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The taper and merchantable tree volume equations are the most used models in forestry because of their accuracy in estimating both total and merchantable tree volume. However, numerous studies reported that artificial neural network models show fewer errors and a greater success rate as compared to regression models. This study used data from 200 Crimean pine trees in Turkey’s Central Anatolia and Mediterranean Region to assess the performance of artificial neural network (ANN) models and the Max-Burkhart’s equation for estimating taper and merchantable tree volume. The most accurate results were obtained using 3 hidden layers and 10 neurons in the taper model and 1 hidden layer and 100 neurons in the volume model. The hyperbolic tangent sigmoid function was used for the ANN analysis and hyper-parameter customization. Using the ANN model with hyper-parameter customization, the AAE in the Max-Burkhart taper model decreased from 9.315 to 6.939 (-25.5%), the RMSE decreased from 3.072 to 2.656 (-13.5%), and the FI increased from 0.964 to 0.966 (+1.23%). Similarly, using the ANN model with hyper-parameter customization, the AAE in the Max-Burkhart volume model decreased from 0.056 to 0.013 (-76.6%), the RMSE decreased from 0.247 to 0.12 (-51.6%), and the FI increased from 0.909 to 0.979 (+7.69%). Our results showed that the ANN models’ predictions were more accurate and reliable compared to the Max-Burkhart’s equations. We resolved overfitting via hyper-parameter modification, which also allowed for monitoring the impact of error and prediction outputs at various learning rates. It was also possible to develop tree taper and volume equations with lower error rates in both training and validation data, consistent with tree growth trends in both data sets.

The progress towards fully autonomous mobility is significantly impacted by the integration of evolving technologies in connected and automated mobility (CAM). Connected and automated vehicles (CAVs) have the potential to revolutionize our transportation system by improving efficiency, safety, and environmental sustainability. Automated shuttles and public buses, smart traffic signals, intelligent passenger cars, and smart roundabouts are just a few examples of technologies that are being planned and actively researched for integration into transportation systems. Sensors are essential in making this possible. This article provides a structured overview of research on the selection and positioning of sensors on- and off-vehicle to achieve cooperative, connected, and automated mobility. The general integration and usage of sensors in vehicles and infrastructure is described, a detailed taxonomy of the examined research is provided, and future research directions are presented, involving solutions for quantification of sensor performance and addressing current trends. The findings of this article also highlight numerous challenges in creating a universal framework, the lack of application of novel machine learning methods, and the complexity of modeling multi-sensor settings.

Power systems are increasingly confronted with operational uncertainties. These stem from the integration of wind and solar energy sources with inherently stochastic generation behavior. The ensuing risks include power plant curtailments or grid imbalances. The deterministic approaches that currently underpin most planning have proven insufficient to manage these risks. This paper presents an alternative in the form of a data-driven probabilistic approach with direct relevance for transmission system operators. The models present a novel methodology that is independent of the common boundary conditions, e.g. case-specific models. First, we compare several data-driven algorithms and assign the forecasting task to the best-performing one. Second, the resulting forecast serves as an input for three optimal power flow (OPF) problems we tailor to the German power system. These problems minimize energy import volumes, energy import costs, and overall power losses. Third, based on the OPF results, we perform a risk assessment for operational instability, power loss, financial losses, and renewable energy waste. The results show that neural networks slightly outperform traditional machine learning algorithms in forecasting accuracy. However, linear-quadratic regulators remain attractive for their simplicity-performance ratio. Our probabilistic OPF approach can reduce power losses and identify frequency and line loading irregularities that deterministic methods do not. The data-driven approach we propose is superior to existing approaches in terms of its performance, usability, and applicability to complex power systems.

The efficient processing of information plays a critical role in data-driven domains. Data acquisition pipelines, which act as the interface between data collection and subsequent processing, are central to this. Traditional software engineering methods form the foundation for the development of robust data acquisition pipelines, but agent-based systems hold great potential for realizing such pipelines, and this study presents an innovative architecture that uses agent-based components. This modular approach makes it possible to use specialized agents for individual tasks and to increase the effectiveness of the pipelines. The applicability and effectiveness of this architecture are demonstrated using a system for autonomous driving that collects and processes data from edge computers along roads. Our results are on GitHub (https://github.com/BenjaminAcar/Agent-based-Data-Acquisition-Pipeline).

Abstract The future of driving is autonomous. It requires a comprehensive stack of embedded software components, enabled by open-source and proprietary platforms at different abstraction layers, and then operating within a larger ecosystem. Autonomous driving demands connectivity, cooperation and automation to form the cornerstone of autonomous mobility solutions. Platform economy principles have revolutionized the way we produce, deliver and consume products and services worldwide. More and more businesses in the field of mobility and transport appear to implement transaction, innovation, and integration platforms as core enablers for Mobility-as-a-Service and transport applications. Artificial intelligence approaches, especially those dealing with distributed systems, enable new mobility solutions, such as autonomous driving. This paper contributes to understanding the intertwining role between distributed artificial intelligence, autonomous mobility and the resulting platform ecosystem. A systematic literature review is applied, in order to identify the intersection between those aspects. Furthermore, the research project BeIntelli is considered as a hands-on application of our findings. Taking into account our analysis and the aforementioned research project, we pose a blueprint architecture for autonomous mobility. This architecture is the subject of further research. Our conclusions facilitate the development and implementation of future urban transportation systems and resulting mobility ecosystems in practice.

This paper presents a comprehensive study on generating subjective inquiries for news media posts to empower public engagement with trending media topics. While previous studies primarily focused on factual and objective questions with explicit or implicit answers in the text, this research concentrates on automatically generating subjective questions to directly elicit personal preference from individuals based on a given text. The research methodology involves the application of fine-tuning techniques across multiple iterations of flan-T5 and GPT3 architectures for the task of Seq2Seq generation. This approach is meticulously evaluated using a custom dataset comprising 40,000 news articles along with human-generated questions. Furthermore, a comparative analysis is conducted using zero-shot prompting via GPT-3.5, juxtaposing the performance of fine-tuned models against a significantly larger language model. The study grapples with the inherent challenges tied to evaluating opinion-based question generation due to its subjective nature and the inherent uncertainty in determining answers. A thorough investigation and comparison of two transformer architectures are undertaken utilizing conventional lexical overlap metrics such as BLEU, ROUGE, and METEOR, alongside semantic similarity metrics encompassing BERTScore, BLEURT, and answerability scores such as QAScore, and RQUGE. The findings underscore the marked superiority of the flan-T5 model over GPT3, substantiated not only by quantitative metrics but also through human evaluations. The paper introduces Opinerium based on the open-source flan-T5-Large model, identified as the pacesetter in generating subjective questions. Additionally, we assessed all aforementioned metrics thoroughly by investigating the pairwise Spearman correlation analysis to identify robust metrics.

While multi-agent frameworks can provide many advanced features, they often suffer from not being able to seamlessly interact with the outside world, e.g., with web-services or other multi-agent frameworks. This may be one factor that hinders a broader application of multi-agent systems in production systems. A possible solution to this problem is the combination of multi-agent systems with the concepts of micro-services and containerization, providing language-agnostic open interfaces, as well as encapsulation and modularity. In this paper, we propose an API and reference implementation that can be employed by multi-agent systems based on different languages and frameworks. Each agent component is encapsulated in a container and is accessed through its parent runtime platform, which takes care of aspects such as authentication, input validation, monitoring and other infrastructure tasks. Multiple runtime platforms can then be connected to form systems of distributed, heterogeneous multi-agent societies.

The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas.

India is one of the leading economies that have been stuck hard by the COVID-19 pandemic and the stringent measures were put in place to combat it. Among several sectors, dairy sector is the most affected as dairy products are highly perishable and rely on time-sensitive supply chains. Though studies are available on the impact of COVID-19 pandemic on dairy sector, there are no studies on COVID-infected dairy farm households. The present study was an attempt to assess the socio-economic impact of COVID-19 pandemic on infected and uninfected dairy farm households in West Bengal. The study covered pre-lockdown, lockdown (both 1st and 2nd wave) and post-lockdown phases of COVID-19 pandemic. The primary data was collected from 150 dairy farm households (COVID-19 infected-75 and uninfected-75) in Murshidabad and Nadia districts of West Bengal. Dairy Economic Performance Index consisting of number of milch animals, milk yield, marketed milk, milk procurement price, concentrate price and veterinary cost was developed using principal component analysis. In order to make infected and uninfected groups statistically comparable, propensity score matching technique was employed. The index values were compared between matched infected and uninfected groups over different phases of COVID-19 pandemic. Dairy households incurred significant economic losses during the lockdown and post-lockdown periods due to increase in cost of concentrates, decline in the number of milch animals and drop in milk procurement prices. Dairy households faced constraints in procuring dry fodder, concentrate feed and in accessing veterinary care. COVID-19 infected dairy farm households had a greater socio-economic hurdle than that of uninfected households.

BackgroundThe increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry of Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform (DMP). ObjectiveThis paper’s objective is to present the design decisions taken and technical solutions provided to ensure successful nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, and adaptability. MethodsThe DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional and national health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensure extensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters. The DMP is horizontally scalable in case of high load to ensure high performance. ResultsAs of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for 16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes, and hypertension, resulting in the diagnosis of 3,545,573 patients with obesity, 534,423 patients with high cardiovascular risk, 490,346 patients with diabetes, and 144,768 patients with hypertension. ConclusionsIt has been demonstrated that the platform can scale horizontally and efficiently provides services to thousands of family medicine practitioners without performance problems. The system seamlessly interoperates with existing health IT solutions and runs as a part of the clinical workflow of physicians at the point of care. By automatically accessing and processing patient data from various sources to provide personalized care plan guidance, it maximizes the effect of evidence-based decision support services by seamless integration with point-of-care electronic health record systems. As the system is built on international code systems and standards, adaptation and deployment to additional regional and national settings become easily possible. The nationwide DMP as an integrated care solution has been operational since January 2020, coordinating effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines.

The application of containerization technology has seen a significant increase in popularity in recent years, both in the business and scientific sectors. In particular, the ability to create portable applications that can be deployed on different machines has become a valuable asset. Autonomous driving has embraced this technology, as it offers a wide range of potential applications, including the operation of autonomous vehicles and the digitization of infrastructure for the development of Cooperative, Connected, and Automated Mobility (CCAM) services. This paper provides a comprehensive analysis of containerization in autonomous driving, emphasizing its application, utility, benefits, and limitations.

The radical increase in the popularity of electric vehicles (EVs) has in turn increased the number of associated problems. Long waiting times at charging stations are a major barrier to the widespread adoption of EVs. Therefore, battery swapping stations (BSSs) are an efficient solution that considers short waiting times and healthy recharging cycles for battery systems. Moreover, swapping stations have emerged as a great opportunity not only for EVs, but also for power systems, with regulation services that can be provided to the grid particularly for small networks, such as microgrid (MG) systems. In this study, the optimum location and size that maximize the revenue of a swap station in an MG system are investigated. To the best of our knowledge, this study is first to solve the placing and sizing problem in the MG from the perspective of a BSS. The results indicate that bus 23 is the BSS’s optimal location and is crucial for maximizing revenue and addressing issues like the provision of ancillary services in microgrid system. Finally, the swap demand profile of the station serving electric bus public transportation system was obtained using an analytical model based on public transportation data collected in Berlin, Germany.

To equip students with 21st-century skills, teachers must have both deep STEM content knowledge and the confidence to implement and teach appropriate STEM content. Many elementary teachers have inadequate STEM background knowledge, low confidence, and STEM self-efficacy for implementing STEM in the classroom; as a result, teachers' classroom practices are affected. The study examined how elementary teachers perceive their ability to implement STEM in the classroom. The STEM Efficacy Survey was sent to a randomized pool of 100 elementary educators, and 18 of them agreed to participate in the study. This instrument was designed to elicit responses related to the teachers' previous background in STEM, their beliefs about their ability to implement STEM, and their actual STEM implementation in the elementary classroom. The results revealed that participants were confident in their understanding of the engineering design process and problem-based learning. However, teachers were unwilling to apply the engineering design process in the classroom. From this research, the researchers concluded that higher levels of training in STEM education may influence how teachers perceive their ability to implement STEM in the classroom. Further research should focus on exploring how STEM training affects teachers' self-efficacy in STEM implementation.

This systematic literature review (SLR) scrutinizes the trends and interrelationships prevalent in Virtual Reality (VR) applications within educational frameworks, analyzing a comprehensive pool of 43 theses conducted in a Eurasian country. The primary objectives encompass investigating learning-teaching theories, learning domains, design elements, VR typology, and the departments undertaking VR research. Findings from the SLR underscore a significant concentration of VR research activities within technology-oriented departments. The prevalent approach involves experimental assessments of diverse variables within VR learning environments, yet a conspicuous dearth of design-centric investigations is observed. This highlights a critical need for comprehensive studies elucidating the design and developmental processes within VR applications, especially in light of the current characterization of VR research as lacking established standards. Moreover, a noteworthy revelation is the prevalent absence of a robust theoretical framework across the majority of studies. This absence may pose impediments to the widespread adoption of VR within educational paradigms, given the pivotal role of learning-teaching theories in guiding pedagogical processes. Examination of design elements highlights the prominence of realistic experiences, passive observation, mobility, and interaction with the environment. Recognizing the potential impact of diverse design elements on enhancing realism, aligning specific elements with distinct learning domains holds promise for augmenting the immersive quality of VR experiences. This research emphasizes the critical need for more comprehensive, theory-guided, and design-focused VR studies to propel its integration effectively within educational landscapes.

Equality in education is a fundamental goal for societies, and educational systems need to provide equal opportunities for all individuals to realize their potential. However, in the current situation, economic, social and cultural differences create inequality of opportunity in education and this is a fundamental problem of education systems. This article emphasizes the importance of achieving the goal of equality in education and discusses the problems encountered in achieving this goal. By examining the root causes and consequences of inequalities in education, it aims to better understand these problems. Furthermore, the article presents key strategies for achieving equity in education. These strategies include reviewing education policies, training and supporting teachers, increasing access to education for disadvantaged groups, using technology-based education methods and developing antidiscrimination awareness programs. In this context, the goal of achieving equity in education is critical for social development and human rights, and the paper aims to provide a roadmap for equity in education, presenting the steps and strategies needed to achieve this important goal.

The mobility of international students is a crucial tool for the European Union's goal of creating a unified European Higher Education Area. Despite the initial assumption that all European universities and students can benefit equally from cross-university study experiences, certain European regions have become disproportionately favored over time. This has resulted in specific geographical patterns, challenging the principles of equality and openness in the EU's higher education policy. To better understand these spatial effects and enhance the EU's mobility policy effectiveness, this research analyzes the network properties of Erasmus+, comparing it with traditional degree-seeking activities. Utilizing a modularity measure with data from the EU and UNESCO, the study reveals significant sub-regional variations in the Erasmus+ geographical network, posing challenges for policy implementation and limiting mobility alternatives.

The prime objective of the current investigation is to explore the variation of viscosity and thermal conductivity impacts on MHD convective flow over a moving non-isothermal vertical plate in presence of the viscous-dissipative heat and thermal-radiation. The compatible transformation of similarity are employed to obtain the non-linear ODE with the appropriate boundary conditions from the governing equations and the numerical solution of the boundary value problem so obtained are solved via MATLAB bvp4c solver. Naturally, the fluid viscosity and thermal-conductivity may vary from liquid to metal with temperatures and therefore, the impact of viscosity and thermal-conductivity in this investigation is quite significant. The physical parameters along with several influences on momentum, temperature, and concentration are explicated and portrayed with graphs. In addition, the velocity, temperature and concentration gradients at the surface are evaluated and displayed in tabular form. A decent agreement is found in the present outcomes with previously issued work. Furthermore, it is found that the growth of the thermal-radiation increases the gas temperature. The present study is useful for various industrial applications like metal and polymer extrusion, continuous casting, cooling process, nuclear plant and many more.

In the present work, solar water heating systems having nominal water usage of 24 cubic meters per day are considered. To identify the better option, both technologically and economically, a typical geographical location in Saudi Arabia, namely Abha, is considered. Internal rate of return (IRR) values for the solar collectors with glazing are found to be higher as compared with that of the unglazed type. The glazed type collectors are found to be more efficient, provide greater savings in fuel consumption, and result in the reduction of greenhouse gas (GHG) emissions. The findings of this study can be used for locations with similar types of climatic conditions in any part of the world.

Multicast communication over wireless networks has many potential applications, such as real-time audiovisual content distribution or digital signage systems with multiple remote terminals. However, today’s common 802.11 networks cannot fully support such applications at the link layer due to the technical challenges in achieving both high transmission rate and high reliability for multicast data transfers. Those challenges primarily emerge from the inapplicability of immediate acknowledgment mechanisms of unicast transmissions to the multicast case and the need for finding a common transmission rate for all receivers with diverse channel conditions. The research literature in this domain mainly addresses the problem at the higher layers of the protocol stack. In this article, we present a novel approach that combines wireless link rate selection with an adaptive packet-level Forward Error Correction (FEC) mechanism in order to achieve high-rate and highly reliable multicast in 802.11 wireless networks. The integration of FEC into the 802.11 MAC layer allows direct interaction between the transmission rate and the coding scheme. As a result, potential packet losses caused by higher link rates can be compensated by the adjustable redundancy provided by FEC. In combination with an aggregated receiver feedback mechanism, this yields improved transmission efficiency and reliability for wireless multicast. We investigate this approach in a simulation environment under a realistic wireless channel model in various application scenarios with up to 50 receivers. The results represent significant performance improvements, in terms of throughput, channel utilization, and packet loss, over the state-of-the-art methods for reliable wireless multicast.

To prevent flutter phenomena in a wind turbine, minimize vibration and increase the blades' life, a systematic analysis is required to investigate the effects between the cyclic aerodynamic loads and the structural performance of the turbine. A dynamic analysis of a straight-bladed vertical axis wind turbine (SB-VAWT) blade is investigated in this paper, and a simplified approach for the energy equations of an Eulerian beam subjected to twist and transverse bending deflections is introduced. The aerodynamic loads are estimated using the double multiple stream tube models. They are introduced into the dynamic model in the aeroelastic coupling, where the structural displacements are fed back to update the aerodynamic loads by utilizing the average acceleration method for the numerical integration of the equations. Reduced order modeling is then imposed based on the first modes of vibration. It is found that the structural displacement has little effect on the aerodynamic loads, and SBVAWTs experience higher transverse displacements compared with those in curved-blade VAWTs.

The recent developments in electric vehicles (EVs) causes several issues that have not been satisfactorily addressed. One of the foremost problems is the charging–discharging processes of EV batteries with diverse characteristics. Although a charging station is the first choice in this regard, a battery swap station (BSS) is also a suitable alternative solution as it eliminates long waiting periods and battery degradation due to fast charging. BSS has the capability to ensure prompt and efficient service for electric vehicles. Since BSS has a large number of battery systems, optimum planning of the charging–discharging operations of the batteries is critical for both BSS and the grid. This study presents an optimal charging–discharging schedule for multiple BSSs based on the swap demand of privately owned EVs and electric bus (EB) public transportation system. In addition, BSSs reinforce the power grid by providing ancillary services such as peak shaving and valley filling with demand response programs. In order to increase the flexibility of the operation, the mobile swapping station (MSS) concept, an innovative and dynamic service, is introduced to the literature and added to the model. The results indicate that BSS is an essential agent in the ancillary services market and the MSS concept is a yielding solution for both BSSs and power networks. Last, the data utilized in the study for swap demand calculation and power grid analysis are real-world data from Berlin, Germany.

In the face of the unpredictable nature of crises, it is expected that school principals exhibit behaviors aimed at maintaining the psychological well-being of teachers, students, and parents along with the educational processes. The sudden occurrence of the February 6, 2023 earthquakes, has presented an important crisis situation in which school principals are expected to display the desired behaviors. This research aims to reveal to what extent school principals demonstrated their crisis management skills in the context of the February 6, 2023 earthquake and what behaviors they exhibited during crisis management processes, based on teachers' opinions. The research was carried out with a mixed method approach in which quantitative and qualitative research methods were used together. The sample of the research consisted of 295 teachers, and the study group consisted of 18 teachers selected from the same sample by purposeful sampling method. Quantitative data of the research were collected using the crisis management scale, and qualitative data were collected using a semi-structured interview form. According to the quantitative results of the research, it was determined that the crisis management skill levels of school principals were "mostly" in the pre-crisis and post-crisis periods, and "sometimes" during the crisis period. When teachers' opinions were examined, it was seen that school principals were unprepared for crises arising from natural disasters such as earthquakes and that they mostly followed the Ministry of National Education guidelines and therefore lacked initiative. School principals who were able to take initiative took action to support the psychological health of teachers, students and parents by meeting their needs and gained their trust.

This study aimed to investigate distance education practices in higher education during the pandemic, focusing on lived experiences, and proposing a policy decision framework for future distance education in similar conditions. Additionally, the study aimed to establish a design framework for an Emergency Action Plan for similar crisis periods. In the study, a case study was used to provide a detailed examination of the current situation's characteristics. The study group consisted of 63 administrators from 34 universities who actively participated in decision-making during the pandemic. Data were collected through 11 online focus group interviews, and the Miles-Huberman Model was used for analysis. The study proposed a policy decision framework for distance education in the post-pandemic period, consisting of 11 headings such as blended learning, open course materials, and Distance Education Center structuring. Additionally, the study presented an emergency action plan framework consisting of six components, including keeping the technological infrastructure working and supporting face-to-face courses with distance education. This study provides valuable insights for universities in preparing for potential crises and improving their distance education practices.

Suicide, which is defined as an individual's intentionally attempting to end his or her life, is considered an important public health problem. In this study, it was aimed to examine the relationship between cognitive distortions and suicide probability in university students who are in age groups at risk for suicide, and to examine the mediating roles of the attachment dimensions of anxiety and avoidance, in this relationship. The study group consisted of 441 university students. In the study, Suicide Probability Scale, Experiences in Close Relationships Inventory, Cognitive Distortions in Relationships Scale and a personal information form were used as data collection tools. In accordance with the purpose of the study, a correlation analysis between variables and regression analyses were applied to examine the mediating roles of avoidance and anxiety in the relationship between cognitive distortions and suicide probability. The findings showed that there was a moderate positive correlational relationship between suicide probability scores and avoidance scores and there was a moderate positive correlational relationship between suicide probability scores and cognitive distortion scores. It was shown that there were low and moderate positive correlations between cognitive distortion scores, and avoidance and anxiety scores. The results of the mediation analyses showed that anxiety and avoidance had a partial mediating role in the relationship between cognitive distortion and suicide probability. The results were discussed in light of the literature.

In many fields, AI chatbots continue to be popular with new tools and attract the attention of universities, K12 schools, educational organizations, and researchers. The aim of this research is to review the research on AI chatbots by restricting it to the category of education and to examine this research from a methodological point of view. Therefore, we performed a systematic literature review with a sample of 37 SSCI articles published in the educational context. Within the scope of the selected studies, the advantages and disadvantages of AI chatbots in education for students and educators, as well as the types of chatbots used, year, keywords, and method were analyzed. According to the research results, increased motivation to learn and language skill development are advantages for students, while cost-effectiveness and reduced workload are advantages for educators. Limited interaction, misleading answers for learners, originality, and plagiarism are the most common disadvantages for educators. The study also includes research results and recommendations related to the methodological review.

The present study aims to investigate the potential variables that influence the faculty members' intention to continue using online learning systems during and after the pandemic based on extended Technology Acceptance Model (TAM) and Self Determination Theory (SDT), and to study individual differences between these variables. The methodology of the study was based on survey research and causal comparative methods. Convenience sampling method was used to identify the participants of the study, who are 302 faculty members working at twelve different state universities. Explanatory and confirmatory factor analysis (EFA-CFA) were used to test the factor structure of the data collection tool and to validate the tool through examining the model fit. Descriptive statistics were used to examine the distribution of the dependent variable scores of the participants, and one-way MANOVA was used to compare the variables based on individual differences. The findings indicated that CMP had the highest mean score, followed by the constructs of SDT (competence, autonomy, relatedness). A significant difference for male participants was observed in perceived ease of use and competence variables based on gender. No significant difference was found between the variables based on academic title. The present study established that all variables except relatedness indicated a significant difference that favors instructors with high and medium level online learning experience. It was concluded that the comparison of the motivational variables based on the individual differences of the instructors, which have critical importance in online education as well as in higher education, can contribute to the establishment of effective and sustainable quality learning environments (distance or hybrid) and to the existing literature.

Climate change and its effects are impacting our world more and more with each passing day. For this reason, we must ensure that our children, as the society of the future, grow up as individuals with high environmental awareness, being aware of climate change and its effects. The aim of this study is to inform students about the subject of climate change and to educate them as individuals with climate change literacy. In this context, a digital game about climate change and its effects was developed for primary school students. A qualitative research method and techniques were adapted in this research. Interview, observation, and document analysis techniques were used to ensure variety in data acquisition in the research. The study was conducted in the 2022 spring semester during the science courses. It was conducted at an urban primary school in Ankara. 22 fourth grade students were participated as control group and 23 fourth grade students were participated as treatment group in the study. It was determined that the students enjoyed this game, called Stop Climate Change; they had fun and simultaneously learned about concepts related to climate change.

With the outbreak of the COVID-19 pandemic, governments around the world were forced to take emergency measures in every aspect of life including education. Instead of the prevalent face-to-face mode of teaching, institutions turned to online teaching one by one. This brought many issues along with it. Because of distance education, it became quite challenging for teachers to maintain the multimodal nature of communication. This research aims to examine in-service teachers' perceptions and actual practices regarding multimodal instruction in online lessons during the COVID-19 pandemic from a descriptive point of view. The research followed an explanatory sequential mixed-methods design. Firstly, to examine the beliefs and preferences of teachers, a 24-item questionnaire (henceforth Multimodal Teaching Questionnaire) that was adapted from a previously developed questionnaire by Bulut et al. (2015) was used. Secondly, 72 hours of distance lessons from 36 teachers were observed via Zoom online conferencing tool. The results showed a discrepancy between the teachers' statements and their actual practices regarding multimodality. While most of them reported extensive use of multimodality in their instruction, observations showed that in the majority of their lessons, only one or two modes were used.

The structural mass of a building provides inherent thermal storage capability. Through sector coupling, the building mass can provide additional flexibility to the electric power system, using, for instance, combined heat and power plants or power-to-heat. In this work, a mathematical model of building inertia thermal energy storage (BITES) for integration into optimized smart grid control is introduced. It is shown how necessary model parameters can be obtained using generalized additive modeling (GAM) based on measurable building data. For this purpose, it is demonstrated that the ceiling surface temperature can serve as a proxy for the current state of energy. This allows for real-world implementation using only temperature sensors as additionally required hardware. Compared with linear modeling, GAM enable improved modeling of the nonlinear characteristics and interactions of external factors influencing the storage operation. Two case studies demonstrate the potential of using building storage as part of a virtual power plant (VPP) for optimized smart grid control. In the first case study, BITES is compared with conventionally used hot water tanks, revealing economic benefits for both the VPP and building operator. The second case study investigates the potential for savings in CO2 emission and grid connection capacity. It shows similar benefits when using BITES compared to using battery storage, without the need for hardware investment. Given the ubiquity of buildings and the recent advances in building control systems, BITES offers great potential as an additional source of flexibility to the low-carbon energy systems of the future.