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Safe haven property of gold and cryptocurrencies during COVID-19 and Russia-Ukraine conflict
No full textDuring recent years the world has witnessed several unprecedented crises that affected the international financial markets. Indeed, the COVID-19 pandemic and the Russia–Ukraine conflict caused major perturbations that slowed down the economic and financial development around the globe. International investors are switching their attention to more reliable assets as a refuge to their portfolios. This paper investigates the hedge and safe haven properties of gold and major cryptocurrencies, mainly Bitcoin and Ethereum. Empirical analysis is conducted on main fiat currencies using the multivariate asymmetric dynamical conditional correlation model. Results show that gold has a superior hedging effectiveness compared to cryptocurrencies. Moreover, the precious metal and the digital currencies are safe havens for almost all fiat currencies
Life Cycle Assessment of Hybrid Vernacular-Modern Technologies: A Comparative Study of the Ecofordable House and Conventional RC Structures
No full textThis paper investigates the environmental benefits of adopting hybrid vernacular-modern building technologies through a detailed life-cycle assessment (LCA) of a residential prototype known as the “Ecofordable House” (EH). The EH integrates hybrid techniques, including partially reinforced interlocking compressed stabilized earth brick walls (ICSEB), jack arch and funicular shell roofing systems, and date palm midrib components. Its environmental impacts are compared to those of a reinforced concrete house (CH) used as the baseline conventionally adopted in the Middle East. The LCA follows a cradle-to-grave scenario, covering stages A1–A4, B1–B5, and C1–C4, with additional reference to stage D. The results showed that the CH has a value of 698.22 kg CO2e/m2, while the EH has a Global Warming Potential of 368.17 kg CO2e/m2, which represents a reduction of approximately 47% in global warming potential (GWP). Fossil-based emissions in the EH are 46% lower, biogenic emissions are reduced by 91%, and land use and land-use change (LULUC) have an impact reduction of 82%. The acidification potential is 43% lower, while eutrophication across freshwater, marine, and terrestrial resources is 28%–44% lower. The photochemical ozone creation potential (POCP) is reduced by 43%, and the resource depletion impact for elements and for fossil fuels is reduced by 50% and by 43%, respectively. Water use is 18% lower. Material production (A1–A3) is identified as the primary driver of environmental impacts for both prototypes. Fired clay bricks, concrete, and reinforcement steel are the major contributors toward GWP for CH, while Portland cement, concrete, and reinforcement steel dominate the GWP for EH, but with much lower values due to their reduced quantities. For CH, the major building part contributors are the foundation, roofs, and external walls, while in the EH, conventional reinforced concrete (RC) foundations and external walls are the major contributors. These results support the significant environmental benefits of adopting hybrid modern and vernacular building technologies and materials as ways of reducing environmental impacts while ensuring more durable and structurally sound buildings in hot, arid climates.The construction of the experimental residential unit “Ecofordable House” was funded by Dr. Nawal El-Degwi, MSA University head of the board of trustees
Rat Swarm Versus Particle Swarm Intelligent Optimization Algorithms for Maximum Power Point Tracking in Designing Energy-Efficient Solar Systems
No full textRecently, Photovoltaic (PV) devices generate electricity directly from sunlight, so their integration into urban infrastructure will not only generate energy but also reduce carbon emissions. Due to those advantages, solar energy plays a crucial role in developing smart cities. However, the efficiency of PV systems is heavily dependent on their ability to operate at the Maximum Power Point (MPP), which represents the bias potential at which the solar cell outputs the maximum net power. This paper employs an intelligent optimization algorithm, RAT Swarm, to find the optimal voltage the PV system can operate to produce maximum power. MATLAB software is used to model and simulate the system using the Rate Swarm algorithm. The simulation results show that the RAT based MPPT has been capable of achieving the capability to precisely track the maximum power point and to maximize the power output of the PV system. Furthermore, the same software is employed to implement and simulate the well-known particle swarm optimization algorithm for comparison. Convergence speed, trading accuracy as well as the stability of power, voltage and current under both uniform and time varying irradiance conditions are also considered in the comparison. Comparative evaluation indicates that RSO is a viable option for PSO owing to faster convergence. Nevertheless, the stability of RSO and the absence of an oscillation in the power output have to be investigated. These improvements would enable RSO to become optimally effective, achieving fast convergence and stable output power for energy capture from PV systems
From Historical Caliphate to Modern Governance Alliance: Tracing the Origin and Evolution of the Organization of Islamic Cooperation
No full textHusain_From+Historical+Caliphate+to+Modern+Governance+Alliance.pdfFounded in 1969, the Organisation of Islamic Cooperation (OIC) bears a modern iteration of the traditional notion of Islamic caliphate, embodying the leadership ideals of the Islamic world. Historically, the caliphate symbolised a unified system of good governance under individual rulers, with the fall of the Ottoman Empire marking its end and heralding significant ideological transformations within the Muslim community. In the wake of modernity and postcolonialism, as traditional Islamic governance structures were reevaluated amidst the rise of nationalism and nation-states, the Israeli-Palestinian conflict crystallised these shifts, fostering a collective Islamic identity through the OIC. This cooperation not only seeks to uphold religious identity and political influence but also to perpetuate Islamic governance in the contemporary milieu. This paper investigates the OIC’s evolution from historical caliphate ideals to modern polity, assessing its ideological foundations, pivotal role and enduring relevance in today’s global landscape.N
SALMANI-INSPIRED ARCHITECTURE | URBAN CULTURAL EXPERIENCE
No full textThe King Salman Park Visitor Center integrates the values of Salmani architecture with
sustainable innovation, reflecting the cultural identity of Riyadh and embodying the ambitions
of Vision 2030. Strategically embedded within the green cultural zone of King Salman Park,
the center acts as a gateway for discovery, environmental education, and community
engagement, rooted in the park’s natural topography and green landscapes.
The project program includes diverse spaces that serve public, semi-public, and private
functions: exhibitions, an auditorium, a VR area, shops, lounges, restaurants, training rooms,
and supporting technical and service zones. These functions are carefully organized to provide
an engaging and seamless visitor experience that encourages exploration and learning.
Inspired by the Salmani Urban Charter, the building’s architectural form responds to both site
topography and functional needs. Its folding geometry and triangular apertures evoke the
traditional architectural language of Najd while ensuring environmental performance through
natural ventilation, shading, and integration with the landscape.
The design philosophy emphasizes sustainability, wellness, innovation, and cultural continuity.
Eco-friendly materials and renewable energy solutions reduce the building’s ecological
footprint, while open courtyards, shaded walkways, and green areas enhance the quality of
public life. The triangular patterns rooted in Salmani architecture create a strong sense of
identity, harmony, and connection with Riyadh’s heritage.
This philosophy is aligned with the core principles of the Salmani Charter:
• Respecting and preserving the site’s natural elements
• Reinforcing architectural identity and continuity
• Enhancing community well-being through meaningful, inclusive spaces
A Qur’anic verse beautifully underscores this vision of environmental stewardship
Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook.
No full textArtificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications in chemotherapy development, cancer diagnosis, and predicting chemotherapy response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) and deep learning (DL).This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field.A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included "Artificial Intelligence (AI)," "Machine Learning (ML)," and "Deep Learning (DL)" combined with "chemotherapy development," "cancer diagnosis," and "cancer treatment." Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies.This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI's potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI's integration into oncology care
Wind Power Prediction Model Based on Capuchin-Prairie Dog Optimization Technique
No full textWind energy production is involved in the existing conventional energy system or microgrid. Integrating renewable distributed generators into distribution networks offers numerous advantages in terms of technology, economy and the environment. Wind power output is significantly concerned by the wind speed, which results in unpredictable alternatives in power production. The wide range of wind speeds, a critical factor in producing energy, describes several opportunities for short-term wind power forecasting. The data set of wind turbines comprises measurements of different parameters, including wind velocity, wind flow orientation and other environmental factors that affect the measurement of wind energy production. Recently, various AI and optimization-based methods have been improved for wind production forecasting. Many wind power prediction projects estimate the total power generated by all turbines in the area, considering time-varying characteristics. Unfortunately, the location and surroundings of wind turbines are often overlooked, and there is a lack of consideration for the variability of power output from turbines at various locations. Furthermore, effective wind power prediction is a complex undertaking that must consider the geographic proximity of wind power factors and the time dependence of sequential data. The model of this paper is based on optimization techniques called Capuchin-Prairie Dog Optimization (CPDO). The objective of the proposed method is to systematically examine the correlation between different wind parameters such as average wind speed, average wind direction and wind power. It purposes to adopt the values of parameter selection, at last adjusting the predictive accuracy of the model. The proposed technique also evaluates the prediction values of wind speed, and it achieved a higher performance and the overall evaluation in short-term wind energy prediction, demonstrating superior prediction accuracy