Dr Mohammed Mahmoud Youssif

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m3/m3, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m3/m3 and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions.

Urban flooding remains a critical hazard for rapidly urbanizing coastal cities, with climate change intensifying both its frequency and severity. This study analyzes 28 years of urban growth (1995–2023) in Alexandria, Egypt, using remote sensing, GIS, and socio-economic data to evaluate spatial justice and flood vulnerability. Three urban expansion types—infilling, edge expansion, and leapfrogging—were identified, revealing that unplanned infilling disproportionately increases flood risk for marginalized, low-income neighborhoods. The findings provide a framework for risk-informed urban planning, supporting equitable zoning, targeted infrastructure investment, and nature-based solutions. The study highlights that flood risk is not only an environmental issue but also a social justice concern, offering evidence-based strategies for decision-makers to mitigate hazard exposure.

Due to the strong nonlinearities of the boiler-turbine system, feasibility and high response capability are hard to realize using traditional linear controllers. Advanced controllers that can ensure stability and feasibility without violating system constraints, and enhance the system’s dynamic output performance, are needed. Therefore, this paper proposes a stable input/output feedback linearization (IOFL) model predictive controller (MPC) technique for the boiler-turbine system. This nonlinear system is decoupled using the IOFL method into a novel linearized model, which is then used for constituting the proposed stable IOFL MPC problem. The proposed scheme uses a constraint mapping method that converts actual input limits into limits on the basis of the control output variable to ensure a feasible solution over the whole prediction horizon. Moreover, a min-max MPC technique in the form of linear matrix inequality with the realization of the input rate-of-change constraints is utilized to ensure the boiler-turbine unit’s stability. The process model and proposed control scheme are executed using MATLAB and the simulation results demonstrate that the proposed controller has an enhanced dynamic output performance compared with an advanced control scheme under various load variations.

BACKGROUND AND STUDY AIMS: Few studies have considered patients treated with trans-arterial chemoembolization (TACE) for non-viral-induced hepatocellular carcinoma (HCC), with some reporting that those patients may have larger tumors, emphasizing the need for determination of the factors affecting survival in such patients. This work aims to study the characteristics and survival of patients with non-viral related HCC treated with TACE.

PATIENTS AND METHODS: This is a multicenter observational study. Patients (166) with non-viral related HCC treated with TACE were recruited from six tertiary care centers (January 2008- June 2022). Follow-up continued until death or the end of the study (August 2023).

RESULTS: The patients had a mean age of 60.2 ± 9.5 years, a male predominance of 79.5 %. The mean size of the lesions was 5.71 ± 3.02 cm, and 42.8 % of the patients had a single lesion. After a median follow-up period of 27.02 months (IQR 14.99-39.37), the median overall survival (OS) was 42.14 months. The Cox regression hazard model revealed that the independent factors affecting survival were: multiple focal lesions, exceeding five in number, have a substantially higher hazard of death (HR = 8.5, p-value = 0.001) compared to those with a single focal lesion. HAP score grade D exhibited a threefold increase in the hazard of death (HR = 3.8, p-value 0.007). Individuals who did not respond positively to treatment faced a significantly higher risk of death (HR = 10.76, p-value 0.001). Albumin-bilirubin score (ALBI), Easy ALBI, platelet albumin (PAL), platelet albumin bilirubin score (PALBI), The hepatoma arterial-embolisation (HAP) and Tumor burden score were found not to impact the survival of our patients.

CONCLUSION: Tumor burden is an important determinant of survival after TACE in patients with non-viral HCC. HAP score can be implemented in selecting patients who would benefit from TACE.