1. Job Load Balancing of Edge Computing Devices by Trained Mathematical Models
Authors- Saurabh Jain, Mahesh kr. Sharma
Abstract- Edge computing reduces latency by bringing computation closer to end devices, but the growing scale and heterogeneity of edge networks make resource management increas-ingly complex. Load balancing is essential for efficient resource use and low response times, yet static approaches struggle in dynamic environments. In this paper, a novel load balancing model is proposed in which task sequences are first generated using a trained mathematical model, and each generated sequence is further optimized using genetic algorithms. Use of genetic algorithm has increases the efficiency as dynamic situa-tion is manage by the algorithm. Experiments are conducted across various environ-ments, and the results demonstrate that the artificial immune-based model outperforms the group search algorithm in terms of overall performance.
2. A Review of Recent Advances in Cardiothoracic Cancer Immunotherapy
Authors- John Ginni, David Raymond
Abstract- Cardiothoracic malignancies such as lung and esophageal cancer are among the most dif-ficult-to-treat malignancies, being highly lethal with aggressive clinical behaviors. Im-munotherapy has become the game-changing method, capitalizing on the natural immune machinery for augmenting the anti-tumor response. Along with considerable successes in the clinical setting, drug resistance, selection of patients through biomarkers, and han-dling toxicities continue to be principal stumbling blocks to optimal therapeutic manage-ment. This review offers an extensive overview of recent developments in immunother-apy of cardiothoracic malignancies, such as immune checkpoint inhibitors (ICIs), cellu-lar therapy, cancer vaccines, and viral-based immunotherapies. We also discuss novel biomarkers, artificial intelligence (AI)-based predictive models, and combination ap-proaches to prevent resistance and maximize efficacy. A comprehensive review of peer-reviewed clinical trials, real-world evidence, and translational studies was performed with a focus on new immunotherapeutic strategies and their clinical significance. Special attention was given to biomarker identification, AI-based treatment choice, and novel combination regimens. The combination of biomarker-guided immunotherapy, predictive modeling with AI, and multimodal treatment modalities has dramatically enhanced pa-tient stratification and rates of therapeutic response. Although PD-1/PD-L1 and CTLA-4 inhibitors continue to be the bedrock of immunotherapy, new approaches like T-cell en-gineering, oncolytic viruses, and targeted cancer vaccines are demonstrating promising activity in preclinical and clinical environments. In addition, combination treatments, such as ICIs with chemotherapy, targeted therapy, and radiation, have shown increased efficacy in overcoming resistance. Nonetheless, issues like immune-related toxicities, treatment availability, and cost remain.
3. Prediction of Increment in Recovery through CO2 Injection Using CMG
Authors- Abhijith Puthumana
Abstract- Cardiothoracic malignancies such as lung and esophageal cancer are among the most dif-ficult-to-treat malignancies, being highly lethal with aggressive clinical behaviors. Im-munotherapy has become the game-changing method, capitalizing on the natural immune machinery for augmenting the anti-tumor response. Along with considerable successes in the clinical setting, drug resistance, selection of patients through biomarkers, and han-dling toxicities continue to be principal stumbling blocks to optimal therapeutic manage-ment. This review offers an extensive overview of recent developments in immunother-apy of cardiothoracic malignancies, such as immune checkpoint inhibitors (ICIs), cellu-lar therapy, cancer vaccines, and viral-based immunotherapies. We also discuss novel biomarkers, artificial intelligence (AI)-based predictive models, and combination ap-proaches to prevent resistance and maximize efficacy. A comprehensive review of peer-reviewed clinical trials, real-world evidence, and translational studies was performed with a focus on new immunotherapeutic strategies and their clinical significance. Special attention was given to biomarker identification, AI-based treatment choice, and novel combination regimens. The combination of biomarker-guided immunotherapy, predictive modeling with AI, and multimodal treatment modalities has dramatically enhanced pa-tient stratification and rates of therapeutic response. Although PD-1/PD-L1 and CTLA-4 inhibitors continue to be the bedrock of immunotherapy, new approaches like T-cell en-gineering, oncolytic viruses, and targeted cancer vaccines are demonstrating promising activity in preclinical and clinical environments. In addition, combination treatments, such as ICIs with chemotherapy, targeted therapy, and radiation, have shown increased efficacy in overcoming resistance. Nonetheless, issues like immune-related toxicities, treatment availability, and cost remain.
4. An Observability-Driven Study of A Two-Tier Cache with Lazy Eviction
Authors- Rahul Pokala
Abstract- Caching systems try to manage and balance memory usage and speed by relying on eviction and promotion policies; however, most web developers find it obscure how cached data moves behind the scenes. This paper presents the design and evaluation of a twotier cache system consisting of a HOT in-memory layer and a distributed COLD layer. In this system, old data is eliminated by TTL-based lazy-eviction, and data is immediately promoted from the cold-tier to the hot-tier upon access. To understand this in its entirety, an observability dashboard was developed to showcase hit patterns, TTL states, and promotion events in real time. Through controlled workload experiments, we observed how lazy eviction and promotion rules affect cache hit rates and data churn. The results demonstrate visualized cache behavior in real time, providing a detailed understanding of how eviction and promotion affect cache performance.
5. AI-Enabled Digital Twins for Low Carbon Logistics in Emerging Mar-kets: A Human-Centric Framework for Cold-Chain Energy Efficiency and CBAM Ready Supply Chains in India
Authors- Syed Eirfan Atthar
Abstract- Cold-chain logistics play a critical role in ensuring food security and pharmaceutical safety in emerging markets; however, they remain among the most energy-intensive and environmen-tally sensitive segments of modern supply chains. In countries such as India, rapid expansion of cold-chain infrastructure has been accompanied by persistent challenges related to refriger-ation energy inefficiency, product spoilage, fragmented decision-making, and limited carbon transparency. At the same time, evolving global sustainability expectations and carbon-linked trade mechanisms are increasing the need for reliable, auditable emission information across logistics operations. This paper proposes a human-centric, AI-enabled digital twin framework aimed at improving energy efficiency, reducing carbon intensity, and enhancing sustainability transparency in food and pharmaceutical cold-chain logistics within emerging markets. Adopting a conceptual design-science approach, the study integrates insights from cold-chain engineering, logistics management, artificial intelligence, and sustainability governance. A layered digital twin architecture is developed that combines real-time operational data, energy-aware system modelling, AI-driven optimization, and human-in-the-loop decision support. Il-lustrative energy and emission calculations are presented to demonstrate potential reductions in refrigeration energy demand, spoilage risk, and operational uncertainty. The proposed framework emphasizes human accountability and explainable AI, ensuring that technological intelligence augments rather than replaces managerial judgment. By linking operational opti-mization with sustainability reporting and future carbon compliance readiness, the framework offers practical value for logistics managers, exporters, and policymakers. The study contrib-utes a structured pathway for transforming cold-chain logistics into an energy-efficient, transparent, and future-ready system capable of supporting sustainable growth in emerging markets.
