Accurate forecasting of carbon prices is crucial for policymakers and market participants, yet remains challenging due to volatility, non-stationarity, and structural breaks in carbon markets. Data preprocessing plays a key role in improving predictive accuracy, but conventional normalization methods such as z-score and min–max rely on static statistics and fail to adapt to evolving dis- tributions. This study introduces Extended Deep Adaptive Input Normalization (EDAIN) into carbon price forecasting. EDAIN dynamically integrates outlier suppression, shifting, scaling, and power transformation, optimized jointly with forecasting models. We evaluate EDAIN with GRU, LSTM, and XGBoost on datasets from KRBN and KCCA obtained via the yfinance API. Experimental results show that EDAIN consistently outperforms static normalization, reducing the RMSE from 2.45 to 0.80, the MAE from 2.09 to 0.63, and the MAPE from 7.07
Social Impact Organisations (SIOs) face persistent challenges in governance and transparency, which impact public trust and operational efficiency. The emergence of Decentralized Autonomous Organizations (DAOs) offers a blockchain-based alternative to traditional governance structures, leveraging transparency, decentralization, smart contracts, and cryptographic security. This paper presents a comparative review of DAO adoption in SIOs, analyzing transparency mechanisms, governance models, adoption barriers, and their impact on the sector. Using a Narrative Literature Review (NLR) approach, the study synthesizes insights from nine selected papers out of 122, focusing on DAO-driven donation tracking, governance models, and regulatory constraints. The findings suggest that DAOs enhance transparency, accountability, and cybersecurity resilience through immutable records and tamper-proof decision-making processes. However, technical complexities, regulatory uncertainty, and financial sustainability remain significant challenges to adoption. Hybrid governance models, which balance decentralization with regulatory compliance, appear promising for overcoming these barriers. The review identifies five key research gaps: (i) empirical validation of DAO models, (ii) decentralized identity and inclusive governance, (iii) integration with AI and IoT, (iv) sustainable financial models, and (v) standardized legal frameworks. Addressing these gaps is crucial for advancing DAO adoption in SIOs, ensuring a scalable and legally compliant governance framework. This study contributes by providing a structured comparative analysis of DAO implementation in social impact initiatives, with emphasis on transparency, accountability, and cybersecurity implications.
In the demanding field of practical and feasible quantum cryptography (QC), a multi-party environment is vital for scalable secure communications. This paper presents a four-party quantum key distribution (QKD) simulation framework based on Optical Frequency Division Multiplexing (OFDM) using OptiSystem™. Building upon our prior two-party QKD model, this enhanced framework utilizes wavelength multiplexers and OptiSystem’s subsystem features to streamline transmitter and receiver designs. The implementation includes a centralized server coordinating signal multiplexing and demultiplexing. We reference real-world fiber-optic QKD studies to evaluate expected key generation rates and quantum bit error rates (QBER). The framework supports future research on multi-party authentication and conference key management, offering a modular and replicable simulation for quantum communication development.
Population aging reduces older adults’ social engagement, leading to loneliness and lower quality of life. Phatic communication—interaction aimed at maintaining social bonds rather than transmitting information—offers a non-pharmacological approach to enhance engagement. This study reviews its concepts, applications, and relevance for older adults, highlighting barriers such as sensory decline, technology complexity, and limited emotional richness. We propose integrating touch perception into everyday communication devices to simulate physical presence, strengthen emotional connection, and compensate for sensory loss. Using literature review, case analysis, and design exploration, findings suggest that tactile-enhanced phatic communication improves intimacy, stimulates engagement, and supports emotional well-being. A conceptual framework linking phatic communication, tactile technology, and social engagement is presented, offering guidance for inclusive, emotionally supportive technology design.
Loop-closure detection is a critical component in LiDAR-based SLAM, as it corrects accumulated drift and improves long-term localization accuracy. While considerable research has focused on designing effective descriptors, the role of clustering and indexing methods in retrieving loop-closure candidates has received little attention. Existing systems primarily rely on KD-Tree, with limited evaluation of alternatives like DBSCAN and Spectral Clustering. This paper addresses this gap through a controlled, head-to-head comparison of KD-Tree, DBSCAN, and Spectral Clustering for organizing and matching LiDAR-Iris descriptors. All methods are evaluated under identical conditions using the same dataset, hardware, and binary descriptor format. Experimental results on a 333-frame dataset show that DBSCAN achieves the fastest total processing time (6.61 s), while Spectral Clustering offers the lowest query time (0.0386 s). In contrast, KD-Tree incurs higher memory usage (+631
