Learning Process Research Articles

학습 효과가 높은 수업과 낮은 수업의 학습 과정과 평가의 특성에 관한 연구

Objectives The purpose of this study is to explore the characteristics of instructions recognized as having a high learning effect and as having a low learning effect. Methods For this purpose, we conducted written interviews using online platform with 18 prospective instructors who are experiencing the pre-instructor course at the graduate school of education. It was designed to focus on instructions experienced before, not on instructions currently being taken, and through this, the process and effect of learning could be reported more objectively. The collected qualitative data were analyzed using a thematic anal-ysis method, and the learning process of instructions with high learning effects and instructions with low learning effects were derived and presented in a diagram. Results As a result of the study, 3 main categories, 9 themes, and 19 subcategories were derived as character-istics of the learning process included in instruction with high learning effect, and the amount of learning load, dif-ficulty, and responsibility perceived by learners were also confirmed along with the learning burden factors. As for the instruction with low learning effect, 3 main categories, 9 themes, and 18 subcategories were derived, and these included ‘no task’ and ‘no burden’. In the instruction with high learning effect, the focus of assignments and evaluations was on the learning process and performance, and improvement and feedback were made through the feedback of the instructor. In the instruction with low learning effect, the focus of assignments and evaluations was on organizing, summarizing, and general evaluation, and it was difficult to confirm improvement and feedback through instructor's feedback. Moreover, the difficulty and amount of learning load in the class were burden factors that appeared in both classes with high and low learning effects. Considering these results, instructors need to consider ways to have explanatory power to effectively deliver class content by analyzing learners' levels rather than reducing the difficulty and amount of learning. Conclusions Based on these findings, recommendations were provided to guide professors in aiming for and de-signing effective instructions.

SOC 기반 한국어 수업 설계 및 운영 사례 연구

Objectives This study aims to design and implement a Korean language course applying the Selection, Optimization, and Compensation (SOC) theory for Japanese elderly learners, and to explore the effectiveness of this instructional approach. Methods For this purpose, Korean language classes were designed and conducted, based on a 5-session course, targeting five elderly learners residing in Tokyo, Japan. To assess the outcomes of class operation, learner inter-views were conducted and qualitatively analyzed. Results Analysis results showed several key findings. Firstly, allowing learners to choose topics they are genuinely interested in led to higher satisfaction levels among learners. Secondly, adjusting the level and scope of learning activities to match the learners' proficiency level during class design and operation resulted in increased sat-isfaction with the learning process. Thirdly, using familiar and comfortable tools to assist learners' memory and fa-cilitate information retrieval also contributed to higher satisfaction with the learning process. Fourthly, learners ex-pressed satisfaction with Korean language classes applying the theory of Selection-Optimization-Compensation (SOC), noting minimal physical exertion and the possibility of review. Lastly, by completing learning activities, learners not only experienced psychological satisfaction but also a sense of achievement in learning. Conclusions It was confirmed that Korean language classes applying the theory of Selection-Optimization- Compensation(SOC) improved the learning autonomy, interest, and satisfaction of elderly learners.This study contributes by addressing Korean language learning for elderly learners aged 70 and above, a topic not extensively explored in Korean language education research. Furthermore, it provides foundational data for designing and op-erating Korean language classes for elderly learners based on SOC theory.

Implementation of team based learning for MBBS students – An innovative teaching learning method in medical education

Team-Based Learning (TBL) is an educational approach that enhances student engagement through individual assessments and collaborative group work. It is an effective and structured form of small group learning adaptable to large classroom settings. TBL ensures student accountability through various stages, including pre-class preparation, readiness assurance tests, problem-solving exercises, and immediate feedback. This study aims to implement and assess the efficacy of the TBL strategy among MBBS students in a large group teaching environment. The pilot study adopted a TBL approach, involving steps such as pre-class preparation with PDF materials and a WhatsApp video guide issued a week prior, individual and team readiness assurance tests (iRAT and tRAT) conducted via Google Forms and hard copy MCQs, clarification sessions on MCQs and relevant medical topics, and collaborative learning through case scenarios related to Type 2 diabetes. Discussions and conclusions were facilitated by the faculty, followed by immediate and scheduled feedback using Google Forms. Statistical analysis was performed using an unpaired t-test to compare iRAT and tRAT scores. The study gathered 133 responses from the first batch and 140 from the second batch of first year MBBS students. The analysis revealed significant improvements in scores from iRAT to tRAT, indicating the effectiveness of TBL in enhancing student learning outcomes. Feedback from the students showed high levels of engagement and interest in the learning process, with over 98% in both batches finding the TBL approach interesting and interactive. The findings suggest that TBL is a valuable and effective educational strategy that improves learning outcomes and fosters collaborative learning among students.

Metacognitive Awareness as a Predictor of Mathematical Modeling Competency Among Preservice Elementary Teachers

<p style="margin-left:28px; text-align:justify">Mathematical modeling offers a promising approach to improving mathematics education. This study aims to determine if the concept of metacognitive awareness in the learning process is associated with mathematical modeling. This study also considers the interaction effect of sex and academic year level on both variables. Focusing the study on preservice elementary teachers might address potential issues and targeted intervention in their preparation program concerning their ability to teach and guide young learners in modeling activities. The research sample includes 140 preservice elementary teachers at Central Luzon State University, Philippines. Data collection used an adapted metacognitive awareness inventory and a validated researcher-made mathematical modeling competency test aligned with the K-12 mathematics curriculum in the Philippines. Results revealed that the preservice elementary teachers had a high metacognitive awareness and mathematical modeling competency, ranging from 22 to 31 out of 36 points. Besides, Factorial ANOVA indicates that academic year level positively affects both variables regardless of sex, and stepwise regression analysis unveiled that information management, declarative knowledge, and planning significantly predict 41.4% of the mathematical modeling competency variance. This suggests that developing metacognitive awareness supports preservice elementary teachers in performing modeling tasks that improve their competency level in mathematics.</p>

대학 교양수업에서 학습자 중심의 팀기반 학습 경험

Objectives The purposes of this study were to explores the experiences that learners who participate in team-based learning experience during the learning process, examines the learners' experiences in team-based learning in a contextual setting through a qualitative approach, and seeks educational reflection and implications. Methods For this purpose, this study was conducted on 94 college students who participated in liberal arts classes. To achieve the purpose of the study, first, team-based learning was designed by selecting a ‘pedagogy-based lib-eral arts’ subject. Second, after analyzing the content covered in the selected subject, the instructor's lecture on the learning topic was conducted in advance in the first half of each lesson, and students studied individually through lectures on the learning topic. After watching various learning materials related to the learning topic, team-based learning related to the learning content was applied. Third, we analyzed individual and team activity sheets, study notes for each session, and reflection logs written by learners before and after learning. The response sheets used in the analysis were all qualitative data collected through classes, and were analyzed through the inter-action of categorization, coding, structuring, and interpretation during the data analysis process. Results In terms of learning motivation from the results of this study, first, the motivation for taking the course was participation due to obligation, accidental choice, and unclear expectations due to the nature of liberal arts subjects, and second, the topic and content of learning were based on their own real-life usefulness and career-related topics. Third, actively apply the cognitive information acquired through participation in learning to solve life problems. Fourth, setting learning goals shows educationally important information and context for converting extrinsic motivation into intrinsic motivation. In terms of cognition, learners showed the highest cognitive factors in motivation to take the course, personal learning goals, and learner needs. In terms of interaction, in the process of participating in team-based learning, learners placed first, the attitude of participants, including communication, participation, and courtesy among team members, as the most important, and second, they meaningfully experienced positive experi-ences and emotional support. In terms of teaching and learning, positive experiences such as self-expression, dis-cussion and presentation, initiative, and overcoming presentation anxiety were recognized through participation in team learning. On the other hand, negative aspects could act as a hindrance to positive interactions between team members. In particular, to overcome these obstacles in the early stages of learning, multifaceted efforts were need-ed, such as clear motivation, setting learning goals, setting learning content that suits the learner's needs and inter-ests, emotional support from the instructor, effective learning support materials, and evaluation plans. Conclusions Through this study, it can be seen that, due to the nature of the learning process in team-based classes where learners' interaction is the center, more active and interactive communication and voluntary and proactive participation are required than previous learning experiences.To achieve this, active and flexible re-sponses will be needed to improve the anxiety and dissatisfaction that learners may feel. Through this, the charac-teristics and diversity of individual learners can be mutually recognized and accepted at the beginning of learning, the strengths of team-based learning of emotional support and consideration through interaction between team members can be effectively demonstrated, and learners' expectations and demands for participation in learning can be improved.

Artificial intelligence and human integration: a conceptual exploration of its influence on work processes and workplace learning

ABSTRACT This paper examines the influence of Artificial Intelligence (AI) on job and organizational processes, drawing on sociotechnical systems (STS) theory. It discusses the emergence of AI and its implications for HRD on individual, group, and organizational learning. AI has changed the trajectory of work processes and outcomes, paving the way for the future of work. An AI-human integration process model is proposed to reinforce its influence on job design in response to AI applications in the workplace. Learning is experienced between humans and between humans and AI. AI’s cognitive intelligence is capable of learning from each other through a process of deep learning. Humans, too, can learn from AI to modify their response towards working with AI-enabled applications. The model illustrates the relationships between individuals, work processes, and sociotechnical systems through multilevel interactions. Of relevance to HRD are two critical processes, namely learning and innovating, that facilitate the continuation of AI-human integration, enabling employees to embrace AI as an integral part of their work life. The paper offers several implications for HRD research and practice, recognizing opportunities for workplace learning and the potential constraints and risks arising from AI adoption that are applicable globally and internationally.

리빙랩에서의 집단학습과 매개 활동에 대한 사례연구

Objectives A group of various stakeholders itself cannot automatically guarantee learning for co-creation. For the creation of collective knowledge, intermediary activities that can interconnect and integrate several subjects with different interests, backgrounds, experiences, and knowledge are essential. Living Lab is a place of collective learning based on the horizontal cooperative relationship between private, public, industry, and academia, and is a representative form of co-creation. However, in the case of Living Lab in Korea, research from the perspective of collective learning through co-creation has hardly been conducted. Therefore, this study aims to analyze the collective learning of Living Lab and derive implications, focusing on intermediary activities. Methods A qualitative case study was conducted on the ‘Smart Farm Planning Living Lab for Revitalizing the Old Urban Community’ held in Innovation District, Incheon. In order to structure interactions for collective learning, ac-tivities and tasks for each stage of the Living Lab were organized around the process and results of collective learn-ing, and then classified and analyzed as detailed intermediary activities of facilitating, configuring, brokering ac-cording to the definition of Hakkarainen & Hyysalo(2016). Results In all three stages of Living Lab, the proportion of facilitating was the highest, and brokering were common. This can be interpreted as a result showing that intermediary activities that facilitate actors' activities and broker connections between actors are essential in the process of co-creation to solve social problems. Next, through the three stages of Living Lab, all detailed intermediary activities of the facilitating, configuring, brokering appeared, and it was confirmed that the proportion of detailed intermediary activities was different for each stage of progress. This shows that it is necessary to pay attention to the importance and multifacetedness of interme-diary activities that are overlooked in co-creation through Living Lab. Conclusions This study supports the results of previous studies that the intermediary activities that contribute to collecitve learning and co-creation of Living Lab need to be extended and understood into three types: configuring and brokering as well as facilitating. In addition, the diversity of intermediary activities has important implications for the practical guidelines for Living Lab operation, which are characterized by a spiral development structure that changes according to the results of interaction and learning. Finally, the recognition of the intermediary's unique interactional expertise and the need for research on practical curriculum development and field operation guide-lines based on it were proposed.

Multi-class boosting for the analysis of multiple incomplete views on microbiome data

BackgroundMicrobiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed to ML methods can be subject to different sampling, sequencing and preprocessing techniques. Each different choice in the pipeline can lead to a different view (i.e., feature set) of the same individuals, that classical (single-view) ML approaches may fail to simultaneously consider. Moreover, some views may be incomplete, i.e., some individuals may be missing in some views, possibly due to the absence of some measurements or to the fact that some features are not available/applicable for all the individuals. Multi-view learning methods can represent a possible solution to consider multiple feature sets for the same individuals, but most existing multi-view learning methods are limited to binary classification tasks or cannot work with incomplete views.ResultsWe propose irBoost.SH, an extension of the multi-view boosting algorithm rBoost.SH, based on multi-armed bandits. irBoost.SH solves multi-class classification tasks and can analyze incomplete views. At each iteration, it identifies one winning view using adversarial multi-armed bandits and uses its predictions to update a shared instance weight distribution in a learning process based on boosting. In our experiments, performed on 5 multi-view microbiome datasets, the model learned by irBoost.SH always outperforms the best model learned from a single view, its closest competitor rBoost.SH, and the model learned by a multi-view approach based on feature concatenation, reaching an improvement of 11.8% of the F1-score in the prediction of the Autism Spectrum disorder and of 114% in the prediction of the Colorectal Cancer disease.ConclusionsThe proposed method irBoost.SH exhibited outstanding performances in our experiments, also compared to competitor approaches. The obtained results confirm that irBoost.SH can fruitfully be adopted for the analysis of microbiome data, due to its capability to simultaneously exploit multiple feature sets obtained through different sequencing and preprocessing pipelines.

Linking organisational learning, performance, and sustainable performance in universities: an empirical study in Europe

Universities are facing changes that could be adapted by learning. Organisational learning helps universities in attaining better organisational and sustainable performance. The study aims to combine and explore how organisational learning culture enables organisational learning to contribute to better organisational performance and better sustainable performance, following the natural resource-based view and organisational learning theory. The study examines the relationship between organisational learning culture, organisational learning, organisational performance, and sustainable performance in the university context from university teachers. The author collected 221 surveys from public university teachers in Europe to test the model. The results indicate a positive relationship between organisational learning culture and organisational learning. In addition to that, the positive relationship between organisational learning and organisational performance is indicated. Moreover, the results indicate a positive relationship between organisational learning and sustainable performance. The results also show that the organisational learning process mediates organisational learning culture and university performance. The study addresses a gap in the scarce studies in the university context for organisational learning and sustainable performance. Finally, this study reproduces an organisational model that has been adapted for universities.

Social Learning with Actor–Critic for dynamic grasping of underwater robots via digital twins

Dynamic grasping is a crucial technology in the field of underwater robotics, playing an essential role in executing complex tasks. However, traditional control methods encounter challenges in achieving an efficient and stable dynamic grasping process due to the complexities and uncertainties of underwater environments. This paper introduces a novel black-box intelligent optimization algorithm named Social Learning with Actor–Critic (SLAC) for the dynamic grasping of underwater robots. The core architecture of SLAC is based on the integration of two key algorithms: Intelligent Social Learning (ISL) for intelligent optimization and Soft Actor–Critic (SAC) for reinforcement learning. ISL enhances SAC by supplying a larger number of transitions, whereas SAC improves ISL with more effective strategies. These algorithms interact synergistically, augmenting their respective strengths throughout the learning process. To evaluate SLAC’s performance, a comparison is made with six state-of-the-art methods across eight continuous control benchmark cases. The results highlight SLAC’s exceptional learning capability and performance benefits. Furthermore, virtual reality software for the underwater robot and a corresponding digital twin system have been developed. The SLAC algorithm is trained in the digital twin environment before its application in the actual underwater setting. Through interactive training and iterative learning, both simulated and experimental results demonstrate the robot’s proficiency in achieving efficient and stable dynamic grasping, effectively adapting to various underwater environments’ variations and complexities.

Students Reading Achievement Based on Their Intelligence Quotient (IQ): A Case Study

This research was conducted to determine the factors that caused some students to have high IQ but low English reading achievement and vice versa. The research aimed to find out what factors caused students to have high intellectual intelligence but low reading achievement and vice versa. This type of research was a qualitative method with a case study research design. Participants in this research were grade VIII students at SMPN 4 Bukittinggi, and using purposive sampling techniques, the researchers selected 9 participants for this research. The instruments used in this research were interviews and observations checklist of students. The results of the analysis showed that there were two factors that caused students to have high IQ but low English reading achievement and vice versa. This could be seen from the results of interviews and observation checklists with students, namely internal factors are physiological, intelligence, talents, interests, student activity during the learning process, and also external factors, namely the role of parents, teachers, learning media, infrastructure, curriculum, school time, and mass media. It can be concluded that the factors causing students to have high IQ but low achievement in learning English and vice versa were internal and external factors, and were also supported by aspects of student activity in the English learning process.

A maize seed variety identification method based on improving deep residual convolutional network

Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network’s learning process and enabled more efficient information propagation across the network layers. Meanwhile, in the later residual blocks of the model, we introduced both the efficient channel attention (ECA) mechanism and depthwise separable (DS) convolution, which reduced the model’s parameter cost and enabled the capturing of more precise and detailed features. Finally, a Swish-PReLU mixed activation function was introduced globally to improve the overall predictive power of the model. The results showed that our model achieved an impressive accuracy of 91.23% in corn seed classification, surpassing other related models. Compared with the original model, our model improved the accuracy by 7.07%, reduced the loss value by 0.19, and decreased the number of parameters by 40%. The research suggested that this method can efficiently classify corn seeds, holding significant value in seed variety identification.

In partnership, for partnership

After almost a year of planning an international conference on student partnership for humanitarian action with the UN Refugee Agency, a student-faculty partnership reflect on the learning process they undertook, and how years of working together built a strong foundation for this project. Working in partnership to communicate the importance of partnership to address complex global issues signifies the importance of engaging multi-level stakeholders in new, innovative solutions. This introduction to the special section titled after the conference provides the context for this challenging yet rewarding feat made possible with a community of partnerships.

Teachers’ Language Use in EFL Classroom Interaction: A Qualitative Study Analysis in Indonesia Settings

This research aims to analyze the classroom language used by a teacher of English department during the learning process. Mastery of a foreign language requires persistent practice and patience among students and teachers. Therefore, languages used in the classroom have a potential to help students produced good communication in the target language. This qualitative study was conducted with 1 male teacher and 22 second grade English major students who were learning writing. Data collection was carried out by observation and interviews both the teacher and 5 sample students and it was analysed with descriptive qualitative techniques. The results showed that the classroom language generally used by the EFL teacher were categorized into several types: instructions, questions, feedback and correction, clasroom management, building rapport, explanations, transitions, and encouragement. While for students, they used language revolved around their interactions within the classroom, both with their peers and teachers. It included phrases and questions that helped them manage their learning experiences, clarify instructions, and participate actively in classroom activities. The results of the analysis are supported by the observation checklists regarding how situations require certain types of language goals. The use of classroom language has different objectives according to the current situation within the scope of learning. The study offers practical recommendations for EFL teachers on how to effectively use different types of language to enhance learning outcomes, such as the strategic use of questions, feedback, and encouragement.

Evaluation of the barriers to organizational learning in a port sector company

Competitive advantage is an essential factor for the development and longevity of businesses. It is associated with companies' ability to learn, manage their knowledge, and recognize and accurately interpret opportunities and barriers to learning processes. This work aimed to identify the barriers to organizational learning in a company in the port sector. For this, an instrument was used, such as an interview script composed of 27 questions, grouped by model learning process: Intuition, interpretation, integration and institutionalization (4I) and subgrouped by influencing factors, with semi-structured questions aimed at identifying the barriers to organizational learning present in the company. The results were analyzed based on the recordings of the interviewees' speeches. The main results showed 28 of the 80 barriers listed in the adopted model, highlighting those related to the intuition process, especially those with a personal action factor, revealing opportunities for improvement in actions and mechanisms to promote the creation of knowledge and the generation of innovative ideas.

The potential of learning analytics for research on behavioral learning processes: current and future significance for research on learning and instruction

ZusammenfassungInternational entwickelte sich unter dem Begriff „Learning Analytics“ in den letzten Jahren ein Forschungsfeld, das sich auf das Sammeln, Auswerten und Anwenden komplexer, häufig multi-modaler und digitaler Verhaltensspuren fokussiert. Diese Verhaltensspuren, die Lernende und Lehrende in digitalen Kontexten hinterlassen, werden mit Hilfe computerbasierter Modelle oder mittels Verfahren des maschinellen Lernens ausgewertet, um Erkenntnisse über Lehr- und Lernprozesse zu gewinnen. Die Lehr-Lernforschung nähert sich derzeit langsam dem Feld der Learning Analytics an. Ein Potenzial von Learning Analytics für die Lehr-Lernforschung wurde demnach bereits erkannt, sodass ein Weiterdenken an dieser Stelle äußerst lohnenswert erscheint. Der vorliegende Beitrag skizziert daher die inzwischen bereits stark fortgeschrittene Forschung zu Learning Analytics und stellt anhand konkreter Beispiele das Potenzial von Learning-Analytics-Ansätzen für die Weiterentwicklung von Lern- und Instruktionstheorien dar. Insbesondere wird hier auf selbstreguliertes und kooperatives Lernen eingegangen sowie auf die Gestaltung von Lernumgebung und Unterstützung von Lehrkräften. Dabei mit- und weitergedacht werden sowohl Risiken und Herausforderungen von Learning Analytics (wie fehlende Kopplung zwischen Theorie und Empirie sowie ethische Aspekte) als auch Chancen (wie Erfassung der Komplexität und Zeitlichkeit von Lehr-Lernprozessen) von Learning Analytics für die Unterrichtswissenschaft und -praxis.

Using Project ECHO to deliver a tele-mentoring and teaching program on palliative care in South Asia: Interpretive description of participants' experiences with a community of practice for learning.

To explore the learning experiences of participants (learners and teachers), in a yearlong tele-teaching and mentoring program on pediatric palliative care, which was conducted using the Project ECHO (Extension for Community Healthcare Outcomes) model and consisted of 27 teaching and clinical case discussion sessions for palliative medicine residents in India and Bangladesh. The goal of the study is to explore how participation and learning is motivated and sustained for both residents and teachers, including the motivators and challenges to participation and learning in a novel online format. Qualitative interviews with ECHO participants, including learners and teachers were conducted. Interviews were recorded and transcribed. Thematic analysis of interview data was conducted within an interpretive description approach. Eleven physicians (6 residents, 5 teachers) participated in interviews. Key elements of the ECHO program which participants identified as supporting learning and participation include small group discussions, a flipped classroom, and asynchronous interactions through social media. Individual learner characteristics including effective self-reflection and personal circumstances impact learning. Providing opportunities for a diverse group of learners and teachers, to interact in communities of practice (COP) enhances learning. Three major themes and 6 subthemes describing learning processes were identified. Themes included (1) ECHO program structure, (2) learner characteristics, and (3) COP. Subthemes included flipped classroom, breakout rooms, learning resources, personal circumstances, self-awareness of learning needs, and community interactions. Project ECHO suggests a novel model to train health providers, which is effective in low- and middle-income countries. Online learning programs can lead to learning through community of practice when learners and teachers are able to interact and engage in peer support and reflective practice. Educators should consider incorporating small group discussions, a flipped classroom design, and opportunities for asynchronous interactions to enhance learning for participants in online learning programs.

Simultaneous Three-Degrees-of-Freedom Prosthetic Control Based on Linear Regression and Closed-Loop Training Protocol

Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left–right, up–down and open–close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom.

Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network

Abstract The strengths of a Memetic Algorithm and an enhanced Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol are combined in this research study to propose a revolutionary method for improving the energy efficiency and longevity of Wireless Sensor Networks (WSNs). WSNs are widely used for a variety of applications, including industrial automation, healthcare, and environmental monitoring, where extending network lifetime and energy conservation are crucial. Although successful, conventional clustering algorithms like LEACH feature subpar cluster formation and premature cluster head failures. To get over these restrictions, a memetic algorithm is used to maximize cluster formation and reduce energy usage. This algorithm was influenced by natural evolution and cultural learning processes. 
The Memetic Algorithm iteratively improves the LEACH protocol in the proposed technique by enhancing dynamic clustering, energy-aware routing, and cluster head selection. These methods work together to increase energy efficiency and lengthen the life of the network. In compared to conventional LEACH-based WSNs and other cutting-edge algorithms, the paper's study of the simulation results shows how successful the suggested technique is. In light of network longevity, energy use, and data transmission efficiency, the results obtained demonstrate notable improvements.

Valuation of guaranteed minimum accumulation benefits (GMABs) with physics-inspired neural networks

Abstract Guaranteed minimum accumulation benefits (GMABs) are retirement savings vehicles that protect the policyholder against downside market risk. This article proposes a valuation method for these contracts based on physics-inspired neural networks (PINNs), in the presence of multiple financial and biometric risk factors. A PINN integrates principles from physics into its learning process to enhance its efficiency in solving complex problems. In this article, the driving principle is the Feynman–Kac (FK) equation, which is a partial differential equation (PDE) governing the GMAB price in an arbitrage-free market. In our context, the FK PDE depends on multiple variables and is difficult to solve using classical finite difference approximations. In comparison, PINNs constitute an efficient alternative that can evaluate GMABs with various specifications without the need for retraining. To illustrate this, we consider a market with four risk factors. We first derive a closed-form expression for the GMAB that serves as a benchmark for the PINN. Next, we propose a scaled version of the FK equation that we solve using a PINN. Pricing errors are analyzed in a numerical illustration.