A Mathematical Model of Dignāga’s Hetu-cakra

A reasoned argument or tarka is essential for a wholesome vāda that aims at establishing the truth. A strong tarka constitutes of a number of elements including an anumāna based on a valid hetu. Several scholars, such as Dharmakīrti, Vasubandhu and Dignāga, have worked on theories for the establishment of a valid hetu to distinguish it from an invalid one. This paper aims to interpret Dignāga’s hetu-cakra, called the wheel of grounds, from a modern philosophical perspective by deconstructing it into a simple probabilistic mathematical model. The objective is to understand how and why a vāda based on a probabilistically weaker hetu can degrade into a Jalpa or vitaṇḍā. To do so, the paper maps the concept of ‘Bounded Rationality’ onto the hetu-cakra. Bounded Rationality, an idea coined by the management thinker Herbert Simon, is often employed in understanding decision-making processes of rational agents. In the context of this paper, the concept would state that the prativādin and ālocaka (debater) may not hold unbounded information to back their pratijñā (proposition). The paper argues that within the probabilistically deconstructed hetu-cakra model, most people argue in the ‘Zone of Bounded Rationality’, and thus, the probability of a debate degrading into Jalpa or vitaṇḍā is high

UBSegNet: Unified Biometric Region of Interest Segmentation Network

Digital human identity management, can now be seen as a social necessity, as it is essentially required in almost every public sector such as, financial inclusions, security, banking, social networking e.t.c. Hence, in today's rampantly emerging world with so many adversarial entities, relying on a single biometric trait is being too optimistic. In this paper, we have proposed a novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for extracting region of interest from five different biometric traits viz. face, iris, palm, knuckle and 4-slap fingerprint. The architecture of the proposed UBSegNet consists of two stages: (i) Trait classification and (ii) Trait localization. For these stages, we have used a state of the art region based convolutional neural network (RCNN), comprising of three major parts namely convolutional layers, region proposal network (RPN) along with classification and regression heads. The model has been evaluated over various huge publicly available biometric databases. To the best of our knowledge this is the first unified architecture proposed, segmenting multiple biometric traits. It has been tested over around 5000 * 5 = 25,000 images (5000 images per trait) and produces very good results. Our work on unified biometric segmentation, opens up the vast opportunities in the field of multiple biometric traits based authentication systems.Comment: 4th Asian Conference on Pattern Recognition (ACPR 2017

A comparative study on strategic analysis and forecasting on profit maximization and operational efficiency in manufacturing business through differential equations

Differential equations are fundamental mathematical tools with wide-ranging applications in science and economics. This study delves into their role in business, focusing on strategic analysis and forecasting for profit maximization and operational efficiency in manufacturing. It explores various equation types, from ordinary to partial differentials, highlighting their critical role in modeling economic phenomena. Through a comprehensive case study, this research demonstrates the practical application of differential equations in optimizing production, sales, revenue, and profit. The study emphasizes their impact on strategic decision-making and navigating complex market dynamics for sustained growth and profitability

Remaining Useful Life Prediction of Lithium-ion Batteries using Spatio-temporal Multimodal Attention Networks

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is challenging, and existing methods have limitations in predicting the number of cycles iteratively. In addition, existing works often oversimplify the datasets, neglecting important features of the batteries such as temperature, internal resistance, and material type. To address these limitations, this paper proposes a two-stage remaining useful life prediction scheme for Lithium-ion batteries using a spatio-temporal multimodal attention network (ST-MAN). The proposed model is designed to iteratively predict the number of cycles required for the battery to reach the end of its useful life, based on available data. The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works. Experimental results demonstrate that the proposed ST-MAN model outperforms existing CNN and LSTM-based methods, achieving state-of-the-art performance in predicting the remaining useful life of Li-ion batteries. The proposed method has the potential to improve the reliability and efficiency of battery operations and is applicable in various industries, including automotive and renewable energy

Assessment of quality of life of type-2 diabetes mellitus patients in tertiary care teaching hospital in North India

Background: Quality of life (QoL) is a standard indicating a person’s life in better condition as compared to a diseased person or patient. Diabetes itself is not a dangerous disease if managed properly, but it becomes life-threatening after a certain time period because of the patient’s poor interest in disease management and treatment adherence. Diabetes mellitus is an endocrine system disorder that invokes lack of insulin secretion in the bloodstream. The study was conducted to assess the QoL of type-2 diabetes mellitus patients with QoL instrument.Methods: Questionnaire based cross-sectional, prospective study was conducted at Teerthanker Mahaveer Hospital, Moradabad, for a duration of 6 months from January - June 2019, on 196 patients of type-2 diabetes mellitus.Results: On an average 56.5% people have accepted that due to the diabetic condition their working proficiency is decreased. 80.3% of patients have accepted that they were facing problems due to diabetic symptoms. Similarly, 84% of patients have accepted that the ongoing treatment was quite expensive than their expectations. Finally, the last section of the patient’s emotional/mental satisfaction showed that 49.7% of patients were very satisfied with their family support while 13.7% of patients were not satisfied.Conclusions: QOL instrument for Indian diabetes patient’s instrument helped to evaluate the patient’s physical strength, psychological strength of the patient during disease condition, the response provided from the family members and relatives, the economical status of the patient and its effects on their living