STEM-POWERED Pakistan

What is Sickle Cell Anemia:  Sickle cell anemia is one disorder out of a group of inherited disorders known as sickle cell disease. It affects the shape of red blood cells which transport oxygen to the entire body. Normal red blood cells have a biconcave disc shape and they are flexible so they can easily fit even into smaller blood vessels like capillaries. RBCs affected with sickle cell anemia have a shape like a crescent moon or a sickle. RBCs become rigid and sticky. Cause:  Sickle cell anemia is caused by a change in the gene (HBB) which codes for the synthesis of hemoglobin, the iron rich protein which is specifically responsible for carrying oxygen in red blood cells. In a normal hemoglobin, the sixth amino acid in the beta globin chain is glutamic acid. Glutamic acid is a hydrophilic amino acid which means it interacts with the watery environment of the cell and maintains the normal shape. In SCD however, this is replaced by the amino acid valine which is hydrophobic m...

 INTRODUCTION Recent research has begun to uncover the hidden environmental costs of artificial intelligence with growing attention on its significant water footprint. Studies conducted by environmental scientists and computer engineers have used a combination of data audits, model training logs and cooling system analyses to estimate water usage across AI operations. Findings reveal that training large scale AI models such as Chat GPT can consume millions of litres of fresh water primarily for cooling the massive servers required to process complex calculations. Training GPT in Microsoft US data centers is estimated to evaporate about 700,000  litres of water directly for cooling. A 2025 study (University of California, Riverside) estimates 10–50 Chat GPT responses consume about 500 mL of fresh water, so roughly 0.5 L per 10–50 queries, or around 10–50 mL per response. The water demand is often localised placing additional stress on regions already facing water scarcity. For examp...

Artificial Intelligence (AI) and Machine Learning (ML) are becoming a big part of our daily lives. They are used in things like facial recognition systems and loan applications, which means they can be a significant part of a persons identity or financial independence. But researchers have shown that these systems are not always fair.  For example, a study by researchers at MIT found that facial recognition technology works very well for lighter-skinned men but makes many more mistakes for darker-skinned women. In some cases, the error rate was over 30% for darker-skinned women, while it was less than 1% for lighter-skinned men.  Similar issues happen with financial systems. Loan algorithms sometimes end up rejecting people just because of where they live or because they are part of a minority group, even if they are just as qualified for a loan as others.  These examples show what we already know: AI can be biased, the bias usually comes from the data it is trained on, a...

FACES OF DECEPTION LEGAL AND ETHICAL CONCERNS OF DEEPFAKES A research Paper by: Eshaal Ammar, Maheen Faheem, Bazil Jalal, Mahd, Sonia, Purvi Abstract: Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. Deep fakes are a form of synthetic media generated through deep learning algorithms, that convincingly imitate real people, manipulating audio, images , or video to create highly realistic but fabricated content. Deepfakes present both innovative opportunities and major risks . This paper examines (1) what deep fakes are, (2) whether and how deep fakes infringe IP and publicity rights. And furthermore, it simultaneously (3) helps to understand the effects of deep fakes caused in society and challenges possessed due to misinformation, identity theft and fraud.   Introduction: “CEO: Sir, please transfer me $10,000 from accounts, we are going to sign these documents." The direct...

Muhammad Ibrahim¹, Muhammad Ahmed², Sara Hania³ ¹ Institute of Information Management, University of the Punjab, Lahore, Pakistan ² Institute of Information Management, University of the Punjab, Lahore, Pakistan ³ Department of Computer Science, Alpha College, P.E.C.H.S Block 6, Karachi, Pakistan Abstract Every year in Pakistan, thousands of university applicants submit multiple applications without clear guidance, often choosing degrees for which they either lack the required merit or genuine interest. This leads to wasted admission fees, reduced chances of selection, and, in many cases, students ending up in fields they later disengage from—sometimes even abandoning their studies, resulting in wasted years and financial loss. To address this issue, we developed a basic machine learning–based recommendation model that matches a student’s aggregate score and field of interest with suitable degree programs. The model was built and trained using last-year merit data and categorized in...