Senior High School Science Teachers’ Attitudes, Knowledge and Skills in Alternative Assessment

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This study investigated senior high school science teachers’ attitudes, knowledge, and skills in alternative assessment within the Philippine context. A descriptive-quantitative research design was employed to gather data from 60 senior high school science teachers. The findings reveal that the teachers generally possess a high level of knowledge and skills in using alternative assessment methods. They hold positive attitudes towards alternative assessment, recognizing its benefits in improving student learning and assessment effectiveness. Some reservations exist, such as time consumption and difficulty in grading. The study also found no significant differences in teachers’ knowledge and attitudes based on current location in teaching, age, highest degree, and years of teaching experience. In contrast, teachers who received in-service training on alternative assessment demonstrated a significantly higher level of knowledge compared to those who did not. These findings highlight the potential of alternative assessment in senior high school science education. The need for ongoing professional development and support to enhance teachers’ knowledge and skills in effectively implementing these methods is crucial. To strengthen the positive attitudes and knowledge base identified in this study, it is recommended that senior high school science teachers be provided with professional development opportunities focused on alternative assessment methods. Additionally, resources and materials should be developed to support them in designing, implementing, and evaluating these assessments. By integrating alternative assessment practices into the curriculum and teacher training programs, we can ensure these effective methods become a mainstay in senior high school science education. Further research into the effectiveness of various alternative assessment methods would provide valuable insights for continual improvement.

Functions of Suffix -an in Javanese

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This article aims at describing functions of suffix –an in Javanese. Suffix -an is the most utilised suffix in Javanese, this suffix is used to create nouns, verbs and adjectives in word formation by maintaining or changing classes of the base. This suffix also functions as marker of reciprocal construction. Data in this article are gathered from novel and magazine which were analysed by using descriptive qualitative method. In the affixation process, suffix -an is used solely or together with prefixes and infixes as circumfixes. In several circumfixes, suffix -an is attached to reduplicated bases. The analysis also shows that the word formation process that uses suffix -na causes vowel shift.

Brain and Neuronal Changes Associated With Over Weight and Obesity

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Considerable brain and neural alterations associated with obesity affect behavior, thought, and emotional control. Changes in brain shape and function are revealed by neuroimaging research, especially in reward-processing regions like the striatum and prefrontal cortex. These alterations could result in a rise in the desire for foods rich in calories and a decline in judgment. Furthermore, neuroinflammation and changes in neurotransmitter systems are linked to obesity and can impact mood and cognitive function. To effectively treat obesity and the health problems it causes, it is essential to comprehend these brain alterations. Obesity is a complex medical problem that has significant effects on brain structure and function in addition to physical health. With an emphasis on changes in brain regions related to reward, food management, and cognitive function, this study examines the most recent research on the neuronal modifications linked to overweight and obesity.

Generative AI in the Categorisation of Paediatric Pneumonia on Chest Radiographs

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Paediatric pneumonia is a leading cause of morbidity and mortality worldwide, necessitating accurate and timely diagnosis. This study explores the application of Generative AI for categorising paediatric pneumonia using chest radiographs. Leveraging deep learning techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), we enhance image quality, generate synthetic training data, and improve model generalizability. The proposed framework integrates AI-driven feature extraction, convolutional neural networks (CNNs), and attention mechanisms to improve diagnostic accuracy. The results demonstrate significant improvements in classification performance compared to traditional methods, with a focus on interpretability and clinical usability.

Urinary Tract Infections as a Trigger for Dementia Progression

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Urinary tract infections (UTIs) are one of the infections that occur most frequently among the elderly and are now increasingly recognized as a significant factor in the causation of cognitive decline, particularly among those who had previously experienced dementia. This broad review focuses on epidemiological evidence, underlying pathophysiological mechanisms, and clinical consequences for examining the complex interrelationship between UTIs and dementia. Frequent UTIs have been shown to worsen the symptoms of dementia and accelerate cognitive deterioration due to the induction of systemic inflammation, neuro-inflammation, and disturbances of the blood-brain barrier. Delayed treatment due to diagnostic difficulties in the elderly further exacerbates cognitive effects. Preventive measures associated with alleviation of UTI-related cognitive burden include early detection, efficient management of UTI, and techniques reducing inflammation. It thus emphasizes on integrating multidisciplinary approaches as an improvement pathway towards better results in this highly vulnerable population.

The Influence of Training Approaches in In-Service Teacher Training on the Implementation of the Competence-Based Curriculum in Public Primary Schools in Kilifi County, Kenya

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In-service teacher training plays a pivotal role in the successful implementation of the Competence-Based Curriculum (CBC) in public primary schools. As Kenya transitions to CBC, it is crucial to examine how different training approaches affect teachers’ ability to deliver and assess the new curriculum effectively. This study investigates the impact of various training methodologies on CBC implementation in Kilifi County, focusing on the prevalence and effectiveness of traditional versus innovative teaching strategies. The findings reveal a dominance of traditional teaching methods, such as group projects, discussions, and lectures, with 93.2% of respondents frequently using group projects and 89.8% employing discussions/presentations regularly. In contrast, innovative methods like gamified learning, problem-solving, and brainstorming are significantly underutilized, with 46.9%, 53.4%, and 69.7% of respondents, respectively, never using these techniques. Chi-square analysis indicates that interactive training approaches, including practical discussions and guided practice, have a substantial positive effect on instructional delivery, assessment, and professional growth, evidenced by p-values of 0.000, 0.009, and 0.003. Theoretical training with immediate feedback is significantly effective in instructional delivery and assessment (p = 0.000) but less impactful on professional development (p = 0.186). Engaging activities and mentorship are crucial for enhancing instructional delivery and assessment (p = 0.000 and p = 0.002), while adapting teaching styles and using technology show inconsistent effects. The study concludes that while traditional methods are prevalent, integrating interactive and innovative training approaches is essential for effective CBC implementation. Recommendations include prioritizing practical, hands-on training that aligns with CBC principles, promoting underutilized innovative methods, strengthening mentorship for inexperienced teachers, and emphasizing continuous professional development through seminars and workshops to improve CBC execution.

Enhancing User Association to mmWave with Network Slicing and QoS Prioritization from Sub-6 GHz Bands

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In order to increase network capacity and user experience, a move toward millimeter-wave spectrum use has become necessary due to the constraints of sub-6 GHz frequencies and the rising demand for mobile data. In this paper, we propose a mathematical framework to dynamically improve user association with mmWave bands using network slicing and Quality of Service (QoS) priority. A utility maximization algorithm that balances user demand, network load, and signal quality across accessible spectrum bands is one of the multi-tier optimization techniques used in the suggested model. Optimal changeover locations from sub-6 GHz to mmWave are predicted using a Markov Decision Process (MDP) based on environmental factors and real-time user mobility. According to simulation data, under conditions of peak demand, this technique can improve user offload to mmWave by up to 50% while reducing congestion on sub-6 GHz bands by 30%. Furthermore, QoS priority ensures that customers encounter the least amount of disturbance when switching between frequency tiers by improving latency-sensitive application performance by an average of 20%. These results demonstrate how network slicing in conjunction with QoS-driven regulations can optimize network capacity, dynamically balance frequency allocation, and guarantee uninterrupted connectivity for next-generation mobile networks.

Artificial Intelligence-Driven Advances in Haemophilia Gene Therapy

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Hemophilia is the most frequent severe genetic haemorrhagic condition. Hemophilia A and B are caused by a lack or dysfunction of the factor VIII and factor IX proteins, respectively, and are distinguished by prolonged and heavy bleeding after minor trauma or even spontaneously. Treatments for hemophilia have been extremely expensive and required the infusion of plasma clotting factors throughout one’s life. The last few years have brought major breakthroughs in gene therapy that now hold real promise for possible curative options. Artificial intelligence has the potential to transform all levels of hemophilia gene therapy, from vector design to predictive modeling and biomarker identification. This review highlights selected applications of AI towards precision medicine including viral vector design, predictive modeling for gene editing, and deep phenotyping in hemophilia gene therapy. It can greatly improve the efficacy and safety of gene therapy through off-target effects prediction, optimization designs of delivery vectors, and determination of personalized combinations of treatments. Consequently, this will also enable accelerated biomarker development for disease diagnosis and monitoring. In such a way, artificial intelligence in hemophilia gene therapy will revolutionize the framework of treatment and make it personalized or even curative for patients all over the world.

The Effect of Employee Engagement (Vigor, Dedication, and Absorption) on Job Satisfaction at PT Pelindo 1

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This study examines the effect of employee engagement variables—vigor, dedication, and absorption—on job satisfaction. The research subjects consisted of all employees of PT Pelindo 1 in 2020. A total of 225 employees were selected as the sample using a simple random sampling method. Primary data were collected through a questionnaire instrument. To test the research hypotheses, data were analyzed using a multiple linear regression model. The results indicate that employee engagement and vigor have a significant but weak positive effect on job satisfaction. In contrast, dedication and absorption show a positive but statistically insignificant effect. The coefficient of determination (R²) is 14%, suggesting that vigor, dedication, and absorption are weak predictors of job satisfaction.

Sustainability Leadership and Employee Engagement: A Key Driver of Productivity in Indonesian Companies

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The background of this research is the challenge faced by Indonesia’s economic development in achieving sustainable growth without increasing carbon emissions. This study aims to analyze the influence of leadership and the implementation of sustainability principles on productivity, with employee engagement as a mediator in public and private companies in Indonesia.

The research adopts a quantitative approach, utilizing data analysis through the Structural Equation Modelling Partial Least Square (SEM PLS) method. The study sample comprises 110 respondents from public and 110 respondents from private companies, with data collected through questionnaire surveys with G*Power 88%. Research variables include sustainability leadership, implementation of sustainability principles, employee engagement, and productivity.

The findings reveal that employee engagement significantly influences employee productivity and mediates the effect of sustainability leadership on employee productivity. These findings highlight the critical role of sustainability-based leadership in fostering employee engagement and enhancing productivity.

The study concludes that Indonesian companies, particularly those oriented toward sustainability, need to prioritize the development of sustainability-based leadership and policies to enhance employee engagement, thereby supporting long-term productivity.