International Journal of Integrative Studies (IJIS)
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Deciphering Post-Translational Modifications: Their Role in Cellular Signaling and Metabolic Pathways
Get PDFPost-translational modifications (PTMs) are another control mechanism which has been shown to be a major form of cellular regulation and an added layer of structural and functional diversity to proteins beyond the information that the cellular genome provides. PTMs are molecular switches that help to regulate the activity, stability, localization, and interactions of proteins by converting molecule chemical states with phosphorylation, ubiquitination, acetylation, glycosylation, and methylation. It is this complexity that allows cells to integrate environmental signals, assemble metabolic processes and dynamically control pathways needed to survive and adapt. The importance of PTMs in cellular signaling and metabolism is very evident in their role in DNA repair, apoptosis, immune response and energy metabolism. PTMs disruption has also been widely recognized as a leading cause of diseases, including cancer, diabetes and neurodegenerative conditions. However, recent developments in mass spectrometry, proteomics and bio informatics have significantly expanded our understanding of PTMs to the point that it is now possible to map both modification site and network on a large scale. A comprehensive description of the application of PTMs to cellular processes and metabolic pathways is provided in the paper. It talks about classical and novel phosphorylation and ubiquitination and SUMOylation and ADPribosylation. The crosstalk of PTMs and how they might be exploited to optimally tune biological processes by making decisions at multiple sites on the same protein, which may either interact synergistically or antagonistically, is discussed. It also talks about advances in high-throughput proteomics methodology and computational modeling that are used to decipher the complexity of PTM. The other more informative PTM network applications that are not directly related to pure biology also have therapeutic applications as they are a form of differential manipulation of the PTMs in human disease
Child Labor in Hazardous Environments: A Crime Against Education
Get PDFChild labour in deplorable conditions has remained one of the most burning international human rights and education issues. Although many international conventions and domestic laws forbid the use of children in hazardous jobs, millions of children across the globe continue to work in hazardous jobs, especially in the mining industries, agricultural sectors, construction and production. Such places subject children to unhealthy chemicals, inappropriate machinery, and harsh working conditions which pose a direct health risk, safety, and above all, their right to an education. In this paper, the author is going to discuss the continuation of child labor in dangerous conditions, its historical context, the socio-economic processes that perpetuate it, and how it is destroying the education sector. The paper, based on secondary data in international reports, academic literature, and international case studies, will argue the point that child labor is not merely an economic issue, but a crime against education because it traps people in poverty, illiteracy and inequality. The discussion shows how it is important that laws, social protection programs and community based interventions that focus on education are enforced with more stringency. The research finds that child labor can only be eradicated through a holistic approach in which governments, industries and civil society collaborate to see to it that all children are safeguarded, rehabilitated and access to quality education
Strategic Partnerships and Ecosystems for Scaling Agri IoT Startups
Get PDFAgri IoT startups face a paradox: the technology to sense, connect, and orchestrate farm operations exists, but fragmented value chains, low per farmer ARPU, and distribution frictions stall scale. This paper develops an ecosystem lens for scaling Agri IoT ventures through strategic partnerships across telecom operators, agri inputs, machinery OEMs, FPOs/Co ops, off takers, insurers, and public programs. We synthesize platform ecosystem theory and agri innovation literature to derive a partnership archetype matrix (distribution, data, risk sharing, finance, and policy enablement) and a three phase scale playbook (beachhead → network effects → multi sided services). Using a conceptual multiple case synthesis and secondary evidence from emerging markets, we outline governance choices (open vs. curated platforms), data rights and interoperability (LoRaWAN/NB IoT, OGC SensorThings), and unit economics levers (bundling, embedded finance, outcome based pricing). Results indicate that partnering with distribution dense incumbents (input retailers, telcos) reduces CAC by 35–60% compared with direct sales, while risk sharing with insurers/off takers improves adoption of decision support by aligning incentives. We discuss policy rails (digital public infrastructure, e KYC, satellite data) that lower transaction costs. The paper contributes a practical framework—PARTNER—for diagnosing ecosystem gaps and sequencing alliances to cross the scale threshold in smallholder dominated markets
Exploring the Role of Artificial Intelligence in Drug Discovery and Development
Get PDFThe pharmaceutical research industry has been revolutionized by the use of Artificial Intelligence (AI) at a very rapid rate since it accelerates the development of drugs, simplifies the lead identification process, and reduces the cost of development. Traditional drug discovery procedure is tedious, costly, and lacks high clinical trial success rates. AI technologies, i.e., machine learning (ML), deep learning (DL), natural language processing (NLP), and predictive modeling have become potent tools that are meant to defeat these limitations. Some of the applications of AI include target identification, prediction of molecular properties, virtual screening, de novo drug design, and optimization of clinical trials. The paper discusses the current applications, technology, and benefits of AI in various stages of drug discovery and development, and points out some of the successful cases of AI utilization over the last few years. It looks at the problems of data quality, model interpretability, the ethical issues, and regulations, as well. We will see why AI will transform personalized and precision medicine through the analysis of the recent progress, such as the use of AI in designing molecules through generative AI and in compounds optimization through reinforcement learning. The article recaps the conclusions that pharmaceutical research and development can be revolutionized by AI using the help of human knowledge and vast data infrastructure that will enable them to create drugs significantly quicker, safer, and cheaper
Leveraging Artificial Intelligence and Machine Learning Techniques Improve Performance of Electrical Systems and Smart Grids
Get PDFThe integration of AI and ML in electrical power systems and smart grids has the ability to greatly enhance their efficiency, reliability, along with sustainability. With the increasing complexity of modern power grids as well as the growing reliance on RES, AI and ML provide advanced solutions to optimize operations, enhance grid stability, and address challenges such as intermittent energy generation, energy storage, and fault detection. This research examines the application of AI and ML, such as supervised learning, deep learning, reinforcement learning, along with anomaly detection, to key areas of power systems, including LF, fault detection, PM, and grid optimization. The use of AI for predictive maintenance, load prediction, as well as real-time optimization of power flow is particularly beneficial for ensuring the efficient integration of renewable energy sources while maintaining system stability. Moreover, these technologies enable the development of self-healing grids that can detect along with respond to faults autonomously, reducing downtime as well as enhancing the resilience of the grid. This paper presents a comprehensive analysis of recent advancements in AI and ML applications within electrical power systems, highlighting case studies and performance evaluations to demonstrate their impact on operational performance and cost-effectiveness. The findings suggest that the adoption of AI and ML can significantly reduce energy losses, improve fault detection accuracy, and increase the overall efficiency of power distribution. As power grids evolve towards more decentralized and renewable-driven systems, AI and ML will be integral to their futuresuccess. The research concludes by exploring the challenges and opportunities in scaling these technologies to address the growing demands of modern energy systems
5G and Beyond: AI-Optimised Network Slicing for Future Wireless Networks
Get PDFImplementation of 5G network is a mammoth event in the history of wireless communication. Nonetheless, with the industry, smart cities, IoT ecosystems expanding it can no longer be considered possible to run the network in a traditional way which is naturally fixed. Network slicing is a critical technology that may empower operators to outsource several virtual as well as separate logical networks utilizing shared infrastructure. The Artificial Intelligence (AI) goes a notch higher to dynamically, proactively, and resource-effectively optimize slices to address the needs of various service-level services including ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). In the following next-generation 5G network, AI-based network slicing is reviewed in this paper. It discusses the potential benefits of using machine learning, deep reinforcement learning, and federated learning to automate slice lifecycle management, predict traffic patterns, and allocate resources in a more efficient way. Recent reports and case studies have shown that optimization of AI-based systems can lower the latency, improve the throughput, and also decrease the energy consumption compared to the rule-based systems. Other challenges witnessed during the research are data privacy, understanding of AI designs and complexity when integrating with the old system. In addition, it focuses on standardization and regulatory plans required to implement the scaling implementation. It will be crucial in 6G networks in future to achieve intelligent slicing with diverse environments, satellite-ground relationship and immersive communications like AR/VR, holographic communications and robotics. AI-optimized network slicing will be developed to form the basis of next-generation wireless networks, balancing network virtualization and AI intelligence to enable network slices with resilience, scalability, and flexibility to accommodate next-generation digital ecosystems
Water Security in Urban Areas: Integrating Smart Monitoring Systems
Get PDFUrban water security hinges on the reliable provision of safe, affordable water and the protection of people and infrastructure from contamination, losses, scarcity, and flood-related disruptions. Rapid urbanization, climate variability, aging assets, and growing cyber–physical risks expose gaps in traditional supervisory control and data acquisition (SCADA) regimes. Smart monitoring systems—continuous sensing of hydraulic and water quality parameters, edge-to-cloud analytics, and decision automation—offer near real time situational awareness to reduce non revenue water, mitigate contamination events, and optimize operations. This paper develops an integration blueprint that combines district metered areas (DMAs), multi parameter sensors (pressure, flow, acoustic leak, residual chlorine, turbidity, conductivity), remote sensing, and city platforms via interoperable standards (e.g., OGC Sensor Things) and low power wide area networks (LPWANs). We present a methodology for pilot design, data quality controls, anomaly detection, and cyber security hardening (IEC 62443/NIST 800 82), followed by a unit economics and impact framework (KPIs: NRW, response time, water quality compliance, energy per m³). A synthesis of documented deployments suggests smart monitoring can accelerate detection, reduce losses, and improve resilience when paired with governance, workforce, and data rights measures. The paper concludes with a roadmap for city utilities to scale from pilots to platform level capabilities while balancing openness, security, and affordability
Bridging the Digital Divide: Strategic Roadmaps for MSMEs in Emerging Economies — an Experimental Study
Get PDFThe digital divide has remained one of the most critical competitiveness challenges against Micro, Small, and Medium Enterprises (MSMEs) in the developing economies. It is an experimental research study which identifies the utility of the existence of structured digital adoption roadmaps in order to increase the productivity, the maturity of digital operations and the market coverage of MSMEs. The sample size of 120 MSMEs in three sectors was used in a quasi experimental study that spanned 12 months. Custom-fit digital roadmap, training materials, and exposure to the digital platform were offered to the intervention group as opposed to the business-as-usual of the control group. The results show that increased productivity (by 32 percent), 40 percent increase in online market activities and significant change in digital capability scores was also a part of the consequences of the intervention. The study demonstrates that strategic and organized digital interventions have the potential to close the resource-constrained environments, making MSMEs more competitive
Phytoremediation Techniques for Heavy Metal Contaminated Soils: Advances and Challenges
Get PDFSoil contamination with heavy metals, such as lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), presents severe ecological and health risks due to their persistence and bioaccumulation in the food chain. These contaminants can cause irreversible damage, including neurotoxic effects, renal injury, and cancer. Phytoremediation, an eco-friendly bioremediation technique, utilizes plants to uptake, immobilize, or detoxify heavy metals from contaminated soils. Methods such as phytoextraction, phytostabilization, and phytodegradation have shown potential in mitigating heavy metal pollution. This process offers a sustainable and efficient alternative to traditional remediation techniques like excavation and chemical treatments, providing environmental benefits and improving public health
Blockchain in Supply Chains: Managing Transparency, Traceability and Efficiency in the Global Market
Get PDFGlobalized supply chains are strained by fragmented data, multi-tier opacity, counterfeit risks, and costly disputes. Blockchain—a shared, append-only ledger—has been proposed to enhance transparency, traceability, and operational efficiency, yet real-world adoption reveals both breakthroughs and bottlenecks. This paper develops a deploymentminded view that integrates GS1 EPCIS/CBV standards for interoperable event data, permissioned ledgers for governance, and privacy-preserving proofs (zero-knowledge) to reconcile transparency with business confidentiality. We synthesize evidence from systematic reviews and flagship pilots (e.g., Walmart–IBM Food Trust) and contrast them with lessons from initiatives that wound down (e.g., TradeLens), extracting adoption patterns, KPI impacts, and failure modes. We then describe a reference methodology—data acquisition via EPCIS events, Fabric-based channels, and role-based access—plus an evaluation rubric for trace time, recall precision, dispute cycle time, and data-reconciliation costs. Results from literature-anchored benchmarks indicate orders-of-magnitude traceability lead-time (TLT) reductions (days → seconds) and measurable reductions in manual reconciliation, with gains contingent on standards compliance and high-quality “oracle” data. Finally, we map future directions—zk-proof rollups, interoperable digital product passports, and policy-aligned sustainability metrics—alongside candid limitations around ecosystem incentives, privacy, scalability, and data veracity. We conclude that blockchain can shift chains from reactive to verifiable and auditable networks when combined with data standards, sound governance, and selective privacy technologies rather than “full transparency” alone