Psoriasis is a chronic inflammatory disease affecting approximately 2–3
Artificial Intelligence (AI) is revolutionizing computational biology by introducing innovative methods across various fields. This review explores the role of AI in key areas such as drug discovery, genomics, and proteomics. In genomics, AI accelerates tasks like DeoxyriboNucleic Acid (DNA) sequencing and gene function analysis, enhancing our understanding of gene interactions. In proteomics, AI models aid in predicting protein functions, while in drug discovery, AI facilitates virtual testing of drug candidates, target identification, side-effect prediction, drug function analysis, and classification of anatomical therapeutic chemical classes, and drug property optimization, significantly expediting drug development. However, challenges remain, including diverse and noisy datasets, data imbalance, model transparency issues, and the limited generalizability of AI models to new species, cell types, or conditions. The effective application of AI also requires seamless collaboration among computational scientists, biologists, and clinicians, which can be difficult to achieve. This review identifies critical research challenges and proposes solutions, such as improved data integration techniques, more interpretable AI models, and hybrid approaches that combine AI with biological knowledge. Future directions emphasize the need for enhanced data-sharing frameworks, interdisciplinary collaboration, and advanced tools capable of managing complex biological information. By addressing these challenges, AI has the potential to drive significant advancements in computational biology, enriching our understanding of biology and advancing precision medicine.
Design is considered one of the most critical tasks for architects. Architecture design studios introduce undergraduate students to the architectural profession by immersing them in the design process, enabling them to apply theoretical knowledge whilst supporting cognitive processes involved in architectural design and reflective design reasoning. This study presents a structured narrative review based on systematic database search of the literature on design teaching approaches in architecture design studios. The review focuses on analysing pedagogical methods and studio outcomes, drawing from papers published between 1980 and June 2024. From these, 72 publications were selected based on their focus on experiments or reviews encompassing all stages of the design process. The findings categorise the reviewed research into five key methodologies: Reflective Design Studio, Collaborative Design Studio (including collaborations with peer institutions), Computer-Aided Design Studio, Interdisciplinary Design Studio, and Online Design Studio, as well as their hybrid variants. All types of studios incorporate a comprehensive design process and emphasise specific pedagogical approaches, ranging from self-reflection and peer collaboration to the integration of digital tools and interdisciplinary involvement. While the identified methodologies reflect current ADS trends, challenges still remain in assessing the effectiveness and efficacy of different pedagogical approaches and adapting to the fast-growing technological advancements. The study contributes an integrated historical-pedagogical typology of undergraduate architecture design studios and identifies gaps in evidence related to learning outcomes and evaluation practices. Future research should focus on developing flexible learning experiences and robust evaluation frameworks, along with an investigation of the impact of technology, promoting equity and inclusion, and fostering interdisciplinary collaborations to create a more adaptable and inclusive architecture education.
Primary recognition of water pollutants remains challenging due to the restrictions in sensitivity and resolution of conventional sensors. The present study shows a multilayer sensor configuration containing black phosphorus (BP) for improved electric field confinement and cerium trifluoride (CeF3) for optimized low loss dielectric tuning capacity in the Kretschmann configuration, enabling sensitive refractive index sensing in water solution (1.33–1.40). Transfer matrix method (TMM) simulations analyze multilayer optical responses and performance metrics, while COMSOL provides electric field visualizations that validate enhanced evanescent coupling. The proposed numerical study gives superior performance with a maximum sensitivity of 417.1104 deg/RIU, detection accuracy of 0.306937 deg−1, and a figure of merit of 128.0265 RIU−1, outperforming conventional metal-only surface plasmon resonance (SPR) sensor designs. This study establishes the BP–CeF3 hybrid approach for applications like water quality analysis with environmental monitoring.
The efficiency and sustainability of solar panels throughout the day remain significant challenges, primarily due to the temperature rise in solar cell materials during peak sunlight hours, which reduces their efficiency. This study aims to enhance the efficiency of solar panels using an air-cooling mechanism. Based on prior insights, an indoor experimental setup was developed, featuring a cooling system with 196 circular pin fins, each with a diameter of 3 mm and a length of 16 mm, mounted on the rear surface of the solar panel. An aluminum heat sink of 3 mm thickness was integrated to support the fins, while a variable-speed fan supplied airflow across the fins. The solar flux and airflow rate were identified as critical parameters influencing solar panel efficiency. These parameters were optimized using Response Surface Methodology, with ranges of 400-800 W/m2 for solar flux and 0.01-0.02 m3/s for airflow rate. Optimization was performed using MINITAB 17 and Design Expert 18 software. The optimized input conditions, solar flux of 403.33 W/m2 and airflow rate of 0.0221 m3/s, yielded the following outcomes: exergy efficiency of 15.79%, power output of 4.12 Wp, module temperature of 22.43 degrees C, and solar panel efficiency of 14.48%, with a composite desirability score of 0.5737. This work is novel and new in its simple and light weight arrangement as compared to heavy vibrating pumps required in liquid and nano-fluid cooling. Additionally, the optimization approach and economic analysis of the solar panel cooling system are relatively new and have received little attention in previous literature. Perturbation plots revealed that solar flux had a more pronounced effect on panel performance compared to airflow rate. This study highlights the potential of air-cooling systems to mitigate midday efficiency losses and improve the operational sustainability of solar panels. The findings contribute to advancing cooling technologies for solar energy systems, promoting greater energy efficiency and reliability.
