Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings. METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

INTRODUCTION: In integrating Spiral Coverage into Cellular Decomposition, which combines structured grid-based techniques with flexible, quick spiral traversal, time efficiency is increased. OBJECTIVES: In the field of robotics and computational geometry, the study proposes a comparative exploration of two prominent path planning methodologies—Boustrophedon Cellular Decomposition and the innovative Spiral Coverage. Boustrophedon coverage has limitations in time efficiency due to its back-and-forth motion pattern, which can lead to lengthier coverage periods, especially in congested areas. Nevertheless, it is useful in some situations. It is critical to address these time-related issues to make Boustrophedon algorithms more useful in practical settings. METHODS: The research centres on achieving comprehensive cell coverage, addressing the complexities arising from confined spaces and intricate geometries. While conventional methods emphasise route optimization between points, the coverage path planning approach seeks optimal paths that maximize coverage and minimize associated costs. This study delves into the theory, practical implementation, and application of Spiral Coverage integrated with established cellular decomposition techniques. RESULTS: Through comparative analysis, it illustrates the advantages of spiral coverage over boustrophedon coverage in diverse robotics and computational applications. The research highlights Spiral Coverage's superiority in terms of path optimization, computational efficiency, and adaptability, proposing a novel perspective into cell decomposition. The methodology integrates the Spiral Coverage concept, transcending traditional techniques reliant on grids or Voronoi diagrams. Rigorous evaluation validates its potential to enhance path planning, exemplifying a substantial advancement in robotics and computational geometry. CONCLUSION: Our findings show that spiral coverage is on an average 45% more efficient than conventional Boustrophedon coverage. This paper set the basis for the future work on how different algorithms can traverse different shapes more efficiently.

INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence. METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration. RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making. CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.

The rapid growth of the Internet of Things innovations stimulates higher educational institutions to invest in and adopt these technologies to support and enhance their learning and teaching strategies. This study aims to investigate the influence of the Internet of Things on teaching and learning in higher education, performing a systematic literature review. Therefore, this research focuses on the following research questions: R-Q1: What are the benefits of the Internet of Things for teaching and learning in higher education? R-Q2: What are the limitations of the Internet of Things for teaching and learning in higher education? The systematic literature review, including the search strategy, the inclusion and exclusion criteria, and the review of the titles, keywords, and abstracts, identified a total of 31 results, mainly journal and conference articles. The findings from the extracted articles in this review were grouped into eleven themes: adoption, personalized learning, learning efficiency, intelligent teaching, collaboration and connectivity, creativity, health and safety monitoring, latency time, security and privacy, quality and ethics, and financing issues. The findings suggest that the Internet of Things can enhance the learning quality, improve the gained knowledge, and reduce costs in higher education. Therefore, adopting a consistent Internet of Things implementation strategy is essential to address identified limitations in higher education.

The current healthcare system often fails to account for individual health needs, leading to ineffective preventive measures and dietary guidance. Ayurvedic principles, which focus on the Dosha, offer a profound understanding of an individual's constitution, influencing their health, vulnerability to specific diseases, and ideal dietary choices. This paper explores the evolving intersection of ancient Ayurvedic wisdom and modern technology in the realm of disease diagnosis. Ayurveda, with its emphasis on personalized well-being, has long been a source of holistic health practices. In this context, the study delves into the intricate system of Ayurvedic Dosha analysis and its potential applications in contemporary healthcare. The research introduces an innovative way that seamlessly integrates traditional Ayurvedic pulse examination with state-of-the-art technology. By employing pulse sensors and advanced algorithms, the system not only identifies specific ailments but also classifies patients into Ayurvedic Prakriti types. Going beyond conventional diagnosis, this holistic approach extends to personalized recommendations, encompassing diet, lifestyle, Ayurvedic treatments, exercise, and daily routines. While addressing the challenges of harmonizing ancient principles with modern technology, the paper also presents the performance metrics of the model. The accuracy rates are as follows: Logistic Regression (LR) - 85.94%, Random Forest - 89.21%, Decision Tree - 99.70%, and k-Nearest Neighbors (KNN) - 86.43%. These metrics underscore the robustness of the system. In addition to outlining core concepts, methodologies, and model accuracies, the study explores current trends and recent developments in the field, offering readers a comprehensive understanding of Ayurvedic Dosha-based disease diagnosis. The research contributes to the broader discourse on healthcare by paving the way for early detection and individualized, holistic well-being for patients.

INTRODUCTION: The Internet of Things (IoT) has transformed daily life by interconnecting digital devices via integrated sensors, software, and connectivity. Although IoT devices excel at real-time data collection and decision-making, their performance on complex tasks is hindered by limited power, resources, and time. To address this, IoT is often combined with cloud computing (CC) to meet time-sensitive demands. However, the distance between IoT devices and cloud servers can result in latency issues. OBJECTIVES: To mitigate latency challenges, Mobile Edge Computing (MEC) is integrated with IoT. MEC offers cloud-like services through servers located near network edges and IoT devices, enhancing device responsiveness by reducing transmission and processing latency. This study aims to develop a solution to optimize task offloading in IoT-MEC environments, addressing challenges like latency, uneven workloads, and network congestion. METHODS: This research introduces the Game Theory-Based Task Latency (GTBTL-IoT) algorithm, a two-way task offloading approach employing Game Matching Theory and Data Partitioning Theory. Initially, the algorithm matches IoT devices with the nearest MEC server using game-matching theory. Subsequently, it splits the entire task into two halves and allocates them to both local and MEC servers for parallel computation, optimizing resource usage and workload balance. RESULTS: GTBTL-IoT outperforms existing algorithms, such as the Delay-Aware Online Workload Allocation (DAOWA) Algorithm, Fuzzy Algorithm (FA), and Dynamic Task Scheduling (DTS), by an average of 143.75 ms with a 5.5 s system deadline. Additionally, it significantly reduces task transmission, computation latency, and overall job offloading time by 59%. Evaluated in an ENIGMA-based simulation environment, GTBTL-IoT demonstrates its ability to compute requests in real-time with optimal resource usage, ensuring efficient and balanced task execution in the IoT-MEC paradigm. CONCLUSION: The Game Theory-Based Task Latency (GTBTL-IoT) algorithm presents a novel approach to optimize task offloading in IoT-MEC environments. By leveraging Game Matching Theory and Data Partitioning Theory, GTBTL-IoT effectively reduces latency, balances workloads, and optimizes resource usage. The algorithm's superior performance compared to existing methods underscores its potential to enhance the responsiveness and efficiency of IoT devices in real-world applications, ensuring seamless task execution in IoT-MEC systems.

The consistent implementation of the independent learning curriculum in Indonesia is a major challenge for education today. The Merdeka Belajar Curriculum needs to be flexible and adaptable to local needs, culture, and environment, considering the country's cultural diversity. By doing so, it can provide each school with the freedom to adjust learning to suit its context. To achieve this, the Batak Toba local wisdom can be utilized to provide a rich cultural context in the independent learning curriculum, incorporating cultural values, traditions, and local history in learning. This approach can help strengthen students' identity, instill a sense of pride in their cultural heritage, and make learning more relevant to their daily lives. Oral literature is one of the appropriate means to strengthen and preserve culture. Therefore, the use of oral literature as part of the Toba Batak local wisdom in the Merdeka Belajar Curriculum can provide a space for students to understand, appreciate, and absorb their cultural value. Indeed, by utilizing Batak Toba's local wisdom through oral literature, the Kurikulum Merdeka Belajar becomes more diverse, and inclusive, and provides opportunities for a more holistic learning approach that is culturally relevant.

The prophetic feminism approach combines elements of feminism and professionalism or more religious things. This research seeks to reveal prophetic feminism contained in Madurese rhymes. The method used in this research is qualitative, namely by examining the rhyming texts that contain prophetic feminist elements. The finding of this research is that there are three prophetic attitudes of Madurese women contained in Madurese rhymes, namely (1) the attitude of firmness towards their religion (2) firmness of principles (3) belief in (the value of) humanity. The three attitudes are the intenalisation of prophetic ethics, namely humanisation, liberation, and transcendence

This research examines education management to improve the quality of national education in the Era of Regional Autonomy. This is qualitative research with a case studies approach in the provinces of Riau Islands, Bangka Belitung Islands, Bali, Gorontalo, and Special Region of Yogyakarta. By using Terry & Rue's model, this research finds that increasing access to education has not been followed by an increase in the quality of learning outcomes. Meanwhile, each management element (planning, organizing, actuating, and controlling) revealed certain obstacles in supporting education programs in regions, which impedes enhancing the quality of Indonesian education in the era of regional autonomy. One of them is the budget planning. This research recommends improvements to each management element, such as establishing a reward and punishment system to fulfill minimum service standards (SPM) in the education sector through a mechanism for the General Allocation Fund (DAU) budget allocation.