Guru Nanak Institute of Technology

Whipped creams are oil-in-water emulsions, generally produced from bovine milk. However, the increasing demand for plant-based and lactose-free alternatives has driven researchers into dairy-free options. To produce lactose-free, nutritionally enriched whipped creams, this study formulated and analyzed whipped creams using lactose-free plant-based milk, viz., oat milk and coconut milk. Xanthan gum was used as stabilizer and glycerol monostearate as emulsifier, with sucrose and citric acid for sweetness and acidity adjustment. The products were further fortified with orange peel powder (OPP), obtained from the flavedo of orange peel waste. In the OPP-unfortified control samples (bovine milk/oat milk/coconut milk-based), the polyphenol and β-carotene content ranged from 0.2 to 0.32 g GAE/100 g and 0.03 to 0.83 mg β-carotene equivalent/100 g, respectively. OPP fortification significantly boosted polyphenol (up to 450

The safe human-level decision-making for expressing the autonomous vehicles and the estimation of the eco vehicles has been proposed for the motion control for the driving behavior. For efficient decision-making, the sensor-based tracking of the autonomous machine for blockchain is to be done. The sensor-based history recording management has to propose, and storing the vehicle’s traveling history must be managed. For security reasons, the blockchain is done. Self-determination theory and energy-efficient mixed-precision neural networks are used in autonomous vehicles’ decision-making, and this technique is used in making moral decisions. The self-determination theory is used in creating the vehicles traveling steps using the innovative signal delivery system of the autonomous vehicles. The energy-efficient mixed-precision neural networks are used in managing the problem that travels the signal to the vehicles using the mixed–precision neural networks. The vehicle network has been made more efficient for storing the data of the mixed precision value and its neural network from autonomous vehicles. Here 80% of the precision value is raised compared to the previous days. In previous days, 20% of the precision has been calculated in autonomous vehicles. Comparing this, 60% of the precision value has been raised in traveling history. According to these variations, 40%–50% of autonomous vehicles’ data transmission that delivers the neural network has been proposed. By improving autonomous vehicles, the efficiency of mixed precision neural networks is the decision-making for efficient precision.

The control of insecurity, transparency, and real-time tracking of assets are the urgent challenges of the construction supply chain because of the complex multi-stakeholder structure and the emergence of quantum computing threats. This paper applies a four-phase systematic approach to developing a Blockchain-based Construction Supply Chain (BCSC) framework, which includes: (1) architectural design of Hyperledger Fabric private blockchain with IoT-enabled real-time tracking, (2) smart contracts to process automated procurement, inventory, and payment, (3) quantum-resilient cryptography based on lattice-based encryption, approachable post-quantum secure digital signatures and (4) lightweight Federated Learning models to predictive analytics. The framework was tested with comparative performance analysis of the conventional blockchain solutions and confirmed by the real-world application of the same at SIL, a multinational construction company. The experiment outcomes prove significant improvements: security resiliency is 50% better, transactions latency decreases by 40% and traceability efficiency increases by 35% as compared to traditional blockchain-based supply chain solutions.

The centralized architectures of global vaccine supply chains pose severe security, latency, and operational efficiency challenges to new threats of quantum computing. The article introduces the initial combined edge-enabled quantum-safe real-time optimization of vaccine supply chain based on multi-access edge computing (MEC), post-quantum cryptography, lightweight machine learning, and blockchain consensus algorithms. The framework uses autonomous decision-making distributed edge nodes, and uses novel algorithms such as hierarchical attention network to resource allocation (HAN-RA), quantum-inspired route optimization (QIRO) and adaptive multi-agent reinforcement learning (AMARL). CRYSTALS-Kyber, CRYSTALS-Dilithium and SPHINCS+ are post-quantum cryptographic protocols that are resistant to both classical and quantum adversaries. The improvements in performance have been verified experimentally: 68.2 percentage latency reduction (245 to 78 ms), 188.2 percentage throughput increase (850 to 2,450 throughput (TPS)), 19.5 percentage increase in security score (82 to 98/100), and any number of nodes can be scaled linearly to 10,000 nodes. The system has a 97.2% temperature breach detection, 94.8% high-demand prediction and 99.97% uptime during nonstop 72-h operations. This quantum-resistant architecture operates to fill key gaps in existing supply chain models, offering a scalable, secure and efficient system to support mission-critical healthcare logistics and developing theoretical bases of next-generation post-quantum distributed computing systems.

This paper presents empirical information regarding the transformational aspect of Artificial Intelligence (AI) in supporting sustainable business ecosystems based on digital innovation and integration in the emerging economies with the circular economy. Based on a mixed-method design, 220 organizations in manufacturing, energy and technology sectors in India, Brazil, Vietnam, and South Africa served as the source of data. The assessment of the research was performed through quantitative analysis employing Structural Equation Modeling (SEM) and qualitative data collected with the help of expert interviews and case analysis. The significant results also indicate that the mature AI governance produces a significant improvement in the scalability of the circular economy initiatives ($\beta=0.72, \mathrm{p}<.001$). The quality of regulation and digital infrastructure was identified as intermediaries that mitigated the barriers to adoption ($\boldsymbol{\beta}=0.24,\mathbf{p}<.001$) and projects that made a net-positive impact on the environment were found to be more viable in the long run and supported by the stakeholders ($t=8.27, p<.001$). Adaptive resilience ($b=0.68, p<.001$) was greatly associated with structured human-AI collaboration, and the data transparency was found to be positively related with trust and fairness in value distribution ($\mathrm{r}=0.66-0.71, \mathrm{p}<.01$). The paper finds that digital transformation is achievable in sustainable manner, but requires not only technological preparedness, but also ethical regulation, collaborative models and institutional assistance. The study provides a confirmed, generalizable construct to policymakers, industry executives, and sustainability planners who are in need of putting into practice AI-based AI-led models of green expansion that will balance growth and innovation, stability, and eco-friendliness.