Analysis of Student Needs for Interactive Physics Learning Module Based on Agricultural Systems at SMK Negeri Luyo

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This study aims to analyze the needs of students at SMK Negeri Luyo for an interactive physics learning module based on agricultural systems in Polewali Mandar. Physics learning at this school still faces several challenges, such as the lack of connection between physics material and students’ daily lives, the limited availability of contextual teaching materials, and the use of conventional teaching methods. As a result, students find it difficult to understand physics concepts because the material taught remains abstract and is not sufficiently linked to agricultural practices, which are a significant part of their lives. This study employs a descriptive method with a qualitative and quantitative approach. Data were collected through observations, teacher interviews, and questionnaires distributed to students to determine their needs for an interactive learning module. The results indicate that students require more interactive and context-based teaching materials to understand the relationship between physics concepts and their daily lives, particularly in the agricultural sector. Most students prefer learning media that incorporate interactive simulations, project-based experiments, and case studies that connect physics concepts with agricultural practices in their area. Furthermore, teachers face challenges in developing contextual teaching materials due to time constraints and limited resources. Based on these findings, the study emphasizes the necessity of developing an interactive physics learning module based on agricultural systems to enhance students’ understanding, interest, and motivation in learning. The development model used in this study is the ADDIE model (Analysis, Design, Development, Implementation, Evaluation), which enables systematic module design tailored to the needs of students and teachers.

The Role of AI in Customer Sentiment Analysis for Strategic Business Decisions

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Customer sentiment analysis has become a vital tool for businesses seeking to understand consumer emotions, preferences, and feedback in real-time. Traditional sentiment analysis methods often struggle with scalability, contextual interpretation, and processing unstructured data from diverse sources such as social media, customer reviews, and survey responses. Artificial Intelligence (AI) has revolutionized this domain by leveraging advanced Natural Language Processing (NLP) techniques, including transformer-based models (e.g., BERT, GPT), recurrent neural networks (RNNs), and sentiment-aware embeddings, to extract nuanced insights with higher accuracy and efficiency. AI-driven sentiment analysis enhances customer experience, optimizes marketing strategies, and informs strategic business decisions in areas such as product development and risk management. However, challenges such as algorithmic bias, data privacy concerns, and model interpretability remain critical hurdles. This paper explores these challenges while discussing potential solutions, such as debiasing techniques, federated learning for privacy-preserving sentiment analysis, and explainable AI approaches. Furthermore, it highlights future advancements that could improve the accuracy, reliability, and ethical application of AI in sentiment analysis, ultimately strengthening data-driven decision-making for businesses in dynamic market environments.

Optimization of Wireless Mesh Networks for Disaster Response Communication

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Wireless Mesh Networks (WMNs) have emerged as a resilient and adaptable solution for disaster response communication, offering self-healing and self-organizing capabilities that ensure uninterrupted connectivity in emergency scenarios. Traditional communication infrastructures often fail due to network congestion, power outages, and physical damage during disasters, necessitating an optimized approach for rapid and reliable data transmission. This study presents an AI-optimized WMN framework aimed at enhancing network performance by improving packet delivery ratio (PDR), reducing end-to-end delay, optimizing energy consumption, increasing network throughput, and strengthening security. Simulations conducted in MATLAB Simulink compare the performance of AI-optimized routing with conventional protocols such as AODV (Ad hoc On-Demand Distance Vector) and OLSR (Optimized Link State Routing). Results demonstrate that AI-optimized routing achieves a 15.5% higher PDR, 43% lower delay, 49% increased throughput, and 30% reduced energy consumption compared to traditional approaches. Furthermore, an AI-driven Intrusion Detection System (IDS) improves network security by increasing attack detection accuracy to 94.6% while reducing false positive rates to 5.2%. The findings highlight the significance of AI-based routing optimization in disaster scenarios, ensuring robust, energy-efficient, and secure communication for first responders and affected communities. Future research will explore hybrid AI-blockchain security mechanisms, 5G and satellite network integration, and real-world experimental validation to further enhance WMN resilience in extreme disaster conditions.

Secure and Efficient Routing in Fog-Enabled VANETs: A Clustering-Based Approach

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Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation systems (ITS) by enabling seamless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, VANETs face significant challenges related to security, routing efficiency, and dynamic load balancing. This paper proposes a novel approach integrating clustering-based routing, fog computing, and authentication mechanisms to enhance network performance. The article proposes a new method for node authentication and redistribution of loads in vehicular ad-hoc networks (VANETs). The proposed method is designed to do better for VANETs in all aspects related to secure node authentication and the efficient load assignment among fog nodes. The approach uses a polynomial-based node authentication protocol and balances the network load dynamically by evaluating two parameters: Network Availability Bandwidth (NAB) and request count. Simulation-based performance evaluation was carried out for comparisons with existing algorithms. Metrics of comparison included throughput, packet delivery ratio (PDR), and latency. The proposed method clearly shows all improvements over existing algorithms. Throughput increased by 8591.86 packets per second; PDR improved to 0.833; latency was cut down to 6.4951 seconds, which makes it a potential candidate for performance enhancement in VANETs.

Innovative Packaging Design to Empower Local SMEs: A Case Study on Honey Pineapple Products

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Innovative packaging design plays a pivotal role in empowering local SMEs by enhancing the marketability and competitiveness of their products. This study focuses on honey pineapple products in Pemalang, Central Java, where local producers face challenges such as low product value due to unattractive packaging and limited promotional efforts. Through a workshop conducted with SMEs and local women’s farming groups (Kelompok Wanita Tani), this program provided training on designing packaging that adheres to functional, aesthetic, and ecological standards. The results demonstrate significant improvements in packaging design, which boosted product appeal and market competitiveness. This study underscores the importance of packaging innovation as a strategic tool for SME growth, with implications for local economic development and sustainability.

The Role of Sino-Vietnamese Words in Reading Comprehension Texts of Literature Textbooks

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Sino-Vietnamese words play an extremely important role in the Vietnamese vocabulary system. Sino-Vietnamese words help the Vietnamese national language become refined and elegant. Besides, the expression in social communication is accurate, polite and cultural. The teaching of Sino-Vietnamese words in Vietnamese schools has achieved certain results, however, the teaching practice shows that the skills of recognizing and accurately using Sino-Vietnamese words are not simple for students. Therefore, we chose the research of the role of Sino-Vietnamese words in reading comprehension texts of Literature textbooks at the secondary school level, belonging to the book series Connecting knowledge with life to analyze the influence of Sino-Vietnamese words on the ability to identify, decode meaning and expand students’ vocabulary in the reading comprehension process. The study aims to evaluate the level of support of Sino-Vietnamese words in improving students’ linguistic thinking, reasoning ability and text perception.

A Model for Developing an Institutional Greening Action Plan (GAP) for TVET: Strategies for Adapting and Alignment with Global Green Policies and Mandated Trends in Sustainable Education

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The integration of sustainability into Technical and Vocational Education and Training (TVET) is essential for equipping a workforce capable of addressing contemporary environmental challenges and fostering a green economy. This article explores a comprehensive framework for Greening TVET, emphasizing the importance of curriculum development, stakeholder engagement, and the establishment of clear, actionable goals based on the UNESCO-UNEVOC Framework ESD-2030 for TVET. A concrete detailed developmental design and plan phases in congruence with the UNESCO framework are proposed and discussed in the light of the TVET’s specifics. Analytically it is examined the significance of engaging a diverse range of stakeholders—including educators, industry partners, students, and community members—to cultivate a collective understanding of sustainability’s importance. This collaborative approach facilitates the design and implementation of effective Greening strategies that are responsive to local needs and global trends. The necessity of plans and thorough needs assessments, developing strategic action plans, and implementing pilot programs are analyzed to ensure continuous evaluation and improvement of greening initiatives. By prioritizing sustainability in vocational education, institutions can play a pivotal role in shaping a workforce that is not only skilled but also socially responsible and environmentally conscious, thereby driving progress towards a more sustainable future.

EcoCycle: A Deep Learning-Based Waste Categorization and Management System for Sustainable Smart Cities

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Waste management is a critical environmental and economic issue worldwide. Existing waste segregation ac- tivities are inefficient, resulting in high landfill contributions and environmental contamination. In this paper, an artificial intelligence-based waste categorization and management system, EcoCycle, is proposed that utilizes deep learning models like VGG16, ResNet50, and DenseNet121 for automatic classification of waste materials. EcoCycle is equipped with a gamification system based on mobile, a marketplace for recyclables supported by blockchain, and an IoT-based network of intelligent bins for real-time monitoring. Experimental results show 92.36% classification accuracy with DenseNet121, which is improved compared to other implementation results. User survey with 500 users shows a 98% positive effect on user experience and increased awareness about sus- tainability issues. The proposed system contributes significantly towards processes related to circular economies and the goals of smart city initiatives, and it has high global applicability potential for urban waste management systems.

Different Learning, Learning Interests, and The Use of Technology in Learning on Economic Learning Outcomes: An Empirical Study with Family Support as Mediation on High School Students

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This study aims to analyze the influence of Differentiated Learning, Learning Interest, and Technology Utilization on Economic Learning Outcomes among students at SMA Negeri 11 Makassar, with Family Support as a mediating variable. Using a quantitative approach with a descriptive method, data were collected through questionnaires, interviews, and documentation. The research sample consisted of 92 students, selected through stratified sampling from a population of 1,120 students using the Slovin formula. Data analysis was conducted using SmartPLS to examine the relationships between variables. The findings indicate that Differentiated Learning significantly affects Economic Learning Outcomes (β = 0.183; p = 0.018). Learning Interest has the strongest influence (β = 0.382; p = 0.001), followed by Technology Utilization (β = 0.171; p = 0.012). Family Support plays a crucial role in improving students’ learning outcomes (β = 0.293; p = 0.000) and mediates the influence of other variables on economic learning outcomes. The mediation effect of Family Support strengthens the impact of Differentiated Learning (β = 0.098; p = 0.042), Learning Interest (β = 0.177; p = 0.004), and Technology Utilization (β = 0.152; p = 0.008) on students’ economic learning outcomes. The results of this study emphasize that diverse learning approaches, strong family support, high learning interest, and optimal technology utilization significantly enhance students’ economic learning outcomes. These findings have implications for educators and parents in developing more effective learning strategies both in school and at home.

Demographic Characteristics and Family Challenges Among Widows and Widowers in South-South Geo Political Zone, Nigeria

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This research study is about demographic characteristics and family challenges among widows and widowers in South-South geopolitical zone of Nigeria. To carry on the study two research questions and two hypothesis was formulated for the study. The study employed survey research design. The study area were the six states of the South-South geopolitical zone of Nigeria. The population of the study was 630 widows and widowers in Federal Universities in South-South Geopolitical Zone of Nigeria based on information from the registrar, ASUU and widows and widowers. The sample of the study is 441 widows and widowers in the research area. The instrument used for data collection is titled” Widows and Widowers Demography and Family Challenges Questionnaire (WWDFCQ). Cronbach Alpha Coefficient was used to determine the instruments’ reliability. Bar chart, mean and standard deviation were used to answer the research questions. Independent t-test, were used to test the null hypotheses at 0.05 level of significance. The findings of the study unveiled that there is no significance difference in the characteristics of the widows and widowers in South-South geopolitical zone of Nigeria and there is a significant difference in the mean responses of widows and widowers on family challenges faced by them in South-South geopolitical zone of Nigeria. Among others it was recommended that Seminars and workshops should always be organized on behalf of widows and widowers in their different locations on issues that could help them get discernment on the alternative ways of coping with the family and cultural challenges.