. Self-learning robotics systems have demonstrated the autonomous acquisition of new skills, making them highly adaptable and versatile. Imitation learning is a crucial technique that allows robots to gain knowledge from human demonstrations. This paradigm allows machines to learn and replicate human actions, thus enhancing the capabilities of self-learning robotic technology. The primary objective of this research was to investigate the potential of imitation learning and evaluate its impact on the advancement of self-learning robotics. This paper provides a comprehensive overview of self-learning robotic systems using imitation learning, examining the foundational concepts, essential methodologies, and various applications in this intriguing area. Furthermore, we highlight recent developments, discuss current trends, and outline potential research initiatives to guide the continued development of self-learning robotic systems using imitation learning. This review aims to contribute to the evolving landscape of autonomous robotics by consolidating knowledge, identifying challenges, and fostering further innovation in the pursuit of intelligent self-learning machines
A personality-informed candidate recommendation framework for recruitment using MBTI typology
Full text linkIn many developing regions, recruitment still relies heavily on traditional methods that often ignore the importance of aligning a candidate’s personality with the job role. This mismatch can lead to poor performance, dissatisfaction, and high turnover. To address this, the study presents a personality-aware recommendation system that combines the Myers–Briggs Type Indicator (MBTI) with machine learning to support smarter hiring decisions. The system is tailored for the South Asian job market and includes two main components: a web-based MBTI assessment for applicants and a dashboard for HR professionals powered by a XGBoost classifier. This model was trained on a dataset correlating applicant profiles and the flagged preferences of MBTI with the job. Experience and the number of skills, education level, and encoded MBTI types were the key features, and the SMOTE method was employed to balance the dataset. The model attained an accuracy of 74.30%, having balanced precision and recall measures. It was also discriminative, the ROC AUC was 0.84, and the precision–recall AUC was 0.85. One example of utilizing the Software Developer position in real life demonstrated the success of the system to filter and rank candidates at the same time according to both technical and personality-specific criteria. Overall, this study emphasizes the worth of combining insights from psychological profiling with machine learning in order to develop a more holistically, fair, and efficient hiring process
Artificial intelligence for enhanced quality assurance through advanced strategies and implementation in the software industry
Full text linkIn this study, an artificial intelligence (AI) assistant is developed specifically for quality assurance (QA) tasks in response to the software industry’s growing demand for better QA solutions. Traditional QA methods are labor-intensive and prone to human error, even when they function properly. Utilizing the most recent advancements in natural language processing and AI, this AI assistant maximizes output and reliability by automating and optimizing QA processes. Using Rasa technology, the AI assistant aims to revolutionize QA by offering testers comprehensive support, including question-answering, contextual recommendations, and expanding users’ knowledge bases. It can reply to queries using both words and images. They are minimizing the amount of time spent searching for answers and raising user engagement. Notwithstanding AI’s potential to improve quality control, challenges remain, particularly in the area of human-like language production and comprehension. In today’s industry, professional QA tester training is a big worry. Due to the conventional training method’s substantial dependence on senior testers to educate and assist trainees, only two or three trainees may typically obtain appropriate instruction at a time from a single senior QA tester. This limitation affects the scalability of QA training programmers. With the new approach, QA training can be made much more productive and scalable since a single senior QA tester can now educate over 20 new hires at once
Big blocks and blind spots: power, knowledge, and epistemic democracy in The West Wing
Full text linkThe critically acclaimed television series The West Wing features two episodes that revolve around the concept of 'Big Block of Cheese Day', an initiative aimed at providing access to marginalised groups and individuals who would not typically have the opportunity to voice their concerns directly to the White House. This article explores the deeper implications of these episodes, examining the complexities of power, knowledge, and democratic participation in the context of epistemic injustice and the 'post-truth' era. Drawing on the theoretical frameworks of Haraway, Fricker, and Estlund, it analyses the show's portrayal of information access, the politics of expertise, and the challenges of achieving knowledge equity in a society marked by power imbalances and unequal access to information. The article argues that the Big Block of Cheese Day episodes, while fictional, offer valuable insights into the ongoing struggle for epistemic democracy and serve as a reminder of the importance of recognising and valuing diverse perspectives in the pursuit of a more just and equitable society
Research in Health and Social Care
No full textResearch in Health and Social Care equips students and early-career practitioners with the crucial knowledge, skills and understanding required to conduct sound research. Accessibly written, it is structured to allow professionals and students to engage in the theoretical development of their practice in ethical and reflective research.
Each chapter is co-written with students, featuring vignettes from health and social care students that highlight their personal journeys with research engagement. Content includes:
-exploring the everyday nature of research, processes, procedures and analysis
-demystifying key terminology
-an introduction to research and its importance in practice
-creative and traditional tools of research
-analysing data and how to disseminate data
-approaches to research
-embedding research into practice
-Discussions around key theoretical ideas are explored throughout, as well as opportunities for deep reflection.
This essential book is perfect for all social work and health and social care students, as well as early-career practitioners, aiming to deepen their knowledge and skills in conducting robust, ethical and relevant developmental research
Relationship between urban forest structure and seasonal variation in vegetation cover in Jinhua City, China
Full text linkUrban forests play a crucial role in enhancing vegetation cover and bolstering the ecological functions of cities by expanding green space, improving ecological connectivity, and reducing landscape fragmentation. This study examines these dynamics in Jinhua City, China, utilizing Landsat 8 satellite imagery for all four seasons of 2023, accessed through the Google Earth Engine (GEE) platform. Fractional vegetation cover (FVC) was calculated using the pixel binary model, followed by the classification of FVC levels. To understand the influence of landscape structure, nine representative landscape metrics were selected to construct a landscape index system. Pearson correlation analysis was employed to explore the relationships between these indices and seasonal FVC variations. Furthermore, the contribution of each index to seasonal FVC was quantified using a random forest (RF) regression model. The results indicate that (1) Jinhua exhibits the highest average FVC during the summer, reaching 0.67, while the lowest value is observed in winter, at 0.49. The proportion of areas with very high coverage peaks in summer, accounting for 50.6% of the total area; (2) all landscape metrics exhibited significant correlations with seasonal FVC. Among them, the class area (CA), percentage of landscape (PLAND), largest patch index (LPI), and patch cohesion index (COHESION) showed strong positive correlations with FVC, whereas the total edge length (TE), landscape shape index (LSI), patch density (PD), edge density (ED), and area-weighted mean shape index (AWMSI) were negatively correlated with FVC; (3) RF regression analysis revealed that CA and PLAND contributed most substantially to FVC, followed by COHESION and LPI, while PD, AWMSI, LSI, TE, and ED demonstrated relatively lower contributions. These findings provide valuable insights for optimizing urban forest landscape design and enhancing urban vegetation cover, underscoring that increasing large, interconnected forest patches represents an effective strategy for improving FVC in urban environments
Embedding research into practice, funding, and practicalities
No full textThis chapter presents Shedding Light on Long Covid (2023), a large-scale city-wide Sci-Art public research project aimed at raising awareness and deepening understanding of Long Covid. It provides essential tips and guidance on creative placemaking through student led curriculum design. Taking the reader on a journey from ideation to the installation of creative practices within a clinical research context, to developing new ecosystems for knowledge transfer with the public.
The chapter provides tips on how to intergrade scientific data into the delivery of artistic and design led outputs, leading to influencing policy through creative placemaking, and the role of sci-art practice as tool for social mobility and improved healthcare provisions and awareness. The benefits and challenges of co-designing with stakeholders in the public realm with Designing Dialogue CIC, that delivers the project S.H.E.D, is explored, and how it embedded clinical research seamlessly into artistic installation and project “Long Covid Diaries"
A sustainable water management framework for schools in Sub-Saharan Africa
Full text linkSafe and adequate water supply, sanitation, and hygiene (WASH) in schools are prerequisites within the right to basic education. WASH facilities across schools in developing nations, particularly in Africa, are unsatisfactory and expose children to risks of disease and infection. This study aims to gather insights into the WASH status of secondary schools in Ibadan, Nigeria, to develop a sustainable water management framework for schools. A concurrent mixed-method design (questionnaires and interviews) was adopted to benchmark water management in schools and inform the design of a framework. Results reveal a wealth of issues and concerns that include infrastructure challenges accessing reliable and safe water supplies, rundown and unhygienic toilet/urinal facilities, and dilapidated sinks/taps, plus resource challenges, such as an absence of tissue paper and soap. These issues are exposing schoolchildren to unnecessary health risks, further supported by reported illnesses and reduced school attendance. Based on these findings, and guided by the UN SDG#6 targets, a water improvement framework has been created and validated by school officials. The framework identifies both short-term and long-term guidance/actions to improve water management in schools across Sub-Saharan Africa. These form crucial steps toward better WASH, building healthier communities and enhancing educational environments and outcomes for schoolchildren
Sensory research using the Zaltman Metaphor Elicitation Technique (ZMET)
No full textThis chapter introduces Zaltman's Metaphor Elicitation Technique (ZMET). This is a tried and tested method which uses all the senses and enables the interviewer to build trust very quickly through using the image as a bridge to the interviewee. ZMET counters depth deficit and uncovers many hidden gemstones, which are often difficult to discover. This is an ideal method for tackling both sensitive and taboo subjects, which are often hard to vocalise. The method results in a vast quantity of rich data, which is also an enjoyable process for both the interviewee and interviewer