In this paper, we have analyzed how the change in the solar activity is able to affect the IRI-2016 outcomes for different latitudes. We have compared the IRI-2016 model predictions with F2 layer critical frequency (foF2) data derived from the low (Manila), middle (Rome and Kiev) and high (Sodankyla) latitude ionosonde stations during high (1980) and low (1986) solar activity periods. In order to test the performance of IRI-2016, we have calculated the root mean square errors (RMSE) during both low and high solar activity periods. The results show that the deviation between the IRI-2016 predictions and the foF2 measurements are the largest for high solar activity period and this deviation varies depending on the seasons, latitudes, and local time. The IRI-2016 model predictions during low solar activity period usually provide better agreement with the observations at all latitudes. The IRI-2016 performance during high solar activity period changes considerably depending on the seasons and local time at low and high latitudes. During high solar activity period, the discrepancy in IRI-2016 predictions at low and high latitudes is found to be larger and more significant than that at mid-latitude region, particularly for winter and equinox seasons. The results show that the last version of the IRI model is not sufficient to properly represent the real conditions of high solar activity and the IRI-2016 model still needs to significant improvements particularly in the low and high latitude regions taking the impacts of solar maximum year into account.

In this paper, using the Total Electron Content (TEC) data obtained from GUAM (13.58° N, 144.86° E) and PNGM (2.04° S, 147.36° E) Global Positioning System (GPS) stations, the seasonal characteristics of equatorial plasma bubbles (EPBs) in conjugate hemispheres during the Solstices and Equinoxes of 2015 are examined. The EPBs were determined using the rate of TEC (ROT) and rate of TEC index (ROTI) indices, which are good indicators for the ionospheric instabilities and behavior of EPBs. The results from all analysis were compared with each other according to hours, seasons and hemispheres. The EPBs for all seasons were detected at the postsunset local times. The intensity level of ROTI and ROTI_ave shows variations during Equinoxes. For both hemispheres, the occurrence probability of EBPs during the Equinoxes is a maximum of 22% while the occurrence probability of EBPs during the Solstices is a maximum of 5%. The severity of EPBs was larger during March while severity of EPBs was smallest during December. It was also observed that the EPBs during Equinoxes were more predominant in the northern hemisphere while the EPBs during June were more predominant in the southern hemisphere.

Bu çalışmada, depremlerin jeomanyetik alan verileri ile ilişkisi incelenmiştir. Öncelikle konu kapsamında deneysel çalışma olarak 3-4 Kasım 2021 boyunca meydana gelen jeomanyetik fırtınanın etkileri, çalışmanın ikinci aşamasında ise 17 Kasım 2021 Düzce depreminin (M=4,8) jeomanyetik alanlar üzerindeki etkileri araştırılmıştır. Bunun için gözlem (IZN) ve referans (PEG ve PAG) manyetometre istasyonlarında ölçülmüş jeomanyetik alanın X, Y ve Z bileşenlerinin verileri kullanılmıştır. Bu bileşenlerin zamansal çözünürlüğü 60 saniyedir. Ayrıca, jeomanyetik aktivite göstergesi olarak küresel jeomanyetik aktivite indisi (Kp) verileri kullanılmıştır. İlk önce Jeomanyetik fırtınadan ve depremden kaynaklı jeomanyetik anomalileri tespit etmek için istasyonlardaki jeomanyetik alan bileşenlerinin (X, Y, Z) jeomanyetik alan değişim oranı (ROG) ve jeomanyetik alan değişim oranı indeksi (ROGI) hesaplanmıştır. Daha sonra X, Y ve Z bileşenlerinin günlük değişimleri arasındaki ilişkiyi istatistiksel olarak tespit etmek için ROGI(X, Y, Z) değerlerini kullanılarak istasyon çiftlerinin (IZN-PEG, PEG-PAG ve IZN-PAG) korelasyon katsayıları (r) hesaplanmıştır. X, Y ve Z için gözlem ve referans manyetometre istasyonlarından elde edilen sonuçlar birbirleriyle karşılaştırılmıştır. Jeomanyetik fırtına boyunca IZN, PEG ve PAG için hesaplanan ROGI(X), ROGI(Y) ve ROGI(Z) değerlerinin çok benzer bir günlük değişime sahip oldukları tespit edilmiştir. 17 Kasım 2021 boyunca PEG ve PAG istasyonlarının ROGI(Y) değerleri birbirleriyle uyumlu bir günlük değişime sahipken, 08:15 EZ (Evrensel Zaman) ile 10:10 EZ arasında IZN istasyonunun ROGI(Y) değerlerinde bir artış (anomali) tespit edilmiştir. Bu sonuçlardan, IZN için tespit edilen anomalinin 17 Kasım Düzce depremi ile olası ilişkili sismomanyetik kaynaklı bölgesel öncül bir jeomanyetik anomali olarak değerlendirilebileceği öngörülmektedir.

Peer-to-peer (P2P) energy trading combines local energy resources, market coordination, data-driven decisions, and transaction management. This review examines how machine learning (ML) and blockchain are used across these functions and separates market and ledger processes from physical electricity delivery. A structured review procedure was applied to a corpus of 52 peer-reviewed journal articles, including the core P2P energy-trading evidence and a limited number of closely related contextual studies, supplemented by 10 non-journal or foundational sources, using defined search families, screening criteria, and qualitative synthesis. The literature is organized by the functional role of ML and compared across architecture, market operation, trust, consensus, privacy, and implementation. The consensus discussion considers practical Byzantine fault tolerance, Istanbul Byzantine fault tolerance, proof-of-authority, and application-oriented Byzantine-fault-tolerance variants, while the privacy discussion distinguishes federated learning, differential privacy, zero-knowledge proofs, and secure multiparty computation. Two deterministic MATLAB examples are included only for illustration. In the five-prosumer forecasting example, regression reduced mean absolute error (MAE) from 0.4240 to 0.2219 kWh and the hourly grid-import mismatch from 30.3529 to 7.9029 kWh. In the 10-peer workflow, five trades settled 7.7587 kWh, corresponding to 59.35% of the horizon-level surplus–deficit denominator defined in the simulation. These examples do not validate feeder feasibility, consensus performance, cryptographic security, or deployment readiness.