Scheduling is a common problem that has been raised for a long time. Many algorithms have been created for this problem. Some algorithms offer flexibility in terms of constraints and complex operations. Because of that complexity, many algorithms will need huge computation resources and execution time. A platform like a web application has many restrictions such as execution time and computation resources. A complex algorithm is not suited for the web application platform. Priority scheduling is a scheduling algorithm based on a priority queue. Every schedule slot will produce a queue based on the constraints. Each constraint will have a different weight. Weight in queue represents their priority. This algorithm provides a light algorithm that only needs a few computations and execution times. The exam schedule is one of many problems in educational institutions. A web application is a popular platform that can be accessed from everywhere. Many educational institutions use web platforms as their main system platform. Web platforms have some restrictions such as execution time. Due to web platform restrictions, priority scheduling is a suitable algorithm for this platform. In this study, the author tries to implement a priority scheduling algorithm in scheduling cases with a website platform and shows that this algorithm solution can be an alternative for solving scheduling cases with low computational resources.

This study explores a digital transformation initiative implemented for UMKM Bakat Jaya, a micro-enterprise based in Magelang specializing in animal feed production. Initially hindered by limited market reach, low digital literacy, and a lack of integrated technological tools, the enterprise relied primarily on traditional social media platforms for promotion. To address these challenges, a comprehensive and user-friendly website was developed using WordPress, featuring a professional company profile, product gallery, contact information, and seamless integration with existing social media accounts. The project was executed in four key phases: needs identification, website design and development, training and mentoring, and implementation followed by evaluation. Remote interviews and surveys were conducted to assess the partner’s needs, while capacity-building activities empowered stakeholders to manage the digital platform independently. Preliminary outcomes indicate a substantial improvement in market visibility, customer engagement, and operational efficiency. The results highlight the potential of structured digital interventions to transform the promotional strategies of rural micro and small enterprises, offering a scalable and sustainable model for enhancing competitiveness in similar contexts.

Models that achieve the best desktop accuracy in Remaining Useful Life (RUL) prediction do not necessarily meet the deployment constraints on IoT microcontrollers. The dominant evaluation paradigm rewards offline accuracy, yet condition-based maintenance ultimately requires inference within strict memory and latency budgets. This paper addresses this gap by evaluating three composable compression mechanisms, namely matched-capacity architecture scaling, Knowledge Distillation (KD), and INT8 Quantization-Aware Training (QAT), on the C-MAPSS FD001 benchmark with ESP32-S3 hardware validation. The analysis shows that the temperature-scaled KD is mathematically inert for scalar regression: KD improves cross-seed consistency (3.5× variance reduction) more than mean accuracy. Matched-capacity scaling compresses a 147K-parameter TTSNet teacher into a 33K StudentTTSNet (77.7% reduction), and INT8 QAT adds a 3.98× weight-level reduction, yielding a 70.9 KB deployed model at only 1.6% RMSE cost. On the ESP32-S3 microcontroller, the best offline GRU baseline fails to produce correct predictions in the tested TFLite Micro configuration due to the 1024-tensor allocation limit, whereas the compressed student model runs in real time at 56.8 ms (FP32) and 83.9 ms (INT8). Preliminary cross-domain experiments on a small plant-extraction dataset suggest that the same pipeline can regularize smaller data settings, indicating that runtime compatibility deserves validation as early as offline accuracy in IoT-bound RUL research.

Identifying asphyxia using computer vision in real-world settings poses challenges due to varying video quality, diverse lighting conditions, and subtle color changes in the newborn’s skin. This study presents an end-to-end framework for automated neonatal asphyxia detection using time series video analysis and makes three key contributions. First, the proposed framework integrates YOLOv8-based instance segmentation with advanced feature extraction across multiple color spaces and texture analysis to detect neonatal asphyxia through the multi-modal analysis of skin features in video streams. Second, we introduce a new quality-aware temporal analysis framework that includes adaptive quality assessment for evaluating frames in real time, multi-stage feature stability tracking across temporal windows, hysteresis-based decision logic for ensuring temporal consistency, and LightGBM classification with comprehensive feature engineering to assess severity. Third, we provide a curated time series video dataset of 12,973 frames from 45 neonates, of which some were healthy, and some had asphyxia of varying severity. The findings show that the YOLOv8-based instance segmentation achieved a mean average precision (mAP@0.5) of 0.925 for accurate skin region isolation, and the LightGBM classifier outperformed traditional models with an accuracy of 0.998 and an F1-score of 0.998. The system maintains real-time processing at 30 FPS for normal and mild asphyxia cases with a minor reduction to 20 FPS in more challenging scenarios and exhibits robust temporal stability across severity levels, with consistency scores above 0.90. This framework has the potential to enhance neonatal care through continuous monitoring and timely intervention.