Learning Data Structures and Algorithms (DSA) can often be noted to be an important challenge, primarily due to the abstract nature of program execution. To address this pedagogical challenge, the current paper presents RunSketch, an interactive educational application that will help with visualizing code in real-time, highlighting syntax, tracking variables, and providing AI-based narration. RunSketch uses code execution to provide feedback on the code to users, and provides explanatory information in the form of written text and spoken output using Google Text to Speech API. Its front-end architecture is based on React.js, D3.js and Monaco Editor, the back-end is written in Python using Flask and uses the sys.settrace() method to trace execution. RunSketch significantly enhances understanding and interaction of learners, due to the combination of visual and audio feedback systems, thus, becoming an important innovation in the sphere of teaching programming.

Sucrose ester (SE) is an anionic surfactant compound with a high affinity for water and potential applications as a component in personal care products. This study aimed to determine the SE purification process using the liquid-liquid extraction method with different types of salts, specifically sodium chloride (NaCl) and potassium chloride (KCl), alongside SE (Palmitate, Laurate, and Stearate). The production process of SE employs a sucrose to fatty acid ratio of 1:3, with the addition of a 6% w/v potassium carbonate (K2CO3) catalyst. The temperature is initially set at 60 °C for 30 minutes and is gradually increased to 100 °C for 90 minutes. The SE purification process is conducted using liquid-liquid extraction. Lactic acid is added until the pH reaches 5. The organic solvent isobutanol is combined with saltwater (10% salt in a 1:10 ratio). The temperature is maintained within the range of 55-70 °C. The organic phase is mixed with saltwater in a 1:1 ratio. The final product is then evaporated. HPLC analysis revealed only monoester compounds. FTIR analysis of sucrose ester indicated the presence of C-H and C=O functional groups within the wavelength range of 1730.15 - 1732.08 and 2850.79 - 2854.65. After purification, emulsion stability, foam stability, and yield parameters decreased. However, droplet size, surface tension, and interfacial tension parameters increased. The use of KCl salt proved to be the most effective treatment. Instead of being the primary emulsifier in the formulation of personal care products, the sucrose ester purification product is more suited as a solubilizer.

This paper presents the design and simulation of a compact multiple-input multiple-output (MIMO) antenna structure optimized for operation within the 2.4 GHz frequency band, which is widely utilized in wireless applications such as Wi-Fi, Bluetooth, and various Internet of Things (IoT) devices. The proposed MIMO antenna features a novel configuration, incorporating a modified rectangular patch on a cost-effective FR-4 substrate. To improve isolation between the two antenna elements within the operating band, orthogonal polarization is employed, effectively minimizing mutual coupling and enhancing overall MIMO performance. Simulation results demonstrate excellent impedance matching at the target frequency, with a return loss S₁₁ of –26 dB around 2.4 GHz, indicating minimal reflection and efficient power transfer. The antenna exhibits stable performance across the 2–3 GHz range, with a distinct resonance peak confirming its narrowband characteristics. The envelope correlation coefficient (ECC) is very close to zero, indicating low signal correlation between the elements and resulting in high diversity gain (DG) at the operating frequency. These results highlight the suitability of the proposed MIMO antenna for integration into modern embedded and portable wireless communication systems.

As climate change intensifies, urban flooding has become a growing threat to cities worldwide, especially in low-lying, densely populated areas. Accurate flood risk prediction is essential for disaster readiness, yet it remains a challenge due to the complex, dynamic nature of extreme weather patterns. This study applies Gaussian Process modelling, a powerful probabilistic method capable of capturing spatial and temporal correlations, to forecast flood inundation risks in Semarang, Indonesia. By integrating diverse data sources, such as weather station records, satellite data, and aerial imagery, the model generates detailed flood risk maps for areas prone to recurrent inundation. Semarang, where up to 40% of lowland regions are affected annually, serves as a critical testbed. The resulting maps provide local authorities with actionable insights to identify high-risk zones, allocate resources, and implement more targeted mitigation strategies. This approach demonstrates how advanced machine learning techniques can enhance urban resilience and inform proactive policymaking in the face of climate uncertainty.

The issue of global warming resulting from the PC manufacturing process and the pollution produced by coal power plants in generating fly ash waste has encouraged the search for alternative green materials. LUSI mud (Lumpur Sidoarjo) is an alternative; however, research on its use remains limited. Therefore, exploring the potential of LUSI mud as a fly ash replacement in geopolymer is important. The high ferrite fly ash-LUSI mud geopolymer performance was identified based on compressive strength and porosity at 7 and 28 days. A depth-penetration test investigated the effect of LUSI mud on setting time. The substitution levels for LUSI mud were 0%, 8%, 27%, and 50% of the total weight. The results showed that the addition of LUSI mud significantly affected geopolymer performance, as evidenced by a decrease in strength. The pore values also increased, which indicated a decrease in density. It also prolonged the geopolymer reaction’s setting time. However, these results showed that, with a specific composition (less than 8%), LUSI mud can be used in standard construction as a sustainable material to support technological innovation (SDG 9) and address climate change issues arising from the PC industry’s carbon footprint (SDG 13).

Foreign learners’ ability to interact effectively in Indonesian multilingual classrooms requires not only linguistic skills but also sensitivity to social expectations that support shared responsibility in communal spaces. This study investigates how politeness and avoidance strategies shape socially sustainable interaction in Bahasa Indonesia untuk Penutur Asing (BIPA) programs. Data were collected from two classroom observations, audio-recorded group activities, and short elicitation interviews with ten international students enrolled in BIPA courses at three Indonesian universities. Thematic analysis shows that learners frequently use mitigated requests, humor, and non-confrontational negotiation when adjusting seating, sharing materials, and maintaining cleanliness. These practices uphold interpersonal harmony while promoting responsible use of shared resources. The findings indicate that pragmatic competence functions as a behavioral foundation of sustainability literacy, contributing directly to the realization of SDG 4 (Quality Education), and supporting SDG 11 and SDG 12 through collective care and conflict avoidance. This study proposes a transferable pedagogical framework positioning pragmatic instruction as a pathway to developing inclusive and environmentally aware classroom communities. Implications for integrating sustainability-oriented communication into BIPA and other multilingual language learning contexts are discussed.

Poor acoustics may impede communication and user experience, and yet traditional design approaches often ignore the varying acoustic needs. The present paper systematically reviews pertinent literature to assess how flexible geometric design options (e.g., adjustable surfaces, modular panels, or dynamic surfaces) affect relevant acoustic parameters (reverberation time, clarity, and sound distribution) in comparison with conventional empty auditorium designs. By following PRISMA Protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) applied strictly to ensure transparency and reproducibility of the research process, 25 experimental and simulation studies from Scopus, Science Direct, Journal of The Acoustical Society of America, Sage, MDPI, and others were analyzed. Results indicate that proper geometric design can improve acoustics quality variation, particularly in multipurpose applications; however, implementation must consider cost and technical complexity. Therefore, conclusions and recommendations serve as an evidence-based guideline for architects and acoustical engineers involved in designing adaptive performance spaces in terms of real-time adjustment technologies and smart materials. On the other hand, new standards for acoustic evaluation of dynamic designs should also be created.

Floods are an acute environmental hazard and affect pliable geographic areas in multiple portions of India, low-lying urban centres like Mahad in the Raigad district of Maharashtra are even more susceptible to flooding. This study presents a multi-temporal application of a hybrid flood detection approach that combines information from the Sentinel-1 Satellite (Synthetic Aperture Radar (SAR)) and Sentinel-2 and Normalized Difference Water Index (NDWI) percentages to assess flood inundation status from 2020 to 2024. While the individual techniques are well established in flood mapping literature, the contribution of this work lies in their consistent integration and repeated application over multiple years using Google Earth Engine (GEE), a hybrid approach to flood detection, particularly during cloud cover of the monsoon season, is very feasible. The hybrid approach employs SAR backscatter ratio thresholding to identify potential flood pixels, followed by NDWI-based filtering to reduce misclassification in urban and vegetated areas, with results visually cross-referenced using false colour composite (FCC) imagery. The combined SAR–NDWI results enabled improved spatial consistency in identifying flood-prone zones. The hybrid model appeared to localise 'true' flood zones effectively. From the results, a significant flood event occurred in 2021 with an inundated area of 2,887.93 hectares based on both SAR and NDWI data, while in 2023 only a small interactive flooding area was noted after dredging. Overall, the study demonstrates a repeatable and scalable hybrid workflow that supports long-term flood assessment and regional flood.

Serengeti district in Tanzania, a climate-vulnerable region, faces increasing risks to farmers' livelihoods and food security due to climate change. This study examines farmers' adaptive behaviors using the Adaptive Capacity Framework (ACF), which analyzes five key dimensions: assets, flexibility, learning, social organization, and agency. Through participatory methods, the research assesses both strengths and capacity gaps in local adaptation. One analytical tool was employed, Importance-Performance Analysis (IPA). IPA revealed significant gaps between the perceived importance and actual performance of critical services such as agricultural extension, access to credit, and timely weather information. These results highlight the need for climate adaptation approaches that align with community priorities and realities. The study concludes that enhancing farmers' adaptive capacity requires inclusive, multi-dimensional strategies supported by improved institutional frameworks and access to key resources. The proposed framework serves as both a diagnostic tool and a practical guide for community-centered climate resilience and planning.

Mosquito-borne diseases, particularly dengue, remain a major public health challenge in tropical regions such as the Philippines. In 2024, dengue cases increased sharply nationwide, with the Davao Region reporting over 9,000 cases and rising mortality. Conventional mosquito control methods are often labor-intensive, costly, and difficult to sustain, especially in underserved communities. This study proposes an automated, real-time mosquito breeding site detection system integrating unmanned aerial vehicles (UAVs) with artificial intelligence-based image analysis and spatial heat mapping. A DJI Air 3 drone conducts aerial surveillance, while an OKdo Jetson Nano microcontroller runs dual YOLOv8 models: YOLOv8n for object detection and YOLOv8seg for water segmentation, optimized using NVIDIA TensorRT. The system detects potential breeding containers such as buckets, tires, and stagnant water, and generates heat maps to classify risk levels across barangays in Caraga, Davao Oriental. Field tests over 72 flights showed inference times of 56.1-114.8 ms and frame rates of 13.21-15 FPS. The model achieved 93.20% accuracy with high recall, precision, and F1 scores. Regression analysis indicated a strong correlation (R 2 = 0.937) between detected breeding sites and dengue cases, confirming predictive validity and supporting its use for targeted public health interventions.