Artificial intelligence is applied in smart grids to improve efficiency, reliability, and the integration of conventional and renewable energy sources. A state of the art review of artificial intelligence methods in smart grids is presented. A methodology is used for resource identification and systematic review. A taxonomy is proposed to classify machine learning models by method and application domain. Models are compared based on accuracy and computational efficiency. Key applications such as demand response, energy forecasting, fault detection, and grid optimization are analyzed. Artificial neural networks, decision trees, long short term memory networks, support vector machines, convolutional neural networks, and random forest models are identified as the most used approaches. The best performance is reported for convolutional neural network based and random forest based models. Load forecasting and energy management are identified as the most common application areas.
This study presents a real-time hand-sign recognition system to support communication for persons with hearing and speech impairments, with a focus on one-handed finger-spelling (the “dactyl alphabet”). The method performs on-device, frame-based detection and classification using RetinaNet with a ResNet-50 backbone, chosen for its favourable speed–accuracy trade-off and stable training on modest, class-imbalanced datasets typical of sign recognition. To ensure robustness beyond laboratory conditions, we curated a purpose-built dataset of more than 5600 annotated images captured across varied indoor and outdoor scenes, lighting conditions, backgrounds, camera distances, skin tones, and hand shapes. In a user study with 30 participants, the system achieved an average accuracy exceeding 93
This study examined the motivating factors behind Bangladesh’s July 2024 uprising, which initially began as a protest against the civil service quota system and subsequently evolved into a nationwide revolutionary movement. Employing Reflexive Thematic Analysis, the research draws on protest narratives, media reports, and activist statements to identify the key dynamics that shaped the uprising’s trajectory. Findings revealed six interrelated themes: structural grievances, rooted in corruption, inequality, and authoritarian governance; the emergence of youth identity as a generational force for change; the role of emotions, including anger, hope, and fear, in sustaining mobilization; the strategic framing processes that expanded sectoral demands into systemic challenges; aspirations for democracy and justice; and online-offline mobilization cycle which gave the movement future-oriented legitimacy. The study contributed to scholarship on contentious politics by demonstrating how grievances, emotions, identity, and framing interact to transform protests into revolutionary moments. It also highlighted the methodological value of Reflexive Thematic Analysis in capturing the meaning-making processes of social movements. While limited by reliance on textual data and focus on immediate events, the research underscores broader implications for theory, policy, and activism. It shows that ignoring youth grievances risks fueling systemic unrest, whereas youth-driven mobilizations can catalyze democratic renewal in semi-authoritarian contexts.
This study presents a Design for Additive Manufacturing (DfAM)-driven redesign of an industrial robot vacuum gripper for Fused Deposition Modeling (FDM), focusing on the systematic transformation of a multi-part, machined aluminum assembly into a lightweight, support-minimized polymer component suitable for continuous industrial operation. Beyond a practical redesign, the work contributes a geometry-centered DfAM methodology that links internal channel topology, overhang control, and functional interfaces to manufacturability, vacuum performance, and cost efficiency. The development follows three iterative design revisions, progressing from a geometry-adapted baseline toward a fully DfAM-optimized solution. A key innovation is the introduction of support-free internal vacuum channels with triangular cross-sections, enabling complete elimination of soluble support material within enclosed cavities. This redesign reduces the internal vacuum volume by 44%, leading to faster vacuum response while maintaining functional suction performance. The optimized overhang angles, filleted load paths, and DfAM-compliant suction cup seats significantly reduce post-processing requirements and improve structural robustness. Experimental validation under industrial operating conditions confirms that the final design achieves reliable vacuum performance and mechanical durability. Compared to the original configuration, the optimized gripper demonstrates a substantial reduction in manufacturing complexity, with printing time reduced by approximately 50% and total part cost decreased by 26%, primarily due to eliminated tooling, reduced support material, and simplified post-processing. The presented results demonstrate that DfAM principles, when applied systematically at both global and internal geometry levels, can yield quantifiable functional and economic benefits. The findings provide transferable design guidelines for support-free internal channels and functional interfaces in FDM-manufactured vacuum components, offering practical reference points for researchers and practitioners developing end-use additive manufacturing solutions in industrial automation.
South Korea is undergoing one of the fastest demographic transitions in the world, with declining fertility and a rapidly expanding retired population. This trend threatens fiscal sustainability by raising age-related transfer expenditures and straining existing fiscal frameworks. To analyze these dynamics, the study develops a forward-looking macro-fiscal model that embeds demographic aging into a fiscal limit framework with endogenous sovereign risk premia. The model links transfer spending, debt issuance, taxation, and default probability, allowing sovereign borrowing costs to evolve as a function of fiscal stress. The analysis is calibrated to Korean economic data using median values from recent studies, providing a realistic baseline for projections. The results show that in the steady state, Korea sustains moderate debt (29
