Search Results (109)

The contemporary American university largely operates as an agent of domestication, tasked more with enforcing the social and economic order than with expanding the horizons of possibility. The dawn of the Anthropocene, however, demands that we reconceive of the humanities not as self-sufficient, hierarchical, or divided away from other modes of seeking knowledge but as core to what human being and responsibility ought to mean in the more-than-human world. The present essay makes a case for reworking—and rethinking—the American university along the lines of Mark C. Taylor’s prompt to reconceive of the academy as a multidisciplinary forum for the “comparative analysis of common problems”. I suggest that religious teachings—and religious traditions themselves—can offer models for the intertwining of the humanities (literature, poetry, philosophy, the expressive and applied arts), the social sciences (the study of governance, political thought, the study and formulation of law), and the natural sciences as well as mathematics and engineering. Further, I argue that when faced with radical and unprecedented changes in technological, social, economic, and environmental structures, we must, I believe, engage with these traditional texts in order to enrich and critique the liberal mindset that has neither the values nor the vocabulary to deal with the climate crisis. We must begin to sow new and expansive ways of thinking, and I am calling this work the “rewilding” of our universities. Parallel to the three Cs of rewilding as a conservation paradigm, I suggest the following three core principles for the rewilding of higher education: creativity, curriculum, and collaboration. Though I focus on the interface of religion, ecology, and the study of the environmental, social, and moral challenges of climate change, I suggest that these categories of activity should impact all domains of inquiry to which a university is home. Full article

The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as “caring”, necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential of humanoid robots to autonomously replicate compassionate care. The study employs computational simulations using mathematical and agent-based modeling to analyze human–robot interactions (HRIs) surpassing Tetsuya Tanioka’s TRETON. It incorporates stochastic elements (through neuromorphic computing) and quantum-inspired concepts (through the lens of Martha Rogers’ theory), running simulations over 100 iterations to analyze complex behaviors. Multisensory simulations (visual and audio) demonstrate the significance of “dynamic communication”, (relational) “entanglement”, and (healthcare system and robot’s function) “superpositioning” in HRIs. Quantum and neuromorphic computing may enable humanoid robots to empathetically respond to human emotions, based on Jean Watson’s ten caritas processes for creating transpersonal states. Autonomous AI humanoid robots will redefine the norms of “caring”. Establishing “pluralistic agreements” through open discussions among stakeholders worldwide is necessary to align innovations with the values of compassionate care within a “posthumanist” framework, where the compassionate care provided by Level 4 robots meets human expectations. Achieving compassionate care with autonomous AI humanoid robots involves translating nursing, communication, computer science, and engineering concepts into robotic care representations while considering ethical discourses through collaborative efforts. Nurses should lead the design and implementation of AI and robots guided by “technological knowing” in Rozzano Locsin’s TCCN theory. Full article

The concept of the centaur, symbolizing the fusion of human and machine intelligence, has intrigued visionaries for decades. Recent advancements in artificial intelligence have made this concept not only realizable but also actionable. This synergistic partnership between natural and artificial intelligence promises superior outcomes by leveraging the strengths of both entities. Tracing its origins back to early pioneers of human–computer interaction in the 1960s, such as J.C.R. Licklider and Douglas Engelbart, the idea initially manifested in centaur chess but faced challenges as technological advances began to overshadow human contributions. However, the resurgence of generative AI in the late 2010s, exemplified by conversational agents and text-to-image chatbots, has rekindled interest in the profound potential of human–AI collaboration. This article formalizes the centaurian model, detailing properties associated with various centaurian designs, evaluating their feasibility, and proposing a design methodology that integrates human creativity with artificial intelligence. Additionally, it compares this model with other integrative theories, such as the Theory of Extended Mind and Intellectology, providing a comprehensive analysis of its place in the landscape of human–machine interaction. Full article

Toxoplasma gondii, an obligate intracellular parasite, is a globally prevalent pathogen capable of infecting a wide range of warm-blooded animals, including humans. Ocular toxoplasmosis (OT), a severe manifestation of T. gondii infection, can lead to potentially blinding complications. This comprehensive review delves into the current understanding of T. gondii biology, exploring its complex life cycle, diverse transmission routes, and strain diversity. This article provides an in-depth analysis of the clinical manifestations of OT, which can result from both congenital and acquired infections, presenting a spectrum of signs and symptoms. The review examines various diagnostic strategies employed for OT, including clinical examination, multimodal imaging techniques such as fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA), as well as laboratory tests including serology and molecular methods. Despite extensive research, the specific mechanisms underlying ocular involvement in T. gondii infection remain elusive, and current diagnostic options have limitations. Moreover, the treatment of active and recurrent OT remains a challenge. While existing therapies, such as antimicrobial agents and immunosuppressants, can control active infections, they do not offer a definitive cure or completely prevent recurrence. The clinical endpoints for the management of active and recurrent OT are also not yet well-established, and the available treatment methods carry the potential for adverse effects. This article highlights the need for future research to elucidate the pathogenesis of OT, investigate genetic factors influencing susceptibility to infection, and develop more sensitive and specific diagnostic tools. Enhancing global surveillance, implementing robust prevention strategies, and fostering multidisciplinary collaborations will be crucial in reducing the burden of OT and improving patient outcomes. This comprehensive review aims to provide a valuable resource for clinicians, researchers, and policymakers, contributing to a better understanding of T. gondii infection and its impact on ocular health. Full article

Antimicrobial resistance (AMR), frequently considered a major global public health threat, requires a comprehensive understanding of its emergence, mechanisms, advances, and implications. AMR’s epidemiological landscape is characterized by its widespread prevalence and constantly evolving patterns, with multidrug-resistant organisms (MDROs) creating new challenges every day. The most common mechanisms underlying AMR (i.e., genetic mutations, horizontal gene transfer, and selective pressure) contribute to the emergence and dissemination of new resistant strains. Therefore, mitigation strategies (e.g., antibiotic stewardship programs—ASPs—and infection prevention and control strategies—IPCs) emphasize the importance of responsible antimicrobial use and surveillance. A One Health approach (i.e., the interconnectedness of human, animal, and environmental health) highlights the necessity for interdisciplinary collaboration and holistic strategies in combating AMR. Advancements in novel therapeutics (e.g., alternative antimicrobial agents and vaccines) offer promising avenues in addressing AMR challenges. Policy interventions at the international and national levels also promote ASPs aiming to regulate antimicrobial use. Despite all of the observed progress, AMR remains a pressing concern, demanding sustained efforts to address emerging threats and promote antimicrobial sustainability. Future research must prioritize innovative approaches and address the complex socioecological dynamics underlying AMR. This manuscript is a comprehensive resource for researchers, policymakers, and healthcare professionals seeking to navigate the complex AMR landscape and develop effective strategies for its mitigation. Full article

Anthrax is a notorious disease of public health importance caused by Bacillus anthracis. The causative agent can also be used as a biological weapon. Spores of these bacteria can sustain extreme environmental conditions and remain viable in soil for decades. Domestic and wild ruminants are highly susceptible to this pathogen, which usually presents as a peracute to acute disease. In humans, cutaneous anthrax is frequent but pulmonary and enteric anthrax are more serious. Humans, animals, and the environment are all involved, making anthrax a perfect target for a One Health approach. The environment plays a key role in disease transmission. At a time when the One Health concept is not mere slogans, collaborative efforts of medical professionals, veterinarians, and environmental scientists will be valuable for the prevention and control of this disease. In this review, we discussed the transmission dynamics of anthrax in the environment, animals, and humans, as well as One Health strategies to control and prevent anthrax. Full article

Object detection is a crucial capability of autonomous agents for human–robot collaboration, as it facilitates the identification of the current processing state. In industrial scenarios, it is uncommon to have comprehensive knowledge of all the objects involved in a given task. Furthermore, training during deployment is not a viable option. Consequently, there is a need for a detector that is able to adapt to novel objects during deployment without the necessity of retraining or fine-tuning on novel data. To achieve this, we propose to exploit the ability of discriminative embeddings learned by an object re-identification model to generalize to unknown categories described by a few shots. To do so, we extract object crops with a class-agnostic detector and then compare the object features with the prototypes of the novel objects. Moreover, we demonstrate that the embedding is also effective for predicting regions of interest, which narrows the search space of the class-agnostic detector and, consequently, increases processing speed. The effectiveness of our approach is evaluated in an assembly scenario, wherein the majority of objects belong to categories distinct from those present in the training datasets. Our experiments demonstrate that, in this scenario, our approach outperforms the current best few-shot object-detection approach DE-ViT, which also does not perform fine-tuning on novel data, in terms of both detection capability and inference speed. Full article

Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human–robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality are also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality, and human factors are explored, as this last aspect is vital to ensuring safe and effective human–robot interactions. Full article

Remote sighted assistance (RSA) has emerged as a conversational technology aiding people with visual impairments (VI) through real-time video chat communication with sighted agents. We conducted a literature review and interviewed 12 RSA users to understand the technical and navigational challenges faced by both agents and users. The technical challenges were categorized into four groups: agents’ difficulties in orienting and localizing users, acquiring and interpreting users’ surroundings and obstacles, delivering information specific to user situations, and coping with poor network connections. We also presented 15 real-world navigational challenges, including 8 outdoor and 7 indoor scenarios. Given the spatial and visual nature of these challenges, we identified relevant computer vision problems that could potentially provide solutions. We then formulated 10 emerging problems that neither human agents nor computer vision can fully address alone. For each emerging problem, we discussed solutions grounded in human–AI collaboration. Additionally, with the advent of large language models (LLMs), we outlined how RSA can integrate with LLMs within a human–AI collaborative framework, envisioning the future of visual prosthetics. Full article

Escherichia coli, including extended-spectrum β-lactamases (ESBL)-producing strains, poses a global health threat due to multidrug resistance, compromising food safety and environmental integrity. In industrial settings, rabbits raised for meat have the highest consumption of antimicrobial agents compared to other food-producing animals. The European Union is facing challenges in rabbit farming as rabbit consumption declines and antibiotic-resistant strains of E. coli cause enteric diseases. The aim of this study was to investigate the antibiotic resistance profile, genetic diversity, and biofilm formation in cefotaxime-resistant E. coli strains isolated from twenty rabbit farms in Northern Portugal to address the effect of the pressing issue of antibiotic resistance in the rabbit farming industry. Resistance to critically antibiotics was observed, with high levels of resistance to several categories, such as tetracycline, ampicillin, aztreonam, and streptomycin. However, all isolates were susceptible to cefoxitin and imipenem. Multidrug resistance was common, with strains showing resistance to all antibiotics tested. The blaCTX-M variants (blaCTX-3G and blaCTX-M9), followed by the tetracycline resistance genes, were the most frequent resistance genes found. ST10 clones exhibiting significant resistance to various categories of antibiotics and harboring different resistance genes were detected. ST457 and ST2325 were important sequence types due to their association with ESBL-E. coli isolates and have been widely distributed in a variety of environments and host species. The strains evaluated showed a high capacity for biofilm formation, which varied when they were grouped by the number of classes of antibiotics to which they showed resistance (i.e., seven different classes of antibiotics, six classes of antibiotics, and three/four/five classes of antibiotics). The One Health approach integrates efforts to combat antimicrobial resistance in rabbit farming through interdisciplinary collaboration of human, animal, and environmental health. Our findings are worrisome and raise concerns. The extensive usage of antibiotics in rabbit farming emphasizes the urgent need to establish active surveillance systems. Full article

Multi-unmanned systems have demonstrated significant applications across various fields under complex or extreme operating environments. In order to make such systems highly efficient and reliable, cooperative decision-making methods have been utilized as a critical technology for successful future applications. However, current multi-agent decision-making algorithms pose many challenges, including difficulties understanding human decision processes, poor time efficiency, and reduced interpretability. Thus, a real-time online collaborative decision-making model simulating human cognition is presented in this paper to solve those problems under unknown, complex, and dynamic environments. The provided model based on the Soar cognitive architecture aims to establish domain knowledge and simulate the process of human cooperation and adversarial cognition, fostering an understanding of the environment and tasks to generate real-time adversarial decisions for multi-unmanned systems. This paper devised intricate forest environments to evaluate the collaborative capabilities of agents and their proficiency in implementing various tactical strategies while assessing the effectiveness, reliability, and real-time action of the proposed model. The results reveal significant advantages for the agents in adversarial experiments, demonstrating strong capabilities in understanding the environment and collaborating effectively. Additionally, decision-making occurs in milliseconds, with time consumption decreasing as experience accumulates, mirroring the growth pattern of human decision-making. Full article

Efforts are being made globally to improve the evaluation and understanding of endocrine-disrupting chemicals. Recognition of their impact on human health and the environment has stimulated attention and research in this field. Various stakeholders, including scientists, regulatory agencies, policymakers, and industry representatives, are collaborating to develop robust methodologies and guidelines for assessing these disruptors. A key aspect of these efforts is the development of standardized testing protocols and guidelines that aim to provide consistent and reliable methods for identifying and characterizing endocrine disruptors. When evaluating the potential endocrine-disrupting activity of chemicals, no single test is capable of detecting all relevant endocrine-disrupting agents. The test battery approach is designed to reduce the risk of false negative results for compounds with toxic potential. A weight-of-evidence approach is therefore necessary for endocrine disruptor evaluation. This approach considers various types of data from multiple sources, assessing the overall strength, consistency, and reliability of the evidence. OECD guidelines are highly regarded for their scientific rigor, transparency, and consensus-based development process. It is crucial to explore and develop new methodologies that can effectively evaluate the risks associated with potential endocrine disruptors. Integrating these methods into a comprehensive weight-of-evidence framework will enhance risk assessments and facilitate informed decisions regarding the regulation and management of these substances, ensuring the protection of human health and the environment from their adverse effects. Full article

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces. Full article

In recent years, the adoption of truck–drone collaborative delivery has emerged as an innovative approach to enhance transportation efficiency and minimize the depletion of human resources. Such a model simultaneously addresses the endurance limitations of drones and the time wastage incurred during the “last-mile” deliveries by trucks. Trucks serve not only as a carrier platform for drones but also as storage hubs and energy sources for these unmanned aerial vehicles. Drawing from the distinctive attributes of truck–drone collaborative delivery, this research has created a multi-drone delivery environment utilizing the MPE library. Furthermore, a spectrum of optimization techniques has been employed to enhance the algorithm’s efficacy within the truck–drone distribution system. Finally, a comparative analysis is conducted with other multi-agent reinforcement learning algorithms within the same environment, thus affirming the rationality of the problem formulation and highlighting the algorithm’s superior performance. Full article

Palmitoylethanolamine (PEA) is an endocannabinoid-like compound first encountered within the lipid fractions of specific foods and has intrigued researchers since the 1950s due to its therapeutic effects. This survey aims to explore the therapeutic promise held by PEA as an anti-inflammatory and immunomodulatory agent. The therapeutic impact of PEA reverberates across diverse physiological systems, such as the central nervous system, gastrointestinal tract, vascular network, and the digestive and respiratory system. Additionally, it is effective in pain management and reducing inflammation and immune responses. These attributes have fostered collaborations targeting conditions such as Alzheimer’s disease, multiple sclerosis, cerebral ischemia, neuroinflammation, general inflammation, pain, coagulopathy, steatohepatitis, and acute lung injury. PEA operates both independently and in synergy with other compounds, like paracetamol, luteolin, and oxymetazoline. This efficacy stems from its interactions with pivotal targets, including PPARα, PPAR-δ, PPAR-γ, CB1, CB2, GPR55, and TRPV1. Additionally, PEA exerts a direct influence on the inflammatory cascade, orchestrating precise adjustments in immune responses. Numerous animal studies have elucidated the inherent potential of PEA. Nevertheless, the imperative of reinforcing clinical investigation is evident. This review notably underscores the pivotal necessity for methodologically rigorous clinical trials to definitively establish the translational efficacy of PEA in ameliorating diverse inflammatory pathologies within the human milieu. Full article