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Over the last decade, West African coastal countries, including Ghana, have experienced extensive economic damage due to illegal, unreported and unregulated (IUU) fishing activity, estimated at about USD 100 million in losses each year. Illegal, unreported and unregulated fishing poses an enormous threat to the conservation and management of the dwindling fish stocks, causing multiple adverse consequences for fisheries, coastal and marine ecosystems and for the people who depend on these resources. The Integrated System for Surveillance of Illegal, Unlicensed and Unreported Fishing (INSURE) is an efficient and inexpensive system that has been developed for the monitoring of IUU fishing in Ghanaian waters. It makes use of fast-delivery Earth observation data from the synthetic aperture radar instrument on Sentinel-1 and the Multi Spectral Imager on Sentinel-2, detecting objects that differ markedly from their immediate background using a constant false alarm rate test. Detections are matched to, and verified by, Automatic Identification System (AIS) data, which provide the location and dimensions of ships that are legally operating in the region. Matched and unmatched data are then displayed on a web portal for use by coastal management authorities in Ghana. The system has a detection success rate of 91% for AIS-registered vessels, and a fast throughput, processing and delivering information within 2 h of acquiring the satellite overpass. However, over the 17-month analysis period, 75% of SAR detections have no equivalent in the AIS record, suggesting significant unregulated marine activity, including vessels potentially involved in IUU. The INSURE system demonstrated its efficiency in Ghana’s exclusive economic zone and it can be extended to the neighbouring states in the Gulf of Guinea, or other geographical regions that need to improve fisheries surveillance.

Accelerating ocean crises driven by interacting stressors such as fossil-fuel-driven warming and inequitable governance are pushing marine systems toward tipping points with cascading global impacts. Here, we call for ocean science to move beyond observation toward active engagement by forging strategic alliances, centring storytelling, and acknowledging the political nature of science. By engaging in this manner, ocean scientists can help transform crisis into opportunity, rebuilding the science-society interface to advance more resilient and just ocean futures.

The rapid growth of 6G Internet of Things (IoT) networks demands scalable and secure learning systems that can support massive device connectivity with minimal coordination overhead. Federated learning (FL) over grant‐free non‐orthogonal multiple access (GF‐NOMA) offers a promising approach by enabling distributed model training with asynchronous uplink access and low signalling cost. However, this setup introduces coupled vulnerabilities: The uncoordinated nature of GF‐NOMA leads to random collisions and residual interference, while the decentralised nature of FL exposes the system to poisoning, Sybil and jamming attacks. These cross‐layer threats jointly degrade model convergence and communication reliability. To address this, we propose Security‐Aware Proximal Policy Optimisation (SA‐PPO), a reinforcement learning framework that co‐designs communication security for FL over GF‐NOMA. SA‐PPO jointly embeds physical‐layer features (e.g., SINR and interference) and learning‐layer signals (e.g., anomaly scores and trust values) into its state, action and reward spaces. This enables the base station to optimise admission control, resource allocation and trust‐weighted aggregation in a unified loop. Unlike prior methods that treat communication and security independently, SA‐PPO learns coordinated strategies that attenuate adversarial impact while preserving update diversity. Simulation results show that SA‐PPO achieves over 90% anomaly detection accuracy, sustains secure participation above 80% and reduces collision‐induced decoding errors by 25% under scenarios with up to 40% compromised devices, while incurring only modest increases in energy and latency. These results demonstrate SA‐PPO's effectiveness for secure, scalable and resilient edge intelligence in future 6G IoT environments.

In this paper, we propose a linear assignment problem (LAP) scheme using greedy algorithms to alleviate the intercell interference (ICI) in massive multi-input multiple-output (maMIMO) systems. ICI has been recognized as one of the main challenges of massive MIMO systems and occurs when pilot sequences (PSs) are reused across neighboring cells or using non-orthogonal PS, and this results in a phenomenon known as pilot contamination (PC). The proposed scheme uniquely assigns pilot sequences to mobile terminals to mitigate PC such that the optimal or near-optimal solution is achieved. This scheme attains maximum SINR by assigning pilot sequence to mobile terminals that will only produce the least PC. Results obtained from simulation showed that the proposed assignment scheme achieved a greater sum rate than some established assignment schemes when compared.

Due to the unprecedented growth in mobile data traffic, emerging mobile access networks such as fifth-generation (5G) would require huge bandwidth and a mobile fronthaul architecture as an essential solution in providing a high capacity for support in the future. To increase capacity, utilizing millimeter waves (mm-waves) in an analog radio over fiber (RoF) fronthaul link is the major advancement and solution in achieving higher bandwidth and high data rate to cater for 5G mobile communication. In this paper, we demonstrate the feasibility of transmission and reception of a 100 Gbits/s data rate link at 28 GHz. The performance of three modulation formats (16-PSK, 16-QAM and 64-QAM) have been compared for an optical fiber length from 5 km up to 35 km for two detection systems; coherent and direct detection. Also, in this paper, the transmission impairments inherent to transmission systems are realized through the implementation of a digital signal processing (DSP) compensation scheme in the receiver system to enhance system performance. Quality factor (QF) and bit error rate (BER) are used as metrics to evaluate the system performance. The proposed system model is designed and simulated using Optisystem 16.

Social media and other web 2.0 tools have provided users with the platform to interact with and also disclose personal information to not only their friends and acquaintances but also relative strangers with unprecedented ease. This has enhanced the ability of people to share more about themselves, their families, and their friends through a variety of media including text, photo, and video, thus developing and sustaining social and business relationships. The purpose of the paper is to identify the factors that predict self-disclosure on social networking sites from the perspective of privacy and flow. Data was collected from 452 students in three leading universities in Ghana and analyzed with Partial Least Square-Structural Equation Modeling. Results from the study revealed that privacy risk was the most significant predictor. We also found privacy awareness, privacy concerns, and privacy invasion experience to be significant predictors of self-disclosure. Interaction and perceived control were found to have significant effect on self-disclosure. In all, the model accounted for 54.6 percent of the variance in self-disclosure. The implications and limitations of the current study are discussed, and directions for future research proposed.

Objective. Peripheral sensory neuropathy (PSN) is among microvascular complications of diabetes that make patients prone to ulceration and amputation. Arterial stiffness is a predictor of cardiovascular diseases and microvascular complications associated with diabetes. We investigated the association between PSN and arterial stiffness, measured as aortic pulse wave velocity (PWVao) and cardio-ankle vascular index (CAVI). Method. In a case-control design, arterial stiffness was measured in 240 diabetes patients and 110 nondiabetic control. Large-fibre nerve function was assessed by vibration perception threshold (VPT) using a neurothesiometer. PSN was defined as the VPT > 97.5th percentile from age- and gender-adjusted models in nondiabetic controls. Results. The overall prevalence of PSN was 16.6% in the entire study participants. Compared to non-PSN participants, PSN patients had higher levels of PWVao (9.5 ± 1.7 versus 8.7 ± 1.2 m/s, p=0.016) and CAVI (8.4 ± 1.3 versus 7.6 ± 1.1, p=0.001). In multiple regression models, VPT was associated with PWVao (β=0.14, p=0.025) and CAVI (β=0.12, p=0.04). PSN patients had increased odds of CAVI (OR = 1.51 (1.02–2.4), p=0.043), but not PWVao (OR = 1.25 (0.91–1.71), p=0.173). Conclusion. PWVao and CAVI were associated with VPT and PSN in diabetes patients in Ghana. Patients having PSN have increased odds of CAVI, independent of other conventional risk factors.