Under the combined effects of climate change and human activities, the evolution of hydrological drought in river basins has become complex. Using monthly runoff data from six major Chinese rivers, a coupled machine learning-hydrological model was applied to simulate runoff changes, and the standardized runoff index (SRI) was used to quantify the contributions of climate and human factors to hydrological drought. The results show that: (1) the coupled model outperformed single models, especially in environmentally complex basins. (2) On a monthly scale, human activities were the primary driver of hydrological drought in the Upper Yangtze River Basin from January to March, May, September, and October. Climate change dominated the monthly drought evolution in the source regions of the Yellow River, Upper Pearl River, Middle-Upper Songhua River, Upper Huaihe River, and the source region of Lancang River in April, June–August, and October. (3) Seasonally, both factors influenced the Upper Yangtze, while climate change dominated other basins (except the Lancang in spring and summer). (4) Overall, climate change was the main driver in most basins, while human activity dominated in the Lancang River Basin.
Resumen: La influencia de la arquitectura greco-romana en la nomenclatura neuroanatómica se remonta a más de 2500 años, cuando las estructuras del cuerpo humano fueron nombradas en función de su semejanza con objetos arquitectónicos. Durante la antigüedad, la terminología anatómica se desarrolló en griego y latín, sentando las bases para la nomenclatura médica moderna. Este artículo explora la relación entre elementos arquitectónicos y neuroanatómicos, destacando estructuras como el atrium, tálamo, clinoides, ínsula, puente, fórnix, acueducto y pulvinar.El análisis incluye su origen arquitectónico, su evolución histórica y su impacto en la neuroanatomía. Por ejemplo, el tálamo, que en griego hacía referencia a una alcoba, es una estructura central del diencéfalo involucrada en la transmisión sensorial. El pulvinar, descrito en 1786 por Vicq d’Azyr y nombrado por Burdach en 1817, hace referencia a una tribuna elevada en los juegos romanos, mientras que el puente del tronco encefálico refleja su función de conexión entre estructuras cerebrales, análoga a los puentes romanos.Estos términos no solo evidencian la permanencia del legado greco-romano en la medicina, sino que también destacan cómo la arquitectura y la neuroanatomía han compartido una visión estructural basada en la funcionalidad y la forma. A través de referencias en textos históricos y literatura, se demuestra cómo estos conceptos han trascendido su contexto original, consolidándose en el lenguaje médico actual. Abstract: The influence of Greco-Roman architecture on neuroanatomical nomenclature dates back more than 2,500 years, when structures of the human body were named according to their resemblance to architectural elements. During antiquity, anatomical terminology developed in Greek and Latin, laying the foundations for modern medical nomenclature. This article explores the relationship between architectural and neuroanatomical elements, highlighting structures such as the atrium, thalamus, clinoid processes, insula, pons, fornix, aqueduct, and pulvinar.The analysis includes their architectural origins, historical evolution, and impact on neuroanatomy. For example, the thalamus—derived from a Greek term referring to an inner chamber or alcove—is a central structure of the diencephalon involved in sensory transmission. The pulvinar, first described in 1786 by Vicq d’Azyr and named by Burdach in 1817, refers to an elevated tribune used in Roman public games, while the pons of the brainstem reflects its role as a connecting structure between different regions of the brain, analogous to Roman bridges.These terms not only demonstrate the enduring legacy of Greco-Roman culture in medicine but also highlight how architecture and neuroanatomy share a structural vision grounded in form and function. Through references to historical texts and literature, this article illustrates how these concepts have transcended their original contexts and become firmly established in contemporary medical language.
Oussama Jabrane, Ilias Obda, Driss El Azzab
et al.
The detection of underground cavities and dissolution features is a critical component in assessing geohazards within karst terrains, particularly where natural processes interact with long-term human occupation. This study investigates two contrasting sites in the Sefrou region of northern Morocco: Binna, a rural travertine-dolomite system shaped by Quaternary karstification, and the urban Old Medina of Bhalil, where traditional cave dwellings are carved into carbonate formations. A combined geophysical and geological approach was applied to characterize subsurface heterogeneities and assess the extent of near-surface void development. Vertical electrical soundings (VES) at Binna site delineated high-resistivity anomalies consistent with air-filled cavities, dissolution conduits, and brecciated limestone horizons, all indicative of an active karst system. In the Bhalil old Medina site, ground-penetrating radar (GPR) with low-frequency antennas revealed strong reflection contrasts and localized signal attenuation zones corresponding to shallow natural cavities and potential anthropogenic excavations beneath densely constructed areas. Geological observations, including lithostratigraphic logging and structural cross-sections, provided additional constraints on cavity geometry, depth, and spatial distribution. The integrated results highlight a high degree of subsurface karstification across both sites and underscore the associated geotechnical risks for infrastructure, cultural heritage, and land-use stability. This work demonstrates the value of combining electrical and radar methods with geological analysis for mapping hazardous subsurface voids in cavity-prone Quaternary landscapes, offering essential insights for risk mitigation and sustainable urban and rural planning.
The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.
Jules Lecomte, Konrad Zinner, Michael Neumeier
et al.
Human machine interaction is a huge source of inspiration in today's media art and digital design, as machines and humans merge together more and more. Its place in art reflects its growing applications in industry, such as robotics. However, those interactions often remains too technical and machine-driven for people to really engage into. On the artistic side, new technologies are often not explored in their full potential and lag a bit behind, so that state-of-the-art research does not make its way up to museums and exhibitions. Machines should support people's imagination and poetry in a seamless interface to their body or soul. We propose an artistic sound installation featuring neuromorphic body sensing to support a direct yet non intrusive interaction with the visitor with the purpose of creating sound scapes together with the machine. We design a neuromorphic multihead human pose estimation neural sensor that shapes sound scapes and visual output with fine body movement control. In particular, the feature extractor is a spiking neural network tailored for a dedicated neuromorphic chip. The visitor, immersed in a sound atmosphere and a neurally processed representation of themselves that they control, experience the dialogue with a machine that thinks neurally, similarly to them.
Debasmita Ghose, Oz Gitelson, Marynel Vazquez
et al.
To collaborate with humans, robots must infer goals that are often ambiguous, difficult to articulate, or not drawn from a fixed set. Prior approaches restrict inference to a predefined goal set, rely only on observed actions, or depend exclusively on explicit instructions, making them brittle in real-world interactions. We present BALI (Bidirectional Action-Language Inference) for goal prediction, a method that integrates natural language preferences with observed human actions in a receding-horizon planning tree. BALI combines language and action cues from the human, asks clarifying questions only when the expected information gain from the answer outweighs the cost of interruption, and selects supportive actions that align with inferred goals. We evaluate the approach in collaborative cooking tasks, where goals may be novel to the robot and unbounded. Compared to baselines, BALI yields more stable goal predictions and significantly fewer mistakes.
Takuro Niitsuma, Mitsuo Yoshida, Hideaki Tamori
et al.
Abstract Cultural evolution theory suggests that prestige bias-whereby individuals preferentially learn from prestigious figures-has played a key role in human ecological success. However, its impact within online environments remains unclear, particularly with respect to whether reposts by prestigious individuals amplify diffusion more effectively than reposts by noninfluential users. We analyzed over 55 million posts and 520 million reposts on Twitter (currently X) to examine whether users with high influence scores (hg indices) more effectively amplified the reach of others’ content. Our findings indicate that posts shared by influencers are more likely to be further shared than those shared by non-influencers. This effect persisted over time, especially in viral posts. Moreover, a small group of highly influential users accounted for approximately half of the information flow within repost cascades. These findings demonstrate a prestige bias in information diffusion within the digital society, suggesting that cognitive biases shape content spread through reposting.
Fumika Mi-Ichi, Hiroshi Tsugawa, Tam Kha Vo
et al.
Entamoeba histolytica is a protozoan parasite belonging to the phylum Amoebozoa that causes amebiasis, a global public health problem. E. histolytica alternates its form between a proliferative trophozoite and a dormant cyst. Trophozoite proliferation is closely associated with amebiasis symptoms and pathogenesis whereas cysts transmit the disease. Drugs are available for clinical use; however, they have issues of adverse effects and dual targeting of disease symptoms and transmission remains to be improved. Development of new drugs is therefore urgently needed. An untargeted lipidomics analysis recently revealed structural uniqueness of the Entamoeba lipidome at different stages of the parasite's life cycle involving very long (26-30 carbons) and/or medium (8-12 carbons) acyl chains linked to glycerophospholipids and sphingolipids. Here, we investigated the physiology of this unique acyl chain diversity in Entamoeba, a non-photosynthetic protist. We characterized E. histolytica fatty acid elongases (EhFAEs), which are typically components of the fatty acid elongation cycle of photosynthetic protists and plants. An approach combining genetics and lipidomics revealed that EhFAEs are involved in the production of medium and very long acyl chains in E. histolytica. This approach also showed that the K3 group herbicides, flufenacet, cafenstrole, and fenoxasulfone, inhibited the production of very long acyl chains, thereby impairing Entamoeba trophozoite proliferation and cyst formation. Importantly, none of these three compounds showed toxicity to a human cell line; therefore, EhFAEs are reasonable targets for developing new anti-amebiasis drugs and these compounds are promising leads for such drugs. Interestingly, in the Amoebazoan lineage, gain and loss of the genes encoding two different types of fatty acid elongase have occurred during evolution, which may be relevant to parasite adaptation. Acyl chain diversity in lipids is therefore a unique and indispensable feature for parasitic adaptation of Entamoeba.
This paper examines the effect of real-time, personalized alignment of a robot's reward function to the human's values on trust and team performance. We present and compare three distinct robot interaction strategies: a non-learner strategy where the robot presumes the human's reward function mirrors its own, a non-adaptive-learner strategy in which the robot learns the human's reward function for trust estimation and human behavior modeling, but still optimizes its own reward function, and an adaptive-learner strategy in which the robot learns the human's reward function and adopts it as its own. Two human-subject experiments with a total number of 54 participants were conducted. In both experiments, the human-robot team searches for potential threats in a town. The team sequentially goes through search sites to look for threats. We model the interaction between the human and the robot as a trust-aware Markov Decision Process (trust-aware MDP) and use Bayesian Inverse Reinforcement Learning (IRL) to estimate the reward weights of the human as they interact with the robot. In Experiment 1, we start our learning algorithm with an informed prior of the human's values/goals. In Experiment 2, we start the learning algorithm with an uninformed prior. Results indicate that when starting with a good informed prior, personalized value alignment does not seem to benefit trust or team performance. On the other hand, when an informed prior is unavailable, alignment to the human's values leads to high trust and higher perceived performance while maintaining the same objective team performance.
Human societal growth has greatly pressured available land resources. The key to reducing land pressure and fostering regional synergistic development is revealing the transmission effect of land pressure. We used a modified gravity model to construct a spatial correlation network (SCN) of the land pressure in the Yangtze River Delta region (YRDR) for the years 1995, 2000, 2005, 2010, 2015 and 2020. To examine how the land pressure is transmitted throughout the cities in the YRDR, we used a social network analysis to examine the overall network structure, individual network characteristics and spatial clustering characteristics. Finally, the center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-smoothness further revealed the spatiotemporal evolution and the different patterns of the influencing factors. The results revealed that (1) the spatial correlation structure of the land pressure in the YRDR was relatively stable. Nanjing, Shanghai, Suzhou, Hangzhou and Changzhou played a significant role as linkages. (2) The YRDR was beyond the geographical limit for the land pressure transmission effect and each block had a considerable and mostly steady transmission impact. (3) The center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-stationarity was a viable method for analyzing the factors that influence the land pressure. (4) There were significant regional and temporal variations in the factors influencing land pressure. The influencing factors differed in intensity and direction from city to city. Our results can provide a new perspective on relieving land pressure from the perspective of urban agglomerations and help accomplish the sustainable development of regional land resources.
1. IntroductionIn recent years, there has been a growing trend in the “North and South” dialectics in all fields. International law is no exception to this rule. Recently, in international law studies, in particular, in philosophical discourses and historical development research regarding the origin and the basis of international law obligations, efforts have been made by new scholars to spread the Eastern approach to international law. As these thoughts normally emerge from the less developed and colonial countries, it is called the “Third-World Approach to International Law (TWAIL)”. This approach is rooted in the critical legal studies movement in international law. By taking the Asian perspective into account and also, the evolution of the history of Asian civilizations, this approach attempts to address the inauspicious phenomenon of colonialism in undeveloped or less developed countries, and thereby, decenter Europe as the origin of international law.From 1996 to 2020, we have been facing a significant increase in studies related to this third-world approach to international law, which depicts the possible emergence of a renaissance period in this field of study. Although its initial consistent rise happened between 1998 and 2012, the volume of scientific content production in this approach gradually increased. In fact, this approach points us to a re-examination of the historical evolution of international law. As mentioned, the researchers and experts of this approach are actively present in the world of international law and this approach will undoubtedly impact their opinions and activities. MethodologyFurthermore, the third-world approaches to international law have rooted in different areas, but naturally, they have become more prominent in some categories of international law, in terms of studying the methodology and the historical background of international law that were mentioned earlier.The third-world approach functions in two ways: first, it challenges the radicalized power and the hierarchy of international institutions and norms, and second, it examines the past and the present foundations of colonies and imperial structures of international law. Many of the insights created by the critical approach have been important and useful for the supporters of the third-world approach to international law. So, this approach will analyze the current issues of international law and human rights in a critical discourse. Although there is a fear of division and conflict in such approaches, they create more awareness and increase the debate between different nations on the subject which leads to the universality of international law. It is worth mentioning that Marty Koskenniemi and David Kennedy are among the most famous experts in this field of study and have written many articles about this approach. ConclusionIn conclusion, it seems that the critical and bold approach to international law through the lens of the so-called third-world countries analyzes the deep-rooted inequalities in the international community. The synergy between the critical approach and the third-world approach has expanded the content of international law norms and has created new discourses in international law. Based on the writings of the experts with the third-world approach to international law, it seems that in the past and especially in recent centuries, through the flawed phenomenon of exploitation and colonialism (both in its traditional and modern forms), the powerful countries of the world have seriously damaged the trust of other countries regarding international decisions and regulations concerning third-world countries and especially Asian countries. Thus, actions should be taken to rebuild that trust. It is possible to change the view of third-world countries to powerful countries in international relations. But the emergence of other powerful governments and Asian actors, especially those countries that have a significant impact on the international economy and, as a result, are noticeably influential on politics and international relations, can lead to a redefinition of many concepts in the modern world.Finally, it seems that, regarding the true goals and ideals of international law, the presence of “North and South” views in all areas related to international law have led to different political sides and the current international order. challenges exist at all levels, but the examination and analysis of such multi-dimensional approaches will lead to the expansion of the discourse and exchange of opinions between different nations and will raise awareness and respect for different cultural systems among them, which finally, contributes to the universality of international law.
Several examples of Cyber-physical human systems (CPHS) include real-time decisions from humans as a necessary building block for the successful performance of the overall system. Many of these decision-making problems necessitate an appropriate model of human behavior. Tools from Utility Theory have been used successfully in several problems in transportation for resource allocation and balance of supply and demand \citep{ben1985discrete}. More recently, Prospect Theory has been demonstrated as a useful tool in behavioral economics and cognitive psychology for deriving human behavioral models that characterize their subjective decision-making in the presence of stochastic uncertainties and risks, as an alternative to conventional Utility Theory \citep{kahneman_prospect_2012}. These models will be described in this article. Theoretical implications of Prospect Theory are also discussed. Examples will be drawn from transportation use cases such as shared mobility to illustrate these models as well as the distinctions between Utility Theory and Prospect Theory.
Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.
This paper analyses the development status and technical characteristics of intelligent and networked technologies for conventional ground automatic driving. This paper also analyses the coal mine environmental characteristics, such as no GNSS (global navigation satellite system) signal coverage, low roadway illumination, lots of obstructions and obstacles and ubiquitous coal dust. In the context of the above technical and environmental characteristics, this study puts forward the key technologies of automatic driving research in underground coal mine. The technologies include mobile high-precision positioning technology without GNSS, laser radar technology, underground obstacle detection technology based on millimeter wave radar, underground low illumination video real-time enhancement and characteristic matching technology, underground environment high-precision map technology, underground autonomous vehicle decision planning technology, underground autonomous vehicle control execution technology, underground 5G communication technology and C-V2X direct connection communication technology. It is pointed out that the application of automatic driving in underground coal mine has the advantages of significant demand for fewer people or no people, clear operation management subject, closed scene, fixed route, slow speed, controllable permeability, good 5G construction foundation, easy open interface, etc. The reference architecture of 'human-vehicle-roadway-cloud' coal mine underground network connected automatic driving system is constructed. The system includes underground automatic driving vehicle, roadway infrastructure, personnel, coal mine cloud/edge computing platform and coal mine automatic driving application service platform. This paper designs the coal mine automatic driving vehicle architecture, which includes perception positioning system, network connected collaborative system, vehicle-mounted operating system and vehicle basic components. This paper puts forward three stages of the evolution of the network connected automatic driving in underground coal mines. The first stage is remote control and automatic driving, which realizes the transfer of vehicle drivers from underground to ground. The second stage is the automatic driving of vehicles with emergency takeover boundary. The vehicles are mainly driven by automatic driving, and remote emergency takeover is used as a safety guarantee method. The third stage is 'human-vehicle-roadway-cloud' collaborative control, the underground autonomous vehicles operate safely, efficiently and autonomously to realize highly unmanned intelligent transportation.
The human factor is the main element in the production of road events, not only in terms of percentage, but also in absolute importance, because, ultimately, road and technical issues are involved in road accidents only in strict accordance with the behavior and the direct action of the driver, who, within the road traffic system, is more variable and unpredictable than the vehicle, road and environmental factors. The driver is guilty of 70- 90% of the total number of road accidents. More than a third of people killed and injured in road accidents worldwide are drivers. The article describes the role of the driver in the DVRE system, the risk factors and the causes contributing to the occurrence of road accidents. A brief analysis of road accident statistics due to drivers in the Republic of Moldova and around the world is performed. It also describes the evolution of the unmanned car and other innovative technologies and ideas for automating car driving.
This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot control during pHRI, the model representing pHRI is critical. Recent developments of deep learning technologies would enable us to learn such a model from a dataset collected from the actual pHRI. Our framework is developed based on variational recurrent neural network (VRNN), which can inherently handle time-series data like one pHRI generates. This paper modifies VRNN in order to include the latent dynamics from robot to human explicitly. In addition, to analyze periodic motions like walking, we integrate a new recurrent network based on reservoir computing (RC), which has random and fixed connections between numerous neurons, with VRNN. By augmenting RC into complex domain, periodic behavior can be represented as the phase rotation in complex domain without decaying the amplitude. For verification of the proposed framework, a rope-rotation/swinging experiment was analyzed. The proposed framework, trained on the dataset collected from the experiment, achieved the latent state space where the differences in periodic motions can be distinguished. Such a well-distinguished space yielded the best prediction accuracy of the human observations and the robot actions. The attached video can be seen in youtube: https://youtu.be/umn0MVcIpsY
Real-time 3D human action recognition has broad industrial applications, such as surveillance, human-computer interaction, and healthcare monitoring. By relying on complex spatio-temporal local encoding, most existing point cloud sequence networks capture spatio-temporal local structures to recognize 3D human actions. To simplify the point cloud sequence modeling task, we propose a lightweight and effective point cloud sequence network referred to as SequentialPointNet for real-time 3D action recognition. Instead of capturing spatio-temporal local structures, SequentialPointNet encodes the temporal evolution of static appearances to recognize human actions. Firstly, we define a novel type of point data, Hyperpoint, to better describe the temporally changing human appearances. A theoretical foundation is provided to clarify the information equivalence property for converting point cloud sequences into Hyperpoint sequences. Secondly, the point cloud sequence modeling task is decomposed into a Hyperpoint embedding task and a Hyperpoint sequence modeling task. Specifically, for Hyperpoint embedding, the static point cloud technology is employed to convert point cloud sequences into Hyperpoint sequences, which introduces inherent frame-level parallelism; for Hyperpoint sequence modeling, a Hyperpoint-Mixer module is designed as the basic building block to learning the spatio-temporal features of human actions. Extensive experiments on three widely-used 3D action recognition datasets demonstrate that the proposed SequentialPointNet achieves competitive classification performance with up to 10X faster than existing approaches.