Excessive Reliance Research Articles

Adapting EFL materials and its influences on Indonesia secondary school students’ language learning

Educators encounter challenges in performing materials adaptation stemming from time limitations, institutional responsibilities, administrative demands, and a dearth of formal training in materials development resulting in improper adaptation and ineffective learning activities. This research investigated the process of adapting materials as a qualitative case study. The data were gathered from a senior high school teacher in Banda Aceh, Indonesia, by means of document analysis (e.g., teaching materials), observation, and interview. The results revealed that the instructor frequently added and modified educational resources to facilitate learning through several modalities. The act of deleting, simplifying, and rearranging was occasionally performed. Nevertheless, students’ reflection on course contents, inadequate level of difficulty, demanding tasks, and excessive reliance on first language remained as issues. This study emphasizes the role of adaptation in promoting meaningful learning experiences and urges the integration of culturally relevant themes with a variety of learning activities. Educators can use these findings to support professional developments focused on effective material adaption tactics, allowing them to reflect on their existing methods, try new approaches, and interact with colleagues to share successful practices.

Transcending traditional metrics: Evaluating school leadership effectiveness through emotional intelligence and qualitative instruments

This paper examines the integration of school leaders' emotional intelligence and qualitative instruments into commonly used metrics for measuring school leadership effectiveness. Traditionally, these metrics rely heavily on questionnaires to assess leadership effectiveness. However, a weakness of these metrics is their excessive reliance on numerical data, which overlooks the qualitative dimensions of leadership. This study utilises Trait Emotional Intelligence Theory and Team Emotional Intelligence as units of analysis to redefine school leadership assessment. To make sense of the related literature and theories used in the study, an integrative literature review was employed as the methodological approach. The findings indicate that emotional intelligence significantly contributes to leadership effectiveness. The study also reveals that leaders who are self-aware of their emotional composition and its impact are more effective than those who are not. Furthermore, the results suggest that leaders who are cognisant and responsive to the emotional dynamics of their teams benefit from enhanced team cohesion, collaboration, and goal attainment. The study emphasises the importance of incorporating adaptable qualitative tools alongside quantitative instruments to accurately measure leadership effectiveness. Thus, this study adds to the existing literature by proposing a theoretically informed and adaptable framework that integrates an emotional intelligence construct and qualitative instruments for assessing school leadership effectiveness.

A Fish Target Identification and Counting Method Based on DIDSON Sonar and YOLOv5 Model

In order to more accurately and quickly identify and count underwater fish targets, and to address the issues of excessive reliance on manual processes and low processing efficiency in the identification and counting of fish targets using sonar data, a method based on DIDSON and YOLOv5 for fish target identification and counting is proposed. This study is based on YOLOv5, which trains a recognition model by identifying fish targets in each frame of DIDSON images and uses the DeepSort algorithm to track and count fish targets. Field data collection was conducted at Chenhang Reservoir in Shanghai, and this method was used to process and verify the results. The accuracy of random sampling was 83.56%, and the average accuracy of survey line detection was 84.28%. Compared with the traditional method of using Echoview to process sonar data, the YOLOv5 based method replaces the step that requires manual participation, significantly reducing the time required for data processing while maintaining the same accuracy, providing faster and more effective technical support for monitoring and managing fish populations.

Datafication of Care: Security and Privacy Issues with Health Technology for People with Diabetes

Through the conceptual framework of datafication, dataism and dataveillance, this study investigates beliefs and attitudes regarding datafication and the related privacy and security concerns among individuals with Type 1 diabetes. Qualitative research was conducted through interviews among fifty-two individuals with Type 1 diabetes in Poland and Italy. The findings reveal a dynamic interplay between self-discipline and empowerment. The majority of interviewees emphasized the benefits of technologies for gaining a better understanding of their health condition and for more effective disease management. However, a minority of interviewees perceived the negative effects of datafication, including dataveillance, which leads to hyper-control of the disease, and dataism, characterized by excessive reliance on and dependency on technology. Critical beliefs about technologies fuelled rejection attitudes, leading some interviewees to suspend or abandon their use. Lastly, reflexivity on privacy and security issues appears to be low, particularly among older individuals with lower levels of education and socioeconomic status. This results in a poor understanding and underestimation of the potential risks associated with security and privacy. The findings increase the understanding of the factors that can facilitate or hinder the adoption of technology among people with diabetes.

Multimodal graph learning with framelet-based stochastic configuration networks for emotion recognition in conversation

The multimodal emotion recognition in conversation (ERC) task presents significant challenges due to the complexity of relationships and the difficulty in achieving semantic fusion across various modalities. Graph learning, recognized for its capability to capture intricate data relations, has been suggested as a solution for ERC. However, existing graph-based ERC models often fail to address the fundamental limitations of graph learning, such as assuming pairwise interactions and neglecting high-frequency signals in semantically-poor modalities, which leads to an over-reliance on text. While these issues might be negligible in other applications, they are crucial for the success of ERC. In this paper, we propose a novel framework for ERC, namely multimodal graph learning with framelet-based stochastic configuration networks (i.e., Frame-SCN). Specifically, framelet-based stochastic configuration networks, which employ 2D directional Haar framelets to extract both low- and high-pass components, are introduced to learn the unified semantic embeddings from multimodal data, mitigating prediction biases caused by an excessive reliance on text without introducing an unnecessarily large number of parameters. Also, we develop a modality-aware information extraction module that is able to extract both general and sensitive information in a multimodal semantic space, alleviating potential noise issues. Extensive experiment results demonstrate that our proposed Frame-SCN outperforms many state-of-the-art approaches on two widely used multimodal ERC datasets.

Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies.

ChatGPT, a generative pretrained transformer, has garnered global attention and sparked discussions since its introduction on November 30, 2022. However, it has generated controversy within the realms of medical education and scientific research. This paper examines the potential applications, limitations, and strategies for using ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant use in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nonetheless, numerous challenges accompany these advancements. In the context of education, it is of paramount importance to prevent excessive reliance on ChatGPT and combat academic plagiarism. Likewise, in the field of medicine, it is vital to guarantee the timeliness, accuracy, and reliability of content generated by ChatGPT. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies for addressing them. First, the risk of overreliance on ChatGPT and academic plagiarism must be mitigated through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. The integration of contemporary pedagogical methodologies in conjunction with the use of ChatGPT serves to enhance the overall quality of medical education. To enhance the professionalism and reliability of the generated content, it is recommended to implement measures to optimize ChatGPT's training data professionally and enhance the transparency of the generation process. This ensures that the generated content is aligned with the most recent standards of medical practice. Moreover, the enhancement of value alignment and the establishment of pertinent legislation or codes of practice address ethical concerns, including those pertaining to algorithmic discrimination, the allocation of medical responsibility, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT's positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.

Energy poverty and environmental sustainability in Sub‐Saharan Africa: Evidence from method of moments quantile regression

AbstractThe enormous ensembles of energy resources have elevated Sub‐Saharan Africa (SSA) to a prominent position in the world energy scene as a leading region in forming a sustainable energy future. However, over half of the population in the region lives without electricity and less than 20% of the population has access to clean fuels. The excessive reliance on fossil fuels such as coal, kerosene, and solid biomass has implications for environmental outcomes in SSA. Nevertheless, the environmental impact of energy poverty from the SSA perspective remains an under‐explored terrain in the research landscape. In this context, to address the pressing challenges of energy poverty and environmental sustainability, this study aims to explore the threat energy poverty poses on the ecological footprints, focusing on “land‐use, carbon footprint, forestry resources, and fishing ground” across 35 SSA countries using disaggregated data covering the period 2000 and 2021. This study employs the novel Method of the Moments Quantile Regression following the Stochastic Impacts by Regression on Population, Affluence and Technology model as the reference theoretical and analytical framework. The findings disclose heterogeneous effects of rural and urban energy poverty on ecological footprint items. When comparing the magnitudes, we found that rural energy poverty has a more detrimental impact on land‐use than urban energy poverty. Energy poverty is found to have no reliable power to explain the variation in carbon footprint and fishing ground. In the meantime, urban energy poverty exerts a positive effect on forestry resource sustainability. Surprisingly, population density is found to have a significant and desirable impact on land‐use. Based on the obtained results, numerous policy suggestions have been discussed along with some prospects for future research.

Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: a narrative review

While generative artificial intelligence (AI) technology has become increasingly competitive since OpenAI introduced ChatGPT, its widespread use poses significant ethical challenges in research. Excessive reliance on tools like ChatGPT may intensify ethical concerns in scholarly articles. Therefore, this article aims to provide a comprehensive narrative review of the ethical issues associated with using AI in academic writing and to inform researchers of current trends. Our methodology involved a detailed examination of literature on ChatGPT and related research trends. We conducted searches in major databases to identify additional relevant articles and cited literature, from which we collected and analyzed papers. We identified major issues from the literature, categorized into problems faced by authors using nonacademic AI platforms in writing and challenges related to the detection and acceptance of AI-generated content by reviewers and editors. We explored eight specific ethical problems highlighted by authors and reviewers and conducted a thorough review of five key topics in research ethics. Given that nonacademic AI platforms like ChatGPT often do not disclose their training data sources, there is a substantial risk of unattributed content and plagiarism. Therefore, researchers must verify the accuracy and authenticity of AI-generated content before incorporating it into their article, ensuring adherence to principles of research integrity and ethics, including avoidance of fabrication, falsification, and plagiarism.

Back to Basics: A Clinical Medicine to Safeguard International Cooperation.

The medical profession is currently undergoing a significant transformation. In recent decades, we have seen the emergence and implementation of new diagnostic tools, therapeutic targets, and technical procedures that have revolutionized our clinical practice. These resources have undoubtedly improved patient outcomes but have also led to excessive reliance on technology. This overreliance can limit the new generation's capacity to provide humane and comprehensive patient care and develop critical thinking skills. In this article, we reflect on the urgent impact of this trend on pediatric international cooperation and propose workable solutions to this problem. We stress the importance of maintaining a patient-centered approach in the face of these technological advancements, as it ensures that the patient's needs remain at the forefront of our practice.

Predicting power and solar energy using neural networks and PCA with meteorological parameters from Diass and Taïba Ndiaye

The excessive reliance on conventional fossil fuel-based resources poses a significant threat to our environment. To mitigate this impact, it has become increasingly crucial to increase the integration of intermittent and non-polluting energy sources into our electrical grids. However, while this higher penetration rate brings benefits such as improved producer satisfaction and reduced fossil fuel consumption, it also presents challenges for traditional non-smart electrical networks. To promote intermittent energy sources effectively and maintain a balance between consumption and production, accurate forecasting of these energy outputs plays a vital role. This research paper focuses on studying the application of artificial neural networks for predicting the power and energy output of the Diass solar power plant in the short and medium term. The proposed approach utilizes not only the meteorological data from the city where the power plant is located but also data from a nearby city with a data acquisition station. Principal component analysis (PCA) is employed to select the relevant variables for the prediction model. Furthermore, the results obtained from our approach are compared to existing literature that solely uses meteorological data from the power plant's location. The comparison shows that our method achieves more satisfactory results, with mean absolute errors and root mean square errors of 0.0223 KWh and 0.003 KWh, respectively, and a prediction accuracy of 94.57% in terms of energy and power. It is worth noting that the computational resource requirements for our approach are higher, with simulation times ranging between 1788 seconds and 2201 seconds. By utilizing a broader range of data sources and employing advanced techniques like artificial neural networks, this research contributes to improving the accuracy of solar power generation forecasts. The findings highlight the potential of incorporating additional data inputs and advanced modeling techniques to enhance the performance of renewable energy systems, paving the way for a more sustainable and efficient energy future.

Application Of SMOTE To Address Class Imbalance In Diabetes Disease Classification Utilizing C5.0, Random Forest, And SVM

The implementation of SMOTE to tackle class imbalance in classification frequently results in suboptimal outcomes, owing to the intricacy of the dataset and the multitude of attributes at play. Consequently, alternative classification models were explored through experimentation to gauge their precision. This research aims to compare the precision of C5.0, Random Forest, and SVM classification models both with and without SMOTE. The methodology encompasses dataset selection, an overview of classification algorithms (C5.0, Random Forest, SVM), SMOTE technique, validation via split validation, preprocessing involving min-max normalization, and execution evaluation utilizing confusion matrices and AUC analysis. The dataset was sourced by Kaggle, specifically to rectify class imbalance in a diabetes dataset using SMOTE, consisting of 768 instances, with 268 samples for diabetic cases and 500 samples for non-diabetic cases. Prior to SMOTE application, the classification precision for C5.0, Random Forest, and SVM were 0.714, 0.733, and 0.746 respectively, with corresponding AUC values of 0.745, 0.824, and 0.799. Post-SMOTE, the precision depicts for the same techniques were 0.603, 0.727, and 0.727, with AUC values of 0.734, 0.831, and 0.794 respectively. It can be inferred that there's minimal impact post-SMOTE across the three classification models due to potential overfitting on the dataset, leading to excessive reliance on synthesized data for minority classes, resulting in diminished model execution, precision, and AUC scores.

Game-changing intelligence: Unveiling the societal impact of artificial intelligence in game software

Efficient artificial intelligence (AI) in gaming software optimizes development, improves user experiences, and increases industrial production, all of which contribute positively to economic development and technical advancement. Possible drawbacks include employment displacement, ethical problems, algorithmic biases, and excessive reliance on AI; everyone has an impact on society’s dynamics and principles. In this study, we proposed a novel method called Fine-Tuned Ring Toss Game Optimization Adaptive Artificial Neural Network (FRTGO-AANN) to improve user experience in game software, examine social implications to promote ethical growth, explore AI transformational significance in games, and shape new game-play. In this research, we employ a dataset of 250 video game projects. The collected data are undergoing feature extraction by using a Principal Component Analysis (PCA) method. We used a few parameters for our suggested and current approaches to analyze the research’s findings. Our proposed FRTGO-AANN method achieves superior results like accuracy (96.7 %), precision (92.5 %), F1-score (95.3 %), recall (90.6 %).AI in gaming software represents a game changer, transforming user experience, narrative intricacy, along with the world of virtual reality. It has a profound social influence, altering entertainment and promoting innovation, along with difficult ethical considerations.

RESEARCH ON OPTIMIZATION OF MOTORWAY ROUTE DESIGN SCHEME IN MOUNTAIN AREAS BASED ON ENTROPY WEIGHT-TOPSIS MODEL

During the design process of a new mountainous motorways, multiple route schemes are often proposed for a comprehensive design effort. Each route scheme will have its advantages and disadvantages, so it is often difficult to choose a route scheme. Usually the expert decision method is used to screen the route schemes, but this method mainly relies on the personal experience of experts, and it is difficult to measure the criteria, which can lead to the embarrassing situation that different experts do not agree on the choice of routes.In order to optimize the route scheme for the design process of mountainous motorways and improve the efficiency and scientificity of route scheme selection, evaluation indicators were selected from traffic safety, construction economy, and environmental friendliness. The Entropy Weight Method (EWM) was used to assign the weight of the evaluation indicators. By improving the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the problem of subjective opinions and excessive reliance on objective data by designers in the multi factor evaluation process was overcome. A EWM-TOPSIS evaluation model was proposed. By analyzing specific examples of mountainous motorway construction, research results were obtained. The results indicate that the model can reflect the designer's intention towards the route scheme and the actual construction project. There is a high degree of consistency with the expert's empirical judgment, which verifies the feasibility and accuracy of the model. This model can provide reliable reference and basis for the decision-making of motorway route schemes in mountainous areas.

Protecting labor rights: Contract design and coordination between brand firms and suppliers

This paper explores the design of a contract aimed at coordinating brand firms and their suppliers to collectively protect the suppliers’ labor rights. It finds that a unilateral cost-sharing contract can incentivize suppliers to protect labor rights, while improving the profits of both brand firms and their suppliers. However, such a contract fails to coordinate the supply chain because of an insufficient incentive for both involved parties. Two improved contracts of this contract can coordinate brands firms and their suppliers to improve their efforts to protect labor rights under certain conditions while failing to achieve coordination. Accordingly, this research proposes a bilateral cost- and revenue-sharing contract. The results indicate that with an appropriate revenue-sharing proportion, this contract effectively encourages brand firms and their suppliers to participate in protecting labor rights and coordinate the supply chain. In addition, an increase in the proportion of prosocial consumers incentivizes brand firms and their suppliers to improve labor rights. Nonetheless, consumers’ excessive reliance on brand goodwill to evaluate the total supply chain efforts to protect labor rights will reduce their efforts. Finally, as the impact of labor rights protection efforts on reference prices increases, it can stimulate brand companies and suppliers to increase investment in improving labor rights. At this high impact level, the reference price in the centralized model may surpass that in the unilateral cost-sharing contract. However, if supply chain members invest little effort to protect labor rights, the increase in this impact could reduce consumers’ reference price.

Development and Improvement of Celebrity Foundations in China

As a form of contemporary philanthropic organizations, celebrity foundations have emerged as a new entity in the field of social charity. Supported by the personal identity and influence of celebrities, the development of celebrity foundations is significantly influenced by their fame and impact. Compared to traditional foundations, celebrity foundations possess broader influence and quicker fundraising capabilities. However, Chinese celebrity foundations also encounter challenges during operation, such as excessive reliance on celebrity effects, insufficient management capabilities, lack of transparency, and insufficient specialization. In response to these challenges, celebrity foundations should improve in the following aspects: firstly, celebrity foundations should moderately separate from celebrities and establish their own brand and credibility. Secondly, internal governance structures need further enhancement, with increased supervision and standardization of fund utilization and project management, as well as collaboration with professional teams to provide more specialized support for celebrity foundations. Lastly, talent recruitment and cultivation should be emphasized to establish a specialized team belonging to the foundations brand, thus enhancing the foundations management level and operational efficiency.

Development and Improvement of Celebrity Foundations in China

As a form of contemporary philanthropic organizations, celebrity foundations have emerged as a new entity in the field of social charity. Supported by the personal identity and influence of celebrities, the development of celebrity foundations is significantly influenced by their fame and impact. Compared to traditional foundations, celebrity foundations possess broader influence and quicker fundraising capabilities. However, Chinese celebrity foundations also encounter challenges during operation, such as excessive reliance on celebrity effects, insufficient management capabilities, lack of transparency, and insufficient specialization. In response to these challenges, celebrity foundations should improve in the following aspects: firstly, celebrity foundations should moderately separate from celebrities and establish their own brand and credibility. Secondly, internal governance structures need further enhancement, with increased supervision and standardization of fund utilization and project management, as well as collaboration with professional teams to provide more specialized support for celebrity foundations. Lastly, talent recruitment and cultivation should be emphasized to establish a specialized team belonging to the foundations brand, thus enhancing the foundations management level and operational efficiency.

Exploring student perspectives on generative artificial intelligence in higher education learning

This study examined the perspectives of Ghanaian higher education students on the use of ChatGPT. The Students’ ChatGPT Experiences Scale (SCES) was developed and validated to evaluate students’ perspectives of ChatGPT as a learning tool. A total of 277 students from universities and colleges participated in the study. Through exploratory factor analysis, a three-factor structure of students' perspectives (ChatGPT academic benefits, ChatGPT academic concerns, and accessibility and attitude towards ChatGPT) was identified. A confirmatory factor analysis was carried out to confirm the identified factors. The majority of students are aware of and recognize the potential of Gen AI tools like ChatGPT in supporting their learning. However, a significant number of students reported using ChatGPT mainly for non-academic purposes, citing concerns such as academic policy violations, excessive reliance on technology, lack of originality in assignments, and potential security risks. Students mainly use ChatGPT for assignments rather than for class or group projects. Students noted that they have not received any training on how to use ChatGPT safely and effectively. The implications for policy and practice are discussed in terms of how well-informed policy guidelines and strategies on the use of Gen AI tools like ChatGPT can support teaching and improve student learning.

Two-Sex Life Table Analysis for Optimizing Beauveria bassiana Application against Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae).

Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae) is a highly dispersive, polyphagous insect pest that severely defoliates crops. Excessive reliance on synthetic insecticides leads to ecological pollution and resistance development, urging scientists to probe eco-friendly biopesticides. Here, we explore the virulence of an entomopathogenic fungus, Beauveria bassiana, against S. exigua, resulting in 88% larval mortality. Using an age-stage, two-sex life table, we evaluated the lethal and sublethal effects of B. bassiana on the demographic parameters of S. exigua, including survival, development, and reproduction. Sublethal (LC20) and lethal concentrations (LC50) of B. bassiana impacted the parental generation (F0), with these effects further influencing the demographic parameters of the first filial generation (F1). The infected F1 offsprings showed a reduced intrinsic rate of increase (r), mean generation time (T), and net reproduction rate (R0). Larval developmental duration varied significantly between the control (10.98 d) and treated groups (LC20: 10.42; LC50: 9.37 d). Adults in the treated groups had significantly reduced lifespans (M: 8.22; F: 7.32 d) than the control (M: 10.00; F: 8.22 d). Reduced fecundity was observed in the B. bassiana-infected groups (LC20: 313.45; LC50: 223.92 eggs/female) compared to the control (359.55 eggs/female). A biochemical assay revealed elevated levels of detoxification enzymes (esterases, glutathione S-transferases, and acetylcholinesterase) in the F0 generation after B. bassiana infection. However, the enzymatic activity remained non-significant in the F1 generation likely due to the lack of direct fungal exposure. Our findings highlight the enduring effects of B. bassiana on the biological parameters and population dynamics of S. exigua, stressing its use in eco-friendly management programs.

South Africa’s Housing Financialisation Crises and Social Resistance

The world’s most unequal country suffers from various housing crises, especially when it comes to excessive reliance upon a private sector prone to market failures, especially affordability. State housing finance strategy during the transition from apartheid to democracy relied upon augmentation of formal banking finance so as to promote home ownership. But as macro-economic conditions changed in the late 1980s, the resulting mass defaults on individual families’ home mortgage bonds led not only to foreclosures by a (white) state, but (black) working-class resistance organised by the country’s leading urban social movement, known as the ‘bond boycott.’ Even after democracy, a worsening housing backlog coincided with resurgence of household debt crises in the wake of the 2008 global financial meltdown. That generated a new housing finance strategy led by Mastercard and a local fintech firm (supported by the World Bank): collateralisation of welfare grants which in turn allowed debit orders for repayment of microfinance (typically used for minor home improvements). Again, social resistance played an important role, as the strategy caused even worse personal debt crises, and a welfare NGO’s successful fight to close Mastercard’s partner. But beyond periodic revolts of these sorts, a durable housing finance policy has remained elusive.

Code-Switching in the Second Language Classroom: A Narrative Study of a Saudi English Language Teacher’s Experience

This narrative study explores the use of code-switching in the second-language classroom from the perspective of a Saudi English language instructor. The aim was to explore how the instructor experienced code-switching as a student and used it in her teaching. The main research question guiding the study was: How does a Saudi English language teacher perceive and utilize code-switching in her teaching practice, and how does this relate to her experiences as a language learner? The study holds significance in contributing to the ongoing discourse on code-switching in second-language classrooms, providing a nuanced understanding from the perspective of an individual who has experienced it as both a learner and a teacher. Examining code-switching from the viewpoint of a Saudi English instructor can offer valuable insights into teaching practices, student engagement, cognitive benefits, and sociolinguistic dynamics within the Saudi educational context, where Arabic is the dominant language and English is increasingly important. The study utilized a narrative inquiry approach involving a series of interviews. However, excessive reliance on code-switching may hinder language acquisition, and learners should gradually reduce their dependence on it as their proficiency improves. The study provided valuable insights into the lived experiences of second language learners and teachers, underscoring the need for further research using this methodology in the field of second language acquisition.