Authors - Yad Sabah Hussein, Raid W. Daoud, hab Abdulrahman Satam, Mohammad Fakhreldin Abstract - This systematic review investigates the design strategies and performance outcomes of AI-powered robotic systems in warehouse automation. The study synthesizes recent advances in robotic navigation, object recognition, task scheduling, and multi-robot coordination, with particular emphasis on machine learning and reinforcement learning techniques that allow robots to adapt to dynamic environments and optimize decision-making in real time. Performance evaluation across the literature reveals significant improvements in throughput, accuracy, and flexibility when AI-driven robotics are deployed. Robots equipped with advanced sensors and computer vision systems demonstrate enhanced capabilities in obstacle avoidance, inventory management, and autonomous material handling. Integration with cloud computing and Internet of Things (IoT) infra-structures further strengthens interoperability and enables predictive analytics for supply chain optimization. Key performance metrics such as energy efficiency, error reduction, and scalability are analyzed to highlight the comparative ad-vantages of AI-enhanced solutions over conventional automation. Despite these advances, challenges remain in ensuring safety, interoperability among heteroge-neous systems, and cost-effective scalability. High implementation costs, cyber-security risks, and the absence of standardized protocols are identified as barriers to widespread adoption. The review concludes that hybrid approaches—combining learning-based adaptability with formal safety guarantees—represent a promising direction for future research. Moreover, sustainable design practices and energy-efficient robotics are essential to align warehouse automation with broader environmental goals. This review provides a consolidated perspective on the state of AI and robotics in warehouse automation, offering insights for re-searchers, practitioners, and industry leaders seeking to design resilient, intelligent, and sustainable warehouse systems.
Authors - Johanes Fernandes Andry, Hendy Tannady, Glisina Dwinoor Rembulan, Ongky Alex Sander, Guan Nan Abstract - Fast paced change in digital transformation, government institutions have been forced not only to embrace IT but to also govern it strategically for alignment with organizational goals. Unfortunately, most organizations still grapple with several major problems including lack of alignment between business and IT and unreliable configuration data management. In light of this, this research proposes an effective IT governance strategy that will address the out-lined challenges through utilization of the COBIT 2019 approach. Based on the results, the organization is moderately mature in terms of compliance and risk awareness but still needs improvement in the alignment between its IT and business objectives and the validation of configuration data. Moreover, organizational resistance and lack of user involvement were found to be among the most important obstacles to successful implementation. According to the findings, the application of COBIT 2019 framework in the process of governance is likely to lead to better governance capacity, higher quality of information, and improved decision-making processes.
Authors - Xinyi ZHU, Han Wang, Jun WU Abstract - In rainy-day images, rain patterns exhibit complex morphologies and significant variations in scale, and they tend to overlap with background textures and edge structures, posing significant challenges for rain removal tasks based on a single image. Addressing the shortcomings of existing methods in modeling complex rain patterns, enhancing key regions, and utilizing complementary information across channels, this paper proposes a multi-scale attention and multi-channel fusion image rain removal method tailored for complex rain pattern scenarios. First, we construct a parallel multi-scale feature extraction module that uses standard convolutions and dilated convolutions with varying dilation rates to capture fine-scale local rain patterns, mesoscale rain patterns, and large-scale contextual information, thereby enhancing the network's ability to perceive rain patterns across multiple scales. Second, an adaptive attention optimization module is designed to re-calibrate multi-scale features across both channel and spatial dimensions, enabling the model to focus more on areas with dense rain patterns, edge structures, and effective texture information. Finally, a multi-channel feature interaction and fusion mechanism is introduced. Through channel segmentation, cross-channel interaction, and residual fusion, this mechanism enhances information complementarity between different feature subspaces, thereby improving the structural preservation and visual naturalness of the restored results. Experimental results on the Rain100H, Rain100L, Rain12, and SPA-Data datasets demonstrate that our method achieves superior rain removal performance across multiple test scenarios, exhibiting particularly strong generalization capabilities in real-world rainy conditions.
Authors - Jatmiko Yogopriyatno, Nursanty, Yorry Hardayani Abstract - Ambiguity in public decision-making constitutes a structural impediment to effective e-governance, manifesting as procedural uncertainty, inconsistent treatment of analogous cases, and unclear authority allocation. This study examines the Integrated Aspiration and Minutes Management Information System (SMART MURA) developed by the Regional Legislative Council (DPRD) of Musi Rawas Regency, Indonesia, as a sociotechnical solution for resolving decision ambiguity through stakeholder consensus-based participatory design. Employing design science research (DSR) methodology, the study analyses the SMART MURA User Guide as a social contract encoding collective stakeholder agreements generated through multi-stakeholder Focus Group Discussions involving five organisational levels. Three ambiguity-resolution mechanisms are identified: (1) bounded automation for standardising routine administrative processes, (2) explicit judgment points for clarifying the locus of professional discretion, and (3) flexible categorisation that accommodates case complexity without sacrificing treatment consistency. These mechanisms produce four interdependent governance benefits: procedural certainty for citizens, cross-case treatment consistency, traceable accountability through digital audit trails, and operational efficiency. The system further demonstrates structural adaptability to regulatory change and incorporates a tiered data governance architecture that balances operational transparency with individual privacy protection. The study advances e-governance theory by proposing a Transparent Discretionary Space Structuring (TDSS) framework that transcends the binary opposition between rigid Standardization and unstructured discretion. Keywords: E-Governance, Public Decision Ambiguity, Design Science Research, Digital Discretion, Participatory Design, Legislative Information System, Data Governance.
Authors - A. Aruna Kumari, Tamminana Visweswari, Sri Vishnu Prabhu Gudavalli Abstract - Globally, food waste accounts for almost one-third of total food production, which is approximately 1.3 billion tons annually. Some of the most wasted foodstuffs at the consumer level include fruits, vegetables and bread. This proposed work will help to solve this financial and environmental issue by creating an intelligent system to eliminate vegetable waste with the help of MobileNetV2 which is a Convolutional Neural Networks (CNN) based model to classify the freshness of vegetables and use object detection model called as YOLOv8n to recognize types of vegetables. It begins with the user uploading images of vegetables, which are pre-processed with the help of normalization and data augmentation. The MobileNetV2 model categorizes fresh and spoiled produce with accuracy rates of 95 percent, which is expected of a Freshness classifier. In the meantime, the YOLOv8n finder detects the specific type of vegetable with the help of a mAP50 of 0.934 to recommend the specific vegetable. If the vegetable is spoilt, it will provide instructions on how to compost and if it is fresh, it provides zero waste recipes. The entire system shall be made easily accessible in a web application that has Flask back-ends.
