Substantive justice in corruption cases can only be achieved if judges dare to redefine themselves as reflective and transformative actors. This study aims to analyse and formulate a reimagining of the judge’s role in achieving substantive justice in corruption cases in Indonesia. This study employs a juridical-normative approach, enriched by a socio-legal perspective, within an exploratory-analytical qualitative research design. The findings unequivocally challenge the judicial orthodoxy that positions judges merely as enforcers of norms, by asserting that in corruption cases, a rigidly neutral stance risks reproducing structural injustice. Thus, the reconstruction of the judge’s role as an architect of substantive justice is no longer an option, but a normative and ethical imperative. This study offers a novel approach through a critical synthesis of judicial independence, legal politics, and a sentencing paradigm that positions judges as reflective actors who dare to interpret the law progressively while remaining accountable, thereby simultaneously integrating deterrence, the recovery of state losses, and social justice. The implications call for fundamental reform of the penal system, the strengthening of protections for judicial independence against the pressures of populism and power, and a transformation of legal education that is no longer merely legalistic.
The depletion of fossil fuels necessitates efficient catalytic routes for converting waste lipids into renewable diesel-range hydrocarbons. This work highlights the novelty of tailoring metal–support synergy in γ-Al₂O₃-based catalysts to direct deoxygenation pathways without external hydrogen, offering a cost-effective strategy for sustainable green diesel production from waste oils. The deoxygenation reaction was conducted under an inert atmosphere at elevated temperature, and the resulting liquid products were characterized using gas chromatography–mass spectrometry analysis. The results demonstrate that metal modification markedly enhances deoxygenation performance compared to pristine γ-Al₂O₃. Ni/γ-Al₂O₃, possessing higher acidity, promotes intensified deoxygenation accompanied by excessive cracking, leading to an increased formation of lighter hydrocarbons, particularly C₉ (9.21%). In contrast, Co/γ-Al₂O₃ exhibits a more balanced catalytic behavior, favoring decarboxylation and decarbonylation pathways while preserving the carbon backbone of fatty acid derivatives. Consequently, Co/γ-Al₂O₃ produces a liquid product predominantly composed of C₁₅–C₁₈ paraffinic hydrocarbons (40.32%), characteristic of green diesel, with a significantly reduced fraction of oxygenated compounds. Compared to previously reported hydrogen-free alumina-based catalysts, the Co-modified system demonstrates improved carbon-chain retention and enhanced diesel selectivity without noble metals or external H₂. This study provides new insight into the structure–pathway–selectivity relationship in bifunctional catalysts and identifies Co/γ-Al₂O₃ as a promising non-noble catalyst for sustainable green diesel production.
The rapid evolution of engineering education has intensified the demand for instructional approaches that can effectively support experiential learning, complex problem solving, and student engagement beyond traditional lecture-based methods. In this context, serious games have emerged as a promising pedagogical paradigm, particularly when combined with emerging technologies such as artificial intelligence (AI) and immersive environments. This study presents a systematic literature review (SLR) of serious games in engineering education, synthesizing peer-reviewed research published between 2020 and 2025 and indexed in Scopus and IEEE Xplore. Following the PRISMA 2020 framework, 63 primary studies were rigorously selected and analyzed to identify dominant technological innovations, application domains, game genres, research methodologies, and implementation challenges. The findings indicate that web- and mobile-based serious games remain the most prevalent due to their accessibility and scalability, while immersive technologies especially Virtual Reality (VR) demonstrate strong potential for enhancing spatial understanding and experiential learning in complex engineering domains. However, the integration of AI-driven adaptivity remains limited, with only a small subset of studies employing machine learning or computer vision techniques for personalized learning support. Key challenges include high development and infrastructure costs, limited pedagogical alignment, inconsistent evaluation frameworks, and insufficient attention to long-term learning outcomes and soft skill development. This review contributes a comprehensive cross-disciplinary perspective on technological trends and research gaps, highlighting the need for pedagogically grounded, AI-enhanced, and scalable serious game frameworks to support sustainable innovation in engineering education.
This study explores the revitalization of the Kebun Botol Community Reading Park (TBM) in Tlogomas, Malang, aiming to enhance community participation, literacy, foreign language proficiency, and environmental awareness. The objective was to transform TBM into a multifunctional space integrating literacy programs, foreign language education, and environmental conservation. The research applied a Community-Based Participatory Research approach, involving active community engagement throughout the revitalization process. Data was collected through semi-structured interviews, focus group discussions, surveys, and observational field notes. Results showed a significant increase in community participation, particularly in foreign language classes and environmental workshops. Participants reported improved literacy, with 90% indicating enhanced proficiency in English and Arabic. The integration of environmental conservation activities, such as sustainable farming and recycling workshops, fostered greater ecological awareness. The revitalization process also led to increased satisfaction, with 90% of participants expressing high satisfaction with the new programs and facilities. This study demonstrates the success of integrating foreign language development and environmental education in community spaces. It highlights the importance of participatory approaches in ensuring the sustainability of such initiatives. The findings suggest that TBM Kebun Botol serves as a model for community-driven educational projects, offering valuable lessons for other similar initiatives. Further research should examine the long-term impact and scalability of these revitalization efforts in different contexts.
The rapid development of IoT research in various fields has promoted the evolution of manufacturing in the Industry 4.0 context. However, the growing and dispersed literature makes it difficult to see the dominant trends and open challenges. The aim of the study is to synthesize the existing IoT research in the manufacturing, by analyzing the sectoral adoption, enabling technologies and implementation objectives. The review develops a systematic understanding of the links between manufacturing sectors, IoT technologies and operational priorities to identify dominant research directions and gaps for future research. A systematic literature review was conducted according to the PRISMA guidelines, screening and analysing peer-reviewed studies along three analytical dimensions: distribution by manufacturing sector, typologies of IoT technologies and strategic objectives of implementation. The analysis identified shared adoption patterns in some manufacturing sectors, common use of sensor-based and cloud-enabled technologies, and a high emphasis on productivity, monitoring and efficiency of operations. The results reveal a significant concentration of IoT research in discrete manufacturing, as well as noticeable attention in process manufacturing, healthcare and general manufacturing, while other sectors remain less explored, indicating an uneven research focus across industries. In terms of technology, Industrial IoT and smart manufacturing solutions are the most common, followed by IoT-enabled digital twin technologies, while the combination of IoT with artificial intelligence, machine learning, and computer vision indicates a growing shift towards more adaptive and intelligent systems. A smaller portion of IoT implementations are related to sensors and monitoring applications, blockchain enabled IoT solutions and distributed architectures, while middleware and system integration appear least often. Regarding implementation objectives, efficiency enhancement is the main driver, followed by predictive maintenance, quality control and productivity enhancement, and real-time monitoring, showing a strong orientation toward improving operational performance. In summary, the synthesis implies that the IoT research in manufacturing is mainly focused on discrete manufacturing applications, operational efficiency objectives, and intelligent automation technologies. The concentration indicates a continued research focus on production optimization, while broader contexts of industrial integration are relatively underexplored.
