Volume 14 | Issue 5 |

 Abstract

Pre-seed startup evaluation is a messy problem. There is no funding history to analyse, no revenue to model, and the founder's pitch deck may be the only artefact that exists. Venture capitalists still need to assess market size, map competitors, stress-test the business model, and flag risks--but at this stage, much of that work amounts to educated guesswork. Large language models can generate business analyses that read well, yet their outputs suffer from two recurring failures: structural inconsistency (reports that cover financials one time and skip them the next) and hallucinated data (invented market figures, nonexistent competitors). This paper introduces ValidatorAI, a system that addresses both problems by constraining LLM output through a seven-dimension JSON schema derived from standard VC due-diligence checklists and grounding generation in live web search via the Gemini API. We evaluated three configurations against 50 startup concepts spanning ten sectors, scored by four independent raters on factual accuracy (FA), analytical completeness (AC), and decision quality (DQ) on a 1-5 Likert scale (Cohen's ? = 0.76). A 20-concept held-out subset was also run on OpenAI's GPT-4o. The full pipeline (C3) achieved FA = 3.9, AC = 4.1, DQ = 3.7--a 41% accuracy gain and 28% completeness gain over the unconstrained baseline. GPT-4o under the same schema matched on completeness (AC = 4.0) but scored lower on accuracy (FA = 3.2), lacking native search grounding. Paired t-tests confirmed significance for all C1-to-C3 comparisons (p < 0.01). The two mechanisms fix different things: schema enforcement drives completeness; search grounding drives accuracy. Neither alone was enough.

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 Keywords

Large language models, Prompt engineering, Startup evaluation, Search-grounded generation, Structured output generation, JSON schema validation, Retrieval-augmented generation, Business intelligence, Venture capital due diligence, AI-assisted decision systems, Hallucination reduction, Automated startup assessment

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 Abstract

Talent management practices play a significant role in developing and retaining talented employees in organizations. The present study focuses on the talent management practices prevailing in the Information Technology (IT) industry in Bangalore. The study emphasizes the importance of effective strategies for improving organizational performance and employee retention. It highlights how organizations identify and nurture employees' hidden talents and competencies. Effective talent management practices also contribute to employee development and career growth. The study suggests that proper talent management helps reduce labour attrition in IT companies. The research identifies various factors influencing talent retention and organizational effectiveness. Data for the study were collected using a well-designed and structured questionnaire. A qualitative research approach was adopted for data collection and analysis. The findings indicate that IT companies in Bangalore follow effective talent management practices. The results show that these practices positively influence employee satisfaction and performance. Talent management practices also improve service delivery and enhance corporate image. The study reveals a strong relationship between employees' skills, competencies, and talent retention. The research area was selected because Bangalore is a major hub for IT industries.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Information Technology (IT) Companies, Talent Management Practices and Employee Training Programme

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nanomedicine is a rapidly growing area of healthcare that uses nanosized materials for the diagnosis, prevention, and treatment of different diseases, including cancer, infectious diseases, and neurological disorders. Various types of nanoparticles, such as lipid-based, polymeric, and inorganic nanoparticles, are widely explored because of their ability to improve drug delivery and therapeutic effectiveness. However, despite encouraging research outcomes, converting these nanoparticle systems into approved clinical products is still a difficult process. Several important parameters, including particle size, surface properties, stability, and interaction with biological systems, need proper optimization to achieve safe and effective results in humans. In many cases, positive preclinical findings do not always produce the same success during clinical studies. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as advanced technologies that can significantly improve the development of nanomedicine. These technologies help researchers analyze large amounts of experimental data, identify important relationships between nanoparticle properties and biological performance, and design optimized formulations more efficiently. AI-based computational models can also predict biodistribution, drug release patterns, toxicity, and therapeutic outcomes, helping reduce the time and cost involved in traditional trial-and-error research methods. Advanced experimental approaches, including high-throughput screening and automated liquid handling systems, generate large datasets that support AI-driven analysis and accelerate nanoparticle discovery. In addition, AI plays an important role in studying protein corona formation on nanoparticle surfaces, which greatly affects immune response, cellular uptake, circulation time, and overall therapeutic performance. Although AI has shown great potential in nanomedicine research, several challenges still limit its full clinical application. Problems such as lack of standardized datasets, limited reproducibility of computational models, and absence of specific regulatory guidelines for AI-integrated nanomedicine remain major concerns. Therefore, better data sharing systems, accurate in vivo validation, ethical considerations, and clear regulatory frameworks are essential for future advancements in this field. This review highlights the growing contribution of AI and ML in nanomedicine development, particularly in nanoparticle design, prediction of biological behavior, and optimization of drug delivery systems. It also discusses the present limitations and future opportunities associated with the integration of artificial intelligence into next-generation nanomedicine research.

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 Keywords

Artificial Intelligence (AI),Machine Learning (ML),Nanomedicine,Nanoparticles (NPs),Drug Delivery System (DDS),Lipid Nanoparticles (LNPs),PBPK Modeling,Protein Corona,Generative AI,Targeted Drug Delivery

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 Abstract

Rapid urbanization in Bhubaneswar has significantly increased the demand for affordable housing, particularly for Economically Weaker Sections (EWS). Under the Pradhan Mantri Awas Yojana-Urban (PMAY-U), the Beneficiary-Led Construction (BLC) component enables eligible households to construct their own houses with financial assistance. However, conventional construction practices under BLC often face challenges such as delays, rising material costs, labour shortages, and inconsistent quality. In this context, the study explores the feasibility and integration of Industrialized Building Systems (IBS) as an alternative construction approach for improving housing delivery. IBS involves the prefabrication of building components in controlled environments and their assembly on-site, which can enhance construction speed, quality control, and resource efficiency. The research is based on a combination of primary surveys, stakeholder interactions, and analysis of existing BLC housing practices in Bhubaneswar. The study evaluates IBS in comparison to conventional methods across key parameters such as cost, time, construction process, and overall performance. It also identifies practical challenges in adopting IBS, including technical adaptability, initial investment, and awareness among stakeholders. Overall, the study highlights that the adoption of IBS can contribute to faster, more standardized, and sustainable housing development, making it a viable approach for enhancing the effectiveness of the BLC component in Bhubaneswar.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Affordable Housing, Economically Weaker Sections (EWS), Pradhan Mantri Awas Yojana-Urban (PMAY-U), Beneficiary-Led Construction (BLC), Industrialized Building Systems (IBS), Precast Concrete Construction (PCC).

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Herbal medicine has gained significant attention in modern healthcare due to its natural origin, therapeutic potential, affordability, and minimal side effects. Medicinal plants contain various bioactive constituents such as flavonoids, alkaloids, tannins, terpenoids, and glycosides that exhibit pharmacological activities including antimicrobial, anti-inflammatory, antioxidant, analgesic, and wound healing properties. Wound healing is a complex physiological process involving hemostasis, inflammation, proliferation, and remodeling phases, requiring effective therapeutic approaches to accelerate tissue repair and prevent complications. Transdermal Drug Delivery Systems (TDDS) have emerged as an advanced and patient-friendly approach for controlled and sustained drug delivery through the skin. These systems improve bioavailability, reduce dosing frequency, avoid first-pass metabolism, and enhance patient compliance. Among various TDDS approaches, transdermal patches have become highly effective dosage forms due to their ability to provide prolonged drug release, painless administration, and targeted therapeutic action. Herbal-based transdermal patches combine the benefits of herbal medicines with modern drug delivery technology, offering a promising strategy for wound management and other therapeutic applications. Their enhanced safety profile, biocompatibility, and sustained drug release characteristics make them a valuable alternative to conventional dosage forms in pharmaceutical and clinical practice.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Herbal medicines, wound healing, transdermal drug delivery system, transdermal patches.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Leguminous crops are among the most important agricultural commodities worldwide because of their nutritional value, nitrogen-fixing ability, and contribution to sustainable agriculture. However, production of legumes is significantly constrained by several fungal diseases, among which Alternaria diseases have emerged as serious threats in diverse agro-climatic regions. Species of the genus Alternaria infect a wide range of legume crops including clusterbean, chickpea, soybean, mungbean, cowpea, lentil, pea, pigeonpea, and blackgram. These pathogens cause leaf spots, blights, pod infections, seed discoloration, and severe yield losses under favorable environmental conditions. The present review summarizes current knowledge on taxonomy, biology, symptomatology, epidemiology, pathogenic variability, host-pathogen interaction, toxin production, disease cycle, and integrated management of Alternaria diseases in legumes. Environmental factors such as temperature, humidity, rainfall, and crop canopy significantly influence disease development and spread. Modern molecular tools have improved understanding of pathogen diversity and disease diagnostics. Integrated disease management involving resistant cultivars, cultural practices, biological control agents, botanicals, fungicides, and advanced molecular approaches offers sustainable disease suppression. The review also highlights future research priorities including climate-resilient disease management, genomics-assisted resistance breeding, and precision disease forecasting systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Alternaria, Legumes, Epidemiology, Pathogenesis, Integrated disease management, Fungal diseases, Host-pathogen interaction, Biological control

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tic syndrome, encompassing both Tourette's Syndrome (TS) and Chronic Tic Disorders (CTDs), represents a group of neurodevelopmental conditions characterized by repetitive, involuntary motor and/or vocal movements. The neurochemical underpinnings of tic disorders involve dysregulation of dopaminergic and GABAergic pathways, glutamate signaling, and potential nutritional deficiencies -- particularly in the B-vitamin group. This project investigates the design, formulation, and comprehensive evaluation of an oral tablet combining two nutraceutical-therapeutic agents: L-Theanine (200 mg), a bioactive amino acid derived from green tea known to enhance GABAergic tone and modulate glutamate excitotoxicity; and Cyanocobalamin (Vitamin B12, 1000 mcg), an essential cobalamin involved in neuronal myelination and neurotransmitter synthesis.

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Using L-Theanine and Vitamin B12 as Active Pharmaceutical Ingredients

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 Abstract

In Ayurveda, Constipation, often referred to as Vibandha or Baddha Purisha, signifies an inhibition or blockage in the Purishavaha Srotas, i.e. Sangha, a term denoting the obstruction of channels responsible for the smooth movement and elimination of stool within the body. It is a frequently encountered issue in the paediatric population and significantly impacts the physical well-being, physiological functioning and overall quality of life of affected children.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Vibandha, Constipation, Purishavaha Srotas, Bowel, chikitsa

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Manual explosive ordnance disposal (EOD) presents extreme physical risks to field technicians. Traditional remote-controlled defusal assets minimize these hazards but are frequently restricted by the localized range boundaries and signal dropouts typical of radio-frequency architectures. This paper presents an Internet of Things (IoT) driven mobile robotic platform designed for unrestricted long-distance bomb reconnaissance and neutralization. Controlled via an ESP8266 NodeMCU microcontroller interfaced with the Blynk cloud framework, the system routes remote locomotion and manipulation instructions over standard IP networks. From an automotive engineering perspective, the platform features a highly maneuverable wheeled chassis driven by high-torque DC gear motors, an integrated multi-axis robotic arm, and a relay-isolated wire-cutting end-effector. For real-time hazard identification and situational awareness, the vehicle incorporates a front-mounted inductive proximity metal sensor tied to a local warning buzzer, alongside an independent ESP32-CAM module supplying live video telemetry. Operational trials indicate that the platform maintains reliable, low-latency control loops through structural obstructions, offering an adaptable, low-cost unmanned ground vehicle (UGV) solution for tactical safety operations.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Unmanned Ground Vehicle (UGV), Internet of Things (IoT), Bomb Defusal, ESP8266 NodeMCU, Chassis Dynamics, Cloud Telemetry.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Through the lenses of the maritime-infrastructure nexus and geoeconomic competition in the Indo-Pacific from 2020 to 2026, this article conducts a qualitative case study and policy analysis to examine the development of the geopolitical rivalry between China and India. The study uses the theoretical lens of "weaponized interdependence" to examine how commercial maritime infrastructure, supply chain corridors, and logistics tracking software have evolved into tools of state coercion and strategic leverage, going beyond conventional naval power comparisons. It analyzes India's asymmetric counter-strategy and breaks down China's "Strategic Strongpoints" throughout the Indian Ocean Region (IOR). According to the study, New Delhi uses macro-diplomatic frameworks such as the Quadrilateral Security Dialogue (Quad) in conjunction with state-backed private capital accumulation to create rival port hubs. The Indo-Pacific Partnership for Maritime Domain Awareness (IPMDA) is given particular attention, demonstrating how open-source intelligence (OSINT), AI-driven satellite monitoring, and data-sharing networks are essential defenses against maritime asymmetry. The study concludes by bridging these tangible realities with the politics of perception by examining how maritime maneuvers and satellite tracking data contribute to hostile domestic information ecosystems and "visual warfare." The study comes to the conclusion that the maritime-economic sector has emerged as the defining boundary of the Asian "Cold Peace," where the balance of power is determined by control over international trade and information flows.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Weaponized Interdependence, Geoeconomic Competition, India-China Rivalry, Strategic Strongpoints, Asymmetric Counter-Strategy, Maritime Domain Awareness, and Visual Warfare.

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Creative Commons Attribution 4.0 and The Open Definition