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By:

Rajendra Joshi

3 December 2024 at 9:20:26 am

The ‘sweet’ scam behind the sugar price surge

AI generated image Kolhapur: A sugar shortage can push up prices. But what happens when prices rise sharply despite the country having enough sugar in its warehouses? That is the uncomfortable question emerging from the sugar market’s July-August price surge. There was no evidence of an outright shortage when the new sugar season approached. Government estimates indicated that the country would have around 43 lakh metric tonnes of carry-forward stock. The Centre, relying on this broad...

The ‘sweet’ scam behind the sugar price surge

AI generated image Kolhapur: A sugar shortage can push up prices. But what happens when prices rise sharply despite the country having enough sugar in its warehouses? That is the uncomfortable question emerging from the sugar market’s July-August price surge. There was no evidence of an outright shortage when the new sugar season approached. Government estimates indicated that the country would have around 43 lakh metric tonnes of carry-forward stock. The Centre, relying on this broad availability, permitted exports of 20 lakh tonnes. And yet, the market behaved as though sugar had suddenly become scarce. At the beginning of July, tender prices were around Rs 3,800 a quintal. By the latter half of the month, prices began climbing rapidly. In the retail market, sugar prices that were around Rs 45 a kg reportedly touched Rs 78. The government responded with stock limits and permission for duty-free imports of 10 lakh tonnes. But these interventions address the symptoms. They do not answer the more fundamental question: what caused the price spike in the first place? The first clue could lie in the lifting quota. The quantity of sugar released into the market in July was lower than expected. By itself, a lower monthly quota need not create a crisis when overall stocks are comfortable. But markets are driven not only by physical availability; they are also driven by expectations. A smaller release can create the perception of scarcity. Once that perception takes hold, stock-holding can become more profitable. The initial spark need not be large if the market has enough speculative fuel. This is why the July episode deserves a forensic examination. The timing is particularly significant. July marked the transition from Ashadh to Shravan, when the festive calendar begins and sugar consumption traditionally strengthens. At such a time, the normal expectation would be that adequate stocks are released into the market to prevent abnormal price escalation. Instead, prices moved sharply upwards. Was this coincidence, a genuine mismatch between demand and supply, or something more? The GST system offers an unusually powerful investigative tool. Every registered transaction leaves a digital footprint. A sugar mill selling to a registered trader generates an invoice recording the quantity, value, date and buyer. The trader’s subsequent sale generates another transaction. GST returns and e-invoices can therefore help create a chain showing who purchased sugar, at what price, when it was invoiced and when it was subsequently sold. This information can be matched with physical stock registers, dispatch records and warehouse inventories. The key question should be simple: Did the sugar that was shown as sold actually enter the market? Suppose a trader purchased substantial quantities in July when prices were relatively low, but the commodity remained physically stored at or near the mill. If the stock was released only after prices rose substantially in August, investigators would have a legitimate reason to examine the transaction more closely. That alone would not prove hoarding or cartelisation. A commercial decision to hold inventory is not illegal merely because prices subsequently rise. Large purchases before a price surge. Delayed physical movement. Repeated transactions among related entities. Unusually high margins. Sudden releases when prices peak. These are precisely the patterns that a data-led investigation can identify.

AI in Sperm Sorting: An Unbiased Decision for A Better Outcome

Artificial Intelligence or AI is revolutionising fertility treatments of the future. The inclusion of AI enhances the accuracy, efficiency, and objectivity of sperm selection, hence potentially improving fertility outcomes by leaps and bounds. Traditionally, sperm sorting through manual methods is subjective to judgments. Processes like centrifugation and swim-up methods are used to separate sperm based on motility and morphology. Although they are effective, they have their limitations, leading to human errors that affect the success rates of fertility treatment. For instance, studies have shown that traditional sperm sorting techniques can have variability in success rates, with reported live birth rates ranging between 15 per cent to 25 per cent per cycle depending on the method and quality of sperm. Hence the introduction of AI helps in maintaining consistency in evaluations of sperm, using the same data set for every sample which leads to better judgments.


Automation and Standardisation- Automation of sperm selection and also introduction of AI in the process have improved the results in ART. AI-assisted sperm selection improves the accuracy in choosing high-quality sperm for fertilisation purposes, and also, pregnancy and live birth rates might be improved. Technologies like Intracytoplasmic Morphologically Selected Sperm Injection along with AI ensure the chances of pregnancies increase by about 10-20 per cent compared to the standard procedures. AI and Automation will decrease time taken to analyze sperm and increase opportunities to select better sperm with DNA integrity for better development and higher success rates in embryo selection. These processes ensure that the sperm selection process follows consistent criteria, reducing variability in outcomes caused by human error.


Analysing Complex Data for Better Outcomes- AI plays a crucial in improving IVF outcomes by analysing complex data and providing tailored recommendations. AI-driven tools and models such as those on SpOvum.ai point towards an opportunity to optimise ovarian stimulation decisions by assessing patient characteristics and follicle growth patterns. A study revealed that the use of AI in IVF improved egg yield and reduced medication costs. AI enables fertility specialists to make data-driven choices, improving overall IVF success rates and streamlining treatment processes.


Reducing Human Error- AI models can continuously learn and refine their performance by being trained on newer data. This adaptability ensures the technology remains unbiased and up-to-date with the latest scientific insights into sperm quality and fertility success rates. Studies have shown that AI-driven sperm sorting can decrease human-related errors by up to 25 per cent, improving sperm selection quality in terms of morphology and motility.


Reduction of Sperm Damage- The new AI-driven sperm sorting techniques also include microfluidic systems that are known to exhibit several advantages over the most commonly used conventional method, which is centrifugation. Traditional centrifugation methods, such as density gradient centrifugation, also cause severe oxidative stress and DNA fragmentation of the sperm because of the very high mechanical forces involved. The AI-infused microfluidic sorting minimises this damage significantly by involving gentler processes that mimic the natural pathway of sperm selection. The studies show that the process of microfluidic sorting decreases DNA fragmentation in sperm, which gives improved opportunities for success for IVF. For example, DNA fragmentation is 20 percent lower in sperm sorted using microfluidic processes than in traditional processing methods.


AI is bound to play an increasingly definitive role in fertility treatments, which will improve the outcomes for couples experiencing infertility.


(The author is a Co-Founder & CEO at SpOvum® Technologies. Views personal.)

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