Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
This paper presents an adaptive short-term intraday load forecasting strategy designed for the operational requirements of transmission and distribution system operators. Standard forecasting approaches often report strong performance on selected periods, yet real utility operations require accurate predictions for every day and every hour of the year. Deviations during the operating day, caused by unexpected changes in consumer behavior, introduce forecasting errors and financial risk. To address this problem, we propose a multi-tiered forecasting model that selects the base method according to the availability of historically similar days. When many similar days exist, the model uses a pretrained artificial neural network, while linear regression is applied under moderate similarity conditions, and an arithmetic mean is used when only a few similar days are available. A real-time delta correction layer is applied in all cases, using recent intraday measurements to forecast short-term error and adjust the baseline output. This approach enables rapid adaptation to atypical days and intraday anomalies. Testing on five years of utility data demonstrates that the method maintains consistently low MAPE across all days and all hours, providing the level of accuracy needed for intraday market operations and system balancing....
This paper advances user-centric Artificial Intelligence (AI) frameworks for reliability in fifth-generation and beyond (B5G) networks by examining their use in high-demand services such as video streaming. The proposed framework can leverage multi-layer monitoring across the edge–cloud continuum, application-layer metrics, and 5G core performance data to evaluate reliability through Quality of Experience (QoE) optimization. Results demonstrate that improved frame delivery can be achieved via dynamic resource prediction and proactive resource allocation. The study validates the framework’s scalability in dynamic workload conditions, emphasizing its role in mission-critical video services....
Background: Nurse teletriage has emerged as a component of modern healthcare delivery, utilizing telecommunication technologies to assess patient conditions remotely and guide appropriate care decisions. As healthcare systems face increasing demand and the need for cost-effective care delivery, teletriage services have expanded, particularly following the COVID-19 pandemic. Objective: This narrative review examines the current state of nurse teletriage practice, its effectiveness, safety outcomes, and implementation considerations. A comparative analysis with physician-led teletriage models is provided, and the emerging role of artificial intelligence is explored. Methods: A narrative review of the literature was conducted through searches of multiple databases including PubMed/MEDLINE, CINAHL, Cochrane Library, Embase, Web of Science, and Google Scholar. This approach was selected due to the heterogeneous nature of the teletriage literature, which spans diverse study designs, populations, and outcomes that are not amenable to formal systematic synthesis. Peer-reviewed articles published between 1970 and 2024 examining safety outcomes, effectiveness, and implementation frameworks were reviewed. Results: The available evidence suggests that nurse-led teletriage systems, particularly when supported by computerized decision support systems, can improve patient access to care while maintaining safety standards. Studies indicate that telephone triage nursing does not increase mortality, hospitalization rates, or emergency department referrals when properly implemented. One well-documented physician-led model in Israel reported diagnosis accuracy rates of 98.5% and decision reasonableness rates of 92%, though generalizability across settings requires caution. Key success factors appear to include the use of evidencebased protocols, staff training, technology infrastructure, and quality assurance programs. While these findings are promising, the heterogeneous nature of the included studies and absence of formal quality assessment warrant cautious interpretation. Conclusions: Nurse teletriage appears to be an effective and safe approach to healthcare delivery that addresses challenges in modern healthcare systems. The choice between nurse-led and physician-led models should consider population complexity, case types, available resources, and economic factors. Artificial intelligence technologies offer potential opportunities to enhance teletriage, though careful validation is essential. Future research should focus on long-term outcomes, comparative effectiveness across healthcare systems, and rigorous evaluation of AI applications. Highlights: Telephone triage services, where nurses or physicians assess patients remotely and guide them to appropriate care, have become increasingly important in modern healthcare. This narrative review examines the evidence on nurse-led telephone triage, comparing it with physician-led models and exploring emerging technologies like artificial intelligence. The available evidence suggests that nurse-led systems, when supported by appropriate protocols and training, can safely improve patient access to care while reducing healthcare costs. Physician-led models may offer advantages for complex cases but at higher costs. While artificial intelligence shows promise for enhancing triage accuracy, current evidence specific to telephone triage remains limited. Healthcare organizations should carefully consider their population needs, available resources, and local context when implementing teletriage services....
The proliferation of massive antenna arrays and the consequent intensification of near-field effects with 6G necessitate addressing critical security challenges in near-field communication environments. This paper presents a novel artificial noise-aided spatial and directional modulation (SDMN-AN) framework, specifically tailored for secure near-field communications. The proposed system integrates legitimate receiver indices, modulation symbols, and artificial noise (AN) confined to the null space of legitimate channels, thereby enhancing both spectral efficiency and communication security. Two precoding strategies—maximumratio transmission (MRT) and zero-forcing (ZF)—are investigated, offering trade-offs between hardware complexity and detection overhead. Analytical derivations of bit error rate (BER) bounds, corroborated by simulation results, underscore the superiority of the SDMN-AN framework in mitigating eavesdropping threats while significantly improving spectral efficiency, positioning it as a compelling solution for next-generation secure wireless networks....
With the rapid development of 5G communication technology, 5G networks are designed to achieve three major objectives: higher bandwidth, support for a greater number of connected devices, and lower latency. It is necessary to meet the requirements of the three primary 5G application scenarios: Enhanced Mobile Broadband, Massive Machine-Type Communications, and Ultra-Reliable and Low Latency Communications (uRLLC). To meet the stringent requirements for time synchronization and low latency, 5G is being integrated with Ethernet-based Time-Sensitive Networking (TSN) technologies. TSN plays an important role in achieving time determinism in uRLLC scenarios and ensures lowlatency and high-reliability Ethernet communication through the transmission of time signals that are also known as the Precision Time Protocol. We applied TSN technology in the Institute of Electrical and Electronics Engineers 802.1Qbv standard and evaluated its transmission delay performance. Modifying the gate control list (GCL) to accommodate varying network traffic ensures low-latency transmission for high-priority traffic. We propose two GCL configurations for TSN that incorporate time-aware shaper to achieve efficient traffic scheduling....
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