Modeling the effect of bioreactor pH on Chinese Hamster Ovary (CHO) cell metabolism and site-specific N-linked glycosylation of VRC01

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Jallikattu – beyond a heroism sport for SKCRF

Learning outcomes The learning objectives have been prepared in accordance with the Blooms Taxonomy (Engelhart et al., 1984). After completion of this case, students would be able to examine and expand the concept of institutions (i.e. a sport as an institution in this case) and understand how important it is to incorporate them in the policy level decision-making (Knowledge); understand a different form of the social institution (i.e. Jallikattu) and capture its relevance for all the involved stakeholders by taking into consideration the challenges that could stem from their interplay (Application); analyze the interests of various stakeholders and their concerns that add to the complexity of a socially relevant issue (Analysis); and integrate the developments of an event (i.e. Jallikattu) over its timeline and develop an action plan for being prepared or for resolving such exigencies, especially for public policy decision making (Synthesis). Case overview/synopsis The case is centred on Senaapathy Kangayam Cattle Research Foundation whose primary aim is conservation and breeding of native breeds of cattle. The protagonist of the case, Karthikeya Sivasenapathy, managing trustee of this foundation, has invested significant efforts to create awareness on the importance of Jallikattu. Jallikattu is an ancient Indian sport played in the rural regions of Tamil Nadu state in India and has been in existence for over 5,000 years. This issue has come into limelight due to its initial ban by the Supreme Court of India in 2014 and its subsequent stay on the ban in 2016. While there are several arguments surrounding this controversy, the arguments can be broadly classified under those who support the ban (i.e. oppose Jallikattu) and those who oppose the ban (i.e. support Jallikattu). Due to the involvement of various stakeholders (government supreme court, animal welfare boards and breed saviour groups) with conflicting objectives, the dynamics of decision-making to settle this issue became very complicated, confusing and time-consuming for Karthikeya. By using the lens of institutions and stakeholder theory, the authors explain the issue around Jallikattu in this teaching note. Teaching note also documents the unfolding of events that happened after 12 January 2017 which succeeded in lifting the ban on Jallikattu. Complexity academic level The case is written for undergraduate and graduate-level students pursuing business programmes and for senior management professionals participating in the executive education programmes. The case is suitable for those who are expected to work in an environment where there is a multitude of complex, formal as well as informal institutions. This case can be used to teach the concepts of institutions, the dynamics involved and to give the flavour of the interactions between these different institutions in solving a social issue. It will fit well into courses on strategic management, social movement and institutional theory. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email [email protected] to request teaching notes. Subject code CSS 1: Accounting and Finance. </jats:sec

Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

Predicting the amount of combustion generated nano-scale particulate matter (PM) emitted by gasoline direct injection (GDI) is a challenging task, but immensely useful for engine calibration engineers in order to meet the stringent emission legislation norms. The present work aimed to link the in-cylinder combustion with engine-out nano-scale PM for the size range of 23.7 to 1000 nm diameter. Neural network with a single hidden layer using first 8 principal components of cylinder pressure was employed for training and predicting the number of nano-scale PM number count. Using a systematic computational approach and comparing its results with experimental data this work demonstrates that machine-learning approach based on neural network is sufficient for predicting engine out nano-scale PM count as a function of engine load and speed

DEVELOPMENT AND VALIDATION OF RP-HPLC METHOD FOR SIMVASTATIN USING LOVASTATIN AS INTERNAL STANDARD

Simvastatin is a potent competitive inhibitor of 3hydroxy-3-methylglutaryl coenzyme A reductase, which is a rate-limiting enzyme in cholesterol biosynthesis. It may also interfere with steroid hormone production due to the induction of hepatic LDL receptors, it increases breakdown of LDL cholesterol.  We have established a sensitive and accurate High performance liquid chromatographic method for determination of Simvastatin as per the ICH guidelines using specificity, linearity, limit of detection, limit of quantification, Precision and accuracy. Chromatography was performed with an analytical Inspire C18 column (250 mm x 2.0 mm, 5 µm), Shimadzu HPLC model with LC 10AD HPLC Pump and SPD 10A HPLC UV-Detector, and using acetonitrile: 0.1% glacial acetic acid (80: 20 % v/v) as the mobile phase. The linearity of Simvastatin is 0.999 over a concentration range of 0.1 to 10 µg/ml. Interday and intraday variability was &lt; 10%. Keywords: Simvastatin, Lovastatin, HPL

Novel Approach to Validate the Content Generated by LLM

The unprecedented growth of Large Language Models (LLMs) has transformed text generation, but maintaining the validity and dependability of their output is a still an unresolved problem. This article presents an overall framework for validating LLM output using a hybrid methodology that unites automated testing and human auditing. The approach uses fact-checking tools, semantic coherence tests, and source-based authentication to rigorously examine the accuracy, coherence, and factuality of generated output. Through the incorporation of these methods, the framework solves major shortcomings in existing validation methodologies, such as hallucination and contextual drift. To illustrate pragmatic usability, the validation method is applied in a document-based QA(Question and Answers) system with Google's Gemini model. The system cross-checks LLM results against source PDF content and applies confidence scoring for marking uncertain responses. Experimental work indicates that it is possible to enhance validation accuracy by 72% by coupling retrieval-augmented verification and statistical uncertainty estimation with individual standalone methods. Adaptive learning is incorporated into the framework to improve the validation rules under error patterns so that the entire system is enhanced in terms of robustness to different domains. This work adds to the emerging body of reliable AI through the introduction of a scalable, multi-modal verification solution for LLM-based applications. The developed methods not only enhance output trustworthiness in document analysis systems but also define generalizable best practices for content verification. Real-time validation architectures and domain-specific adaptation mechanisms will be investigated in future work to further enhance the robustness of the framework in mission-critical application