Artificial General Intelligence (AGI) seeks to create machines with broad, human-like cognition. However, rapid advances in narrow AI make it clear that general intelligence remains unresolved. This systematic review synthesises AGI research in five dimensions: definitions, enabling technologies, envisioned applications, evaluation methodologies, and open challenges. We performed a systematic search of major academic databases and search engines up to January 2026. After deduplication, two reviewers independently screened 927 records and included 393 studies that met the predefined criteria. We extracted data to address five research questions for each dimension and conducted a qualitative synthesis, as the evidence base did not support a meta-analysis. Across the literature, AGI is primarily characterised by broad adaptability and cross-domain generalisation. The main approaches include symbolic knowledge-based methods, machine learning, cognitively inspired architectures, hybrid neuro-symbolic systems, and brain-inspired or theoretical models Raman et al. (2025) [302]; each contributes partial capabilities, but exhibits limitations in isolation. The proposed application areas include healthcare, education, finance, transportation, smart cities, defence, scientific research, and more. Evaluation practices span multidimensional scales, cognitive test batteries, and safety-oriented benchmarks for alignment and autonomy. However, many benchmarks are saturated rapidly and do not fully capture real-world generality. Persistent challenges include the absence of a unified empirical theory of intelligence; the integration and scalability of heterogeneous methods; symbol grounding and embodiment; value alignment and control of highly capable agents; and the development of rigorous yet comprehensive evaluation protocols. In general, AGI research remains fragmented, with no approach that demonstrates the full spectrum of general cognitive competence. We operationalise “integration” as a system-level property in which learning, reasoning, memory, and social-ethical constraints are coupled through shared representations and closed-loop control so that each component can shape the others during task execution. We also describe two integrative candidate architectures proposed in the literature: (i) a Self-Evolving Binary-Symbolic AGI framework and (ii) the Cohomological Active Inference Architecture (CAIA). Whether a proposed architecture achieves this integration can be measured empirically using multidimensional AGI benchmarks and safety or alignment evaluations discussed in AGI assessment methodologies.
In order to coordinate EVs along with renewable energy, it is necessary to have accurate forecasting, adaptive control, and a secure energy exchange. The hybrid framework that integrates Transformer forecasting with multi-agent reinforcement learning (MARL) suggested in this paper appears to highly suitable for the intended application. In fact, the Transformer encoder is the one responsible for the accurate predictions of EV load and renewable generation, while MARL policies give the required decentralization and dynamic coordination. Blockchain acts as a safety net for the transactions and a sign of trustworthiness for the prosumers, with IoT-level compression playing the role of latency eliminator in densely packed EV networks. Testing results show that predictive reliability is 96.9%, balancing is 32.7%, efficiency is 26.4%, and latency is 24 ms. Based on the comparison with ML baselines, the framework is 6.8% more accurate, 7.5% more balancing is achieved, 5.9% of the cost optimization is improved, and therefore, the EV–renewable integration is not only scalable but also resilient.
Rice is one of the main cereal grains consumed on a regular basis in underdeveloped and developing nations across the globe. As a water-intensive crop, rice is particularly susceptible to drought stress, which adversely affects global food security. Global climate change has significantly increased the intensity and frequency of droughts. Drought stress strongly influences several physiological, morphological, biochemical, and agronomic parameters, directly affecting crop output. Plants use a variety of defence mechanisms, such as ROS-scavenging mechanisms, synthesis of various osmolytes, secondary metabolites, and phytohormones, to adapt to stressful environments. The candidate genes and metabolic pathways crucial to drought resistance in rice are getting revealed by recent advancements in molecular biology tools combined with enhanced breeding methodologies. In order to develop rice cultivars with increased drought tolerance, it will be extremely helpful to understand the ‘omics’ responses in rice during drought stress, particularly of tolerant genotypes. Moreover, molecular breeding techniques, enhanced agronomic management, genome editing, and genetic engineering may make substantial contributions in this regard. The integration of multi-omics methods, including genomics, transcriptomics, proteomics, metabolomics, and ionomics, offers a comprehensive understanding of cellular dynamics in plants under water deprivation. Therefore, it is imperative to utilize omics data from many molecular pathways to develop drought-resistant rice varieties for changing climatic circumstances. This article provides a comprehensive review of research on morpho-physiological, biochemical, molecular, and omics approaches, along with their applications in developing drought-tolerant rice varieties to address global food security concerns.
Alzheimer's disease (AD) is a multidimensional neurodegenerative disease leading to progressive loss of cognitive function and a growing health burden on the world population. Although decades of research have been conducted on this disease, current therapies have limited clinical value, mainly because researchers have not fully incorporated the intricate molecular pathways underlying its development and progression. This review summarizes current knowledge of AD pathophysiology, including amyloid beta (Aβ) dysregulation, tau hyperphosphorylation, neuroinflammation, mitochondrial dysfunction, oxidative stress, and synaptic breakdown. Although the amyloid- and tau-centered paradigms remain prevailing in the field, we note newer molecular targets, including secretase modulators, inflammatory signaling hubs, mitotic and autophagic regulators, epigenetics, and synaptogenesis pathways. We prioritize mechanistic, structural, cellular, and systems levels to facilitate a rational development of therapeutic understanding. The latest trends in medicinal chemistry and computational drug design, multi-target- directed ligands and hybrid scaffolds, as well as in silico ADMET optimization, are also discussed. Furthermore, we discuss the therapeutic aspects of bioinspired analogues of natural products. Lastly, we discuss the ongoing clinical development initiatives, opportunities, and major translational issues. In general, we highlight the need for integrative, mechanism-oriented, and personalized treatment approaches to propel the next generation of AD therapies.
The significance of sustainability necessitates organizations to implement technological solutions in their business practices. Integrating Industry 4.0 with current business practices enables the organization to enhance forward logistics sustainability. However, its integration is complex and unpredictable in reverse logistics due to the nature of reverse logistics activities. This research aimed to investigate the existing literature to understand the utilization of Industry 4.0 in reverse logistics to achieve triple-bottom-line sustainability. By using two strings of keywords, 134 articles from the Scopus database were finally considered for the full-text analysis to perform a systematic literature review with bibliometric and content analysis. The study's outcome denotes an increasing trend of publications on this domain over the years, and India appeared as the primary contributor, both in terms of volume of publications and overall citations. The analysis identified Bag Surajit as the most prolific author with five documents between 2020 and 2022, and 'circular economy' is the most occurring author keyword. The study further underscores the widespread applications of Industry 4.0 for sustainable reverse logistics, providing academicians with a knowledge base at the intersection of Industry 4.0, reverse logistics, and sustainability. This research helps managers to transform reverse logistics activities with sustainability objectives, and it supports policymakers in formulating strategies for the adoption of these technologies for better reverse logistics operations. The study is intriguing as it provides a comprehensive examination of Industry 4.0 in reverse logistics for sustainability, while also uncovering the potential synergies of its integrated application. The study concluded with limitations and suggested the future scope of this research domain.
