Search Results (349)

Artificial general intelligence (AGI) is often depicted as a transformative breakthrough, yet debates persist on whether current advancements truly represent general intelligence or remain limited to domain-specific applications. This study empirically maps AGI-related research across subject areas, geographies, and United Nations Sustainable Development Goals (SDGs) via machine learning-based analysis. The findings reveal that while the AGI discourse remains anchored in computing and engineering, it has diversified significantly into human-centered domains such as healthcare (SDG 3), education (SDG 4), clean energy (SDG 7), industrial innovation (SDG 9), and public governance (SDG 16). Geographically, research remains concentrated in the United States, China, and Europe, but emerging contributions from countries such as India, Pakistan, and Costa Rica suggest a gradual democratization of AGI exploration. Thematic expansion into legal systems, governance, and environmental sustainability points to AGI’s growing relevance for systemic societal challenges, even if true AGI remains aspirational. Funding patterns show strong private and public sector interest in general-purpose AI systems, whereas institutional collaborations are increasingly global and interdisciplinary. However, challenges persist in cross-sectoral data interoperability, infrastructure readiness, equitable funding distribution, and regulatory oversight. Addressing these issues requires anticipatory governance, international cooperation, and capacity-building strategies to ensure that the evolving AGI landscape aligns with inclusive, sustainable, and socially responsible futures. Full article

This study focuses on the exchange of mono- and divalent metal cations in FAU-type zeolite and their behavior in gas-phase CO₂ adsorption measurements and liquid-phase methylene blue (MB) adsorption in the absence of oxidizing agents under dark conditions. Firstly, zeolites exchanged with different cations were characterized by several techniques, such as XRD, SEM, XRF, XPS, and N₂ adsorption–desorption, to reveal the impact of the cations on the zeolite texture and structure. The adsorption studies revealed a positive effect of cation exchange on the adsorption capacity of the zeolite, particularly for silver-loaded FAU zeolite. In liquid-phase experiments, Ag-Y zeolite also demonstrated the highest MB removal, with a value of 79 mg/g. Kinetic studies highlighted that Ag-Y could reach the MB adsorption equilibrium within 1 h, with its highest rate of adsorption occurring during the first 5 min. In gas-phase adsorption studies, the highest CO₂ adsorption capacity was also achieved over Ag-Y, yielding 10.4 µmol/m² of CO₂ captured. Full article

Our temptation to personify machine intelligence is not unexpected. As a child we named our dolls and took our Teddy Bear to bed with us. Today we ask death bots to comfort us with post-mortem conversation. All the while we know this to be pretend. Yet we must ask: if Artificial General Intelligence (AGI) or even Artificial Super-Intelligence (ASI) become available, will our game of pretend continue? Or will intelligent robots actually become selves deserving of dignity that hitherto could be ascribed only to human persons? If government-imposed guardrails shut the door on development of AGI and ASI in order to preserve human safety and even dignity, we might never learn whether AGI or ASI could develop selfhood, personhood, virtue, or religious sensibilities. As we approach the future, can we live without knowing whether AGI or ASI would be capable of developing selfhood and commanding dignity? Full article

Acid gas injection (AGI), which primarily involves injecting hydrogen sulfide (H₂S) and carbon dioxide (CO₂), is recognized as a cost-efficient and environmentally sustainable method for controlling sour gas emissions in oil and gas operations. This review examines case studies of twelve AGI projects conducted in Canada, Oman, and Kazakhstan, focusing on reservoir selection, leakage potential assessment, and geological suitability evaluation. Globally, several million tonnes of acid gases have already been sequestered, with Canada being a key contributor. The study provides a critical analysis of geochemical modeling data, monitoring activities, and injection performance to assess long-term gas containment potential. It also explores AGI’s role in Enhanced Oil Recovery (EOR), noting that oil production can increase by up to 20% in carbonate rock formations. By integrating technical and regulatory insights, this review offers valuable guidance for implementing AGI in geologically similar regions worldwide. The findings presented here support global efforts to reduce CO₂ emissions, and provide practical direction for scaling-up acid gas storage in deep subsurface environments. Full article

Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations. Full article

The optimization of bioactive compound mixtures is critical for enhancing pharmacological efficacy. This study investigates, for the first time, the combined effects of eugenol, camphor, and terpineol, focusing on their half-maximal inhibitory concentrations (IC₅₀) across multiple biological responses related to diabetes management. Using a mixture design approach, the objective was to determine the optimal formulation that maximizes bioactivity and validate the findings experimentally. A simplex-centroid design was applied to evaluate the combined effects of eugenol, camphor, and terpineol on AAI _IC50, AGI _IC50, LIP _IC50, and ALR _IC50 responses. The desirability function was used to determine the ideal composition. The optimized formulation was experimentally validated using in vitro assays, and IC50 values were measured for each response using standard protocols. Results: The optimal formulation identified was 44% eugenol, 0.19% camphor, and 37% terpineol, yielding IC₅₀ values of 10.38 µg/mL (AAI), 62.22 µg/mL (AGI), 3.42 µg/mL (LIP), and 49.58 µg/mL (ALR). The desirability score (0.99) confirmed the effectiveness of the optimized blend. Experimental validation of the optimal mixture resulted in IC₅₀ values of 11.02 µg/mL (AAI), 60.85 µg/mL (AGI), 3.75 µg/mL (LIP), and 50.12 µg/mL (ALR), showing less than 10% deviation from predicted values, indicating high model accuracy. This study confirms the combined potential of eugenol, camphor, and terpineol, with eugenol and terpineol significantly enhancing bioactivity. The validated formulation demonstrates potential for pharmaceutical and cosmeceutical applications. Future research should explore mechanistic interactions, bioavailability, and in vivo efficacy to support the development of optimized natural compound-based therapies. Full article

In this study, AgI/Ag₃PO₄/WO₃ ternary composite photocatalysts with dual Z-scheme heterojunction were fabricated via the in situ loading of Ag₃PO₄ onto WO₃ followed by anion exchange. Compared to single photocatalysts and binary composites, the AgI/Ag₃PO₄/WO₃ composites exhibited enhanced photocatalytic activity in the photodegradation of chlortetracycline hydrochloride (CTC) under visible-light irradiation. Notably, the AAW-40 photocatalyst, which contained an AgI/Ag₃PO₄ molar ratio of 40%, degraded 75.7% of the CTC within 75 min. Moreover, AAW-40 demonstrated an excellent performance in the cyclic degradation of CTC over four cyclic degradation experiments. The separation and transfer kinetics of the AgI/Ag₃PO₄/WO₃ composite were investigated with photoluminescence spectroscopy, time-resolved photoluminescence spectroscopy, and electrochemical measurements. The improved photocatalytic performance was primarily due to the creation of a silver-bridged dual Z-scheme heterojunction, which facilitated the efficient separation of photoinduced electron–hole pairs, retained the strong reducing capability of electrons in AgI, and ensured the strongly oxidizing nature of the photoexcited holes in WO₃. The dual Z-scheme charge-transfer mechanism was further validated using in situ X-ray photoelectron spectroscopy. This study provides a foundation for developing innovative dual Z-scheme photocatalytic systems aimed at the efficient degradation of antibiotics in wastewater. Full article

Cyclic voltammetry (CV) is an accessible, readily available, non-expensive technique that can be used to search for the interaction of compounds with DNA and detect the strongest DNA-binding from a set of compounds, therefore allowing for the optimization of the number of cytotoxicity assays. Focusing on this electrochemical approach, the study of twenty-seven camphorimine silver complexes of six different families was performed aiming at detecting interactions with calf thymus DNA (CT-DNA). All of the complexes display at least two cathodic waves attributed respectively to the Ag(I)→Ag(0) (higher potential) and ligand based (lower potential) reductions. In the presence of CT-DNA, a negative shift in the potential of the Ag(I)→Ag(0) reduction was observed in all cases. Additional changes in the potential of the waves, attributed to the ligand-based reduction, were also observed. The formation of a light grey product adherent to the Pt electrode in the case of {Ag(OH)} and {Ag₂(µ-O)} complexes further corroborates the interaction of the complexes with CT-DNA detected by CV. The morphologic analysis of the light grey material was made by scanning electronic microscopy (SEM). The magnitude of the shift in the potential of the Ag(I)→Ag(0) reduction in the presence of CT-DNA differs among the families of the complexes. The complexes based on {Ag(NO₃)} exhibit higher potential shifts than those based on {Ag(OH)}, while the characteristics of the ligand (^AL-Y, ^BL-Y, ^CL-Z) and the imine substituents (Y,Z) fine-tune the potential shifts. The energy values calculated by docking corroborate the tendency in the magnitude of the interaction between the complexes and CT-DNA established by the reaction coefficient ratios (Q_[Ag-DNA]/Q_[Ag]). The molecular docking study extended the information regarding the type of interaction beyond the usual intercalation, groove binding, or electrostatic modes that are typically reported, allowing a finer understanding of the non-covalent interactions involved. The rationalization of the CV and cytotoxicity data for the Ag(I) camphorimine complexes support a direct relationship between the shifts in the potential and the cytotoxic activities of the complexes, aiding the decision on whether the cytotoxicity of a complex from a family is worthy of evaluation. Full article

Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD) and Parkinson’s disease (PD), are characterized by progressive neuronal dysfunction and loss and represent a significant global health challenge. Oxidative stress, neuroinflammation, and neurotransmitter dysregulation, particularly affecting acetylcholine (ACh) and monoamines, are key hallmarks of these conditions. The current therapeutic strategies targeting cholinergic and monoaminergic systems have some limitations, highlighting the need for novel approaches. Metallodrugs, especially ruthenium and platinum complexes, are gaining attention for their therapeutic use. Among metal complexes, gold(I) and silver(I) N-heterocyclic carbene (NHC) complexes exhibit several biological activities, but their application in NDDs, particularly as monoamine oxidase (MAO) inhibitors, remains largely unexplored. To advance the understanding of this field, we designed, synthesized, and evaluated the biological activity of a new series of Au(I) and Ag(I) complexes stabilized by NHC ligands and bearing a carboxylate salt of tert-butyloxycarbonyl (Boc)-N-protected proline as an anionic ligand. Through in silico and in vitro studies, we assessed their potential as acetylcholinesterase (AChE) and MAO inhibitors, as well as their antioxidant and anti-inflammatory properties, aiming to contribute to the development of potential novel therapeutic agents for NDD management. Full article

Next-generation recycling technologies must be urgently innovated to tackle huge volumes of spent batteries, photovoltaic panels or printed circuit boards (WPCBs). Current e-waste recycling industrial technology is dominated by traditional recycling technologies. Herein, ionic liquids (ILs), deep eutectic solvents (DESs) and promising oxidizing additives that can overcome some traditional recycling methods of metal ions from e-waste, used in our works from last year, are presented. The unique chemical environments of ILs and DESs, with the application of low-temperature extraction procedures, are important environmental aspects known as “Green Methods”. A closed-loop system for recycling zinc and manganese from the “black mass” (BM) of waste, Zn-MnO₂ batteries, is presented. The leaching process achieves a high efficiency and distribution ratio using the composition of two solvents (Cyanex 272 + diethyl phosphite (DPh)) for Zn(II) extraction. High extraction efficiency with 100% zinc and manganese recovery is also achieved using DESs (cholinum chloride/lactic acid, 1:2, DES 1, and cholinum chloride/malonic acid, 1:1, DES 2). New, greener recycling approaches to metal extraction from the BM of spent Li-ion batteries are presented with ILs ([N_8,8,8,1][Cl], (Aliquat 336), [P_6,6,6,14][Cl], [P_6,6,6,14][SCN] and [Benzet][TCM]) eight DESs, Cyanex 272 and D2EHPA. A high extraction efficiency of Li(I) (41–92 wt%) and Ni(II) (37–52 wt%) using (Cyanex 272 + DPh) is obtained. The recovery of Ni(II) and Cd(II) from the BM of spent Ni-Cd batteries is also demonstrated. The extraction efficiency of DES 1 and DES 2, contrary to ILs ([P_6,6,6,14][Cl] and [P_6,6,6,14][SCN]), is at the level of 30 wt% for Ni(II) and 100 wt% for Cd(II). In this mini-review, the option to use ILs, DESs and Cyanex 272 for the recovery of valuable metals from end-of-life WPCBs is presented. Next-generation recycling technologies, in contrast to the extraction of metals from acidic leachate preceded by thermal pre-treatment or from solid material only after thermal pre-treatment, have been developed with ILs and DESs using the ABS method, as well as Cyanex 272 (only after the thermal pre-treatment of WPCBs), with a process efficiency of 60–100 wt%. In this process, four new ILs are used: didecyldimethylammonium propionate, [N_10,10,1,1][C₂H₅COO], didecylmethylammonium hydrogen sulphate, [N_10,10,1,H][HSO₄], didecyldimethylammonium dihydrogen phosphate, [N_10,10,1,1][H₂PO₄], and tetrabutylphosphonium dihydrogen phosphate, [P_4,4,4,4][H₂PO₄]. The extraction of Cu(II), Ag(I) and other metals such as Al(III), Fe(II) and Zn(II) from solid WPCBs is demonstrated. Various additives are used during the extraction processes. The Analyst 800 atomic absorption spectrometer (FAAS) is used for the determination of metal content in the solid BM. The ICP-OES method is used for metal analysis. The obtained results describe the possible application of ILs and DESs as environmental media for upcycling spent electronic wastes. Full article

The progressive advancements in education due to the advent of transformative technologies has led to the emergence of customized/personalized learning systems that dynamically adapts to an individual learner’s preferences in real-time mode. The learning route and style of every learner is unique and their understanding varies with the complexity of core components. This paper presents a hybrid approach that integrates generative adversarial networks (GANs), feedback-driven personalization, explainable artificial intelligence (XAI) to enhance knowledge component (KC) prediction and to improve learner outcomes as well as to attain progress in learning. By using these technologies, this proposed system addresses the challenges, namely, adapting educational content to an individual’s requirements, creating high-quality content based on a learner’s profile, and implementing transparency in decision-making. The proposed framework starts with a powerful feedback mechanism to capture both explicit and implicit signals from learners, including performance parameters viz., time spent on tasks, and satisfaction ratings. By analysing these signals, the system vigorously adapts to each learner’s needs and preferences, ensuring personalized and efficient learning. This hybrid model dynamic knowledge component prediction system (DKPS) exhibits a 35% refinement in content relevance and learner engagement, compared to the conventional methods. Using generative adversarial networks (GANs) for content creation, the time required to produce high-quality learning materials is reduced by 40%. The proposed technique has further scope for enhancement by incorporating multimedia content, such as videos and concept-based infographics, to give learners a more extensive understanding of concepts. Full article

The contamination of natural waters with heavy metals is a global problem. Biosorption is an environmentally friendly and effective technology that offers advantages when metals are present in low concentrations. It also facilitates the recovery and conversion of metals, which are valuable resources. The removal capacity of Ag(I) and Zn(II) ions by Staphylococcus epidermidis CECT 4183 and the ability of its cell extract to synthesize Ag/AgCl and ZnO nanoparticles were investigated. Their biocidal capacity was evaluated. The factors involved were optimized and the mechanisms were studied. The optimal conditions for Ag(I) biosorption were pH 4.5 and a biomass dose of 0.8 g/L. For Zn(II), the biomass dose was 0.2 g/L and pH 4.2. A maximum biosorption capacity (Langmuir model) of 47.43 and 65.08 mg/g, respectively, was obtained. The cell extract promoted the synthesis of Ag/AgCl and ZnO nanoparticles with average sizes below 35 nm. The ZnO nanoparticles exhibited excellent inhibitory properties against planktonic cells of five microbial strains, with MIC values ranging from 62.5 to 250 µg/mL. Their response to biofilms remained between 70% and 100% inhibition at low concentrations (125 µg/mL). The studied bacteria show potential to remove heavy metals and promote the environmentally friendly synthesis of biocidal nanoparticles. Full article

The pursuit of Artificial General Intelligence (AGI) demands AI systems that not only perceive but also reason in a human-like manner. While symbolic systems pioneered early breakthroughs in logic-based reasoning, such as MYCIN and DENDRAL, they suffered from brittleness and poor scalability. Conversely, modern deep learning architectures have achieved remarkable success in perception tasks, yet continue to fall short in interpretable and structured reasoning. This dichotomy has motivated growing interest in Neural–Symbolic AI, a paradigm that integrates symbolic logic with neural computation to unify reasoning and learning. This survey provides a comprehensive and technically grounded overview of AI reasoning in the deep learning era, with a particular focus on Neural–Symbolic AI. Beyond a historical narrative, we introduce a formal definition of AI reasoning and propose a novel three-dimensional taxonomy that organizes reasoning paradigms by representation form, task structure, and application context. We then systematically review recent advances—including Differentiable Logic Programming, abductive learning, program induction, logic-aware Transformers, and LLM-based symbolic planning—highlighting their technical mechanisms, capabilities, and limitations. In contrast to prior surveys, this work bridges symbolic logic, neural computation, and emergent generative reasoning, offering a unified framework to understand and compare diverse approaches. We conclude by identifying key open challenges such as symbolic–continuous alignment, dynamic rule learning, and unified architectures, and we aim to provide a conceptual foundation for future developments in general-purpose reasoning systems. Full article

As LLMs demonstrate remarkable reasoning capabilities, LLM-powered agents are seen as key to achieving AGI (Artificial General Intelligence) and are widely applied in various complex real-world scenarios. Nevertheless, existing studies still suffer from missing steps, deviated task execution and incorrect tool selection. This paper proposes CoReaAgents, a collaboration and reasoning framework based on LLM-powered agents, comprising the Plan Agent (as a precise task planner), the Tool Agent (as a proficient tool user) and the Reflect Agent (as an objective task evaluator). These agents simulate the social division of labor and synergistic cooperation to enable each agent to perform different specialized capabilities in order to solve complex tasks together. Through the above mechanism, the CoReaAgents framework has the skills of prospective thinking and flexible execution. To verify the capability of the CoReaAgents framework, this paper conducts extensive experiments on different complex tasks such as tool learning, math reasoning and multi-hop QA. The results show that the CoReaAgents framework outperforms various comparative methods in both quantity and quality. Full article

In today’s high-stakes arenas—from healthcare to defense—algorithms are advancing at an unprecedented pace, yet they still lack a crucial element of human decision-making: an instinctive caution that helps prevent harm. Inspired by both the protective reflexes seen in military robotics and the human amygdala’s role in threat detection, we introduce a novel idea: an integrated module that acts as an internal “caution system”. This module does not experience emotion in the human sense; rather, it serves as an embedded safeguard that continuously assesses uncertainty and triggers protective measures whenever potential dangers arise. Our proposed framework combines several established techniques. It uses Bayesian methods to continuously estimate the likelihood of adverse outcomes, applies reinforcement learning strategies with penalties for choices that might lead to harmful results, and incorporates layers of human oversight to review decisions when needed. The result is a system that mirrors the prudence and measured judgment of experienced clinicians—hesitating and recalibrating its actions when the data are ambiguous, much like a doctor would rely on both intuition and expertise to prevent errors. We call on computer scientists, healthcare professionals, and policymakers to collaborate in refining and testing this approach. Through joint research, pilot projects, and robust regulatory guidelines, we aim to ensure that advanced computational systems can combine speed and precision with an inherent predisposition toward protecting human life. Ultimately, by embedding this cautionary module, the framework is expected to significantly reduce AI-induced risks and enhance patient safety and trust in medical AI systems. It seems inevitable for future superintelligent AI systems in medicine to possess emotion-like processes. Full article