Search Results (37)

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

Pedestrian flow simulations play a pivotal role in urban planning, transportation engineering, and disaster response by enabling the detailed analysis of crowd dynamics and walking behavior. While physical models such as the Social Force model and Boids have been widely used, they often struggle to replicate complex inter-agent interactions. On the other hand, reinforcement learning (RL) methods, although adaptive, suffer from limited interpretability due to their opaque policy structures. To address these limitations, this study proposes a pedestrian simulation framework based on the Anticipatory Classifier System 2 (ACS2), a rule-based evolutionary learning model capable of extracting explicit behavior rules through trial-and-error learning. The proposed model captures the interactions between agents and environmental features while preserving the interpretability of the acquired strategies. Simulation experiments demonstrate that the ACS2-based agents reproduce realistic pedestrian dynamics and achieve comparable adaptability to conventional reinforcement learning approaches such as tabular Q-learning. Moreover, the extracted behavior rules enable systematic analysis of movement patterns, including the effects of obstacles and crowd composition on flow efficiency and group alignment. The results suggest that the ACS2 provides a promising approach to constructing interpretable multi-agent simulations for real-world pedestrian environments. Full article

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, a native species of the Colombian Caribbean. The system integrates three control strategies: a classical Proportional-Integral-Derivative (PID) controller, a fuzzy logic–based PID controller, and a neural network predictive controller. All strategies were evaluated in simulation using a third-order transfer function model identified from real pond data. The fuzzy PID controller reduced the mean squared error (MSE) by 66.5% compared to the classical PID and showed faster settling times and lower overshoot. The neural predictive controller, although anticipatory, exhibited high computational cost and instability. Only the fuzzy PID controller was implemented and validated experimentally, demonstrating robust, accurate, and stable regulation of potential hydrogen (pH), dissolved oxygen, and salinity under dynamic environmental conditions. The system operated in real time on embedded hardware powered by a solar kit, confirming its suitability for rural or off-grid aquaculture contexts. This approach provides a viable and scalable solution for advancing intelligent, sustainable aquaculture practices, particularly for sensitive native species in tropical regions. Full article

This paper explores the concept of evolutionary urban resilience by framing cities as complex, open, and adaptive Social-Ecological-Technological Systems (SETS), shaped by multi-scalar dynamics, systemic uncertainty, and interdependent crises. It challenges the reductionist view of resilience as a fixed capacity or linear sequence of risk management phases, and instead proposes a process-based paradigm rooted in learning, creativity, and the ability to navigate disequilibrium. The framework defines urban resilience as a continuous and iterative transformation process, supported by: (i) a combination of tangible and intangible qualities activated according to problem typology; (ii) cross-domain processes involving infrastructures, flows, governance, networks, and community dynamics; and (iii) the engagement of diverse agents in shared decision-making and coordinated action. These dimensions unfold across three incremental and interdependent scenarios—baseline, critical, and chronic crisis—forming a ladder of resilience that guides communities through escalating challenges. Special emphasis is placed on the role of Information and Communication Technologies (ICTs) as relational and adaptive tools enabling distributed intelligence and inclusive governance. The framework also outlines concrete operational and policy implications for cities aiming to build anticipatory and transformative resilience capacities. Applied to the case of Taranto, the approach offers insights into how structurally fragile communities facing conflicting adaptive trajectories can unlock transformative potential. Ultimately, the paper calls for a shift from government to governance, from control to co-creation, and from reactive adaptation to chaos generativity, recasting urban resilience as an evolving project of collective agency, systemic reconfiguration, and co-production of emergent urban futures. Full article

The rapid growth of Australia’s hydrogen economy highlights the pressing need for innovative regulatory strategies that address the distinct characteristics of hydrogen supply chains. This study focuses on the supply-side dynamics of the hydrogen energy sector, emphasizing the importance of tailored frameworks to ensure the safe, efficient, and reliable movement of hydrogen across the supply chain. Key areas of analysis include the regulatory challenges associated with various transportation and storage methods, particularly during long-distance transport and extended storage periods. The research identifies notable gaps and inconsistencies within the current regulatory systems across Australian states, which inhibit the development of a unified hydrogen economy. To address these challenges, the concept of Proactive Regulation for Hydrogen Supply (PRHS) is introduced. PRHS emphasizes anticipatory governance that adapts alongside technological advancements to effectively manage hydrogen transportation and storage. The study advocates for harmonizing fragmented state frameworks into a cohesive national regulatory system to support the sustainable and scalable expansion of hydrogen logistics. Furthermore, the paper examines the potential of blockchain technology to enhance safety, accountability, and traceability across the hydrogen supply chain, offering practical solutions to current regulatory and operational barriers. Full article

This paper proposes a theoretical model of meaning-making grounded in proprioceptive awareness and embodied imagination, arguing that human cognition is inherently multimodal, anticipatory, and sensorimotor. Drawing on Peircean semiotics, Lotman’s model of cultural cognition, and current research in neuroscience, we show that readiness to act—a proprioceptively grounded anticipation of movement—plays a fundamental role in the emergence of meaning, from perception to symbolic abstraction. Contrary to traditional approaches that reduce language to a purely symbolic or visual system, we argue that meaning arises through the integration of sensory, motor, and affective processes, structured by axial proprioceptive coordinates (vertical, horizontal, sagittal). Using Peirce’s triadic model of interpretants, we identify proprioception as the modulatory interface between sensory stimuli, emotional response, and logical reasoning. A study on skilled pianists supports this view, showing that mental rehearsal without physical execution improves performance via motor anticipation. We define this process as proprioceptive resonance, a dynamic synchronization of embodied states that enables communication, language acquisition, and social intelligence. This framework allows for a critique of linguistic abstraction and contributes to ongoing debates in semiotics, enactive cognition, and the origin of syntax, challenging the assumption that symbolic thought precedes embodied experience. Full article

Ethiopian pastoralist communities are facing a recurrent drought crisis that significantly affects the availability of water and pasture resources for communities dependent on livestock. The increasing intensity, duration and frequency of droughts in the pastoral community in Ethiopia have drawn the attention of multiple stakeholders and increased stakeholder debates on the role of early warning systems (EWSs) for anticipatory action to build climate resilience in the pastoral community. The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), in collaboration with various partners, has developed an interactive web-based digital EWS to provide near real-time information on water and pasture conditions in pastoral and agro-pastoral regions of Ethiopia. In this study, a stakeholder analysis was conducted to identify key stakeholders, understand stakeholder needs, and facilitate collaboration towards sustaining the EWS. The stakeholder analysis revealed the roles and information needs of key actors engaged in livestock water and pasture monitoring and early warning systems aimed at improving the pastoral communities’ resilience. The analysis showed a pressing need for access to real-time information on water and pasture availability and seasonal climate forecasts by local communities for effective and optimal resources management. Local and national governments need similar data for evidence-based decision-making in resource allocation and policy development. International and non-governmental organizations (INGOs) require the same information for efficient humanitarian responses and targeted development interventions. The private sector seeks insights into market dynamics to better align production strategies with community needs. An EWS serves as a vital tool for development partners, facilitating improved planning, coordination, and impact assessment. It also emphasizes the importance of proactive collaboration among stakeholders, including local communities, government bodies, INGOs, and academic and research institutions. Enhanced communication strategies, such as partnerships with local media, are essential for timely information dissemination. Ultimately, sustained collaboration and adaptive strategies are crucial for optimizing the impact of an EWS towards improving the livelihoods and resilience of pastoral communities amid climate variability. Full article

This study introduces a novel framework with the use of extended reality (XR) systems in hydrology, particularly focusing on immersive visualization of hydrologic data for enhanced environmental planning and decision making. The study details the shift from traditional 2D data visualization methods in hydrology to more advanced XR technologies, including virtual and augmented reality. Unlike static 2D maps or charts that require cross-referencing disparate data sources, this system consolidates real-time, multivariate datasets, such as streamflow, precipitation, and terrain, into a single interactive, spatially contextualized 3D environment. Immersive information systems facilitate dynamic interaction with real-time hydrological and meteorological datasets for various stakeholders and use cases, and pave the way for metaverse and digital twin systems. This system, accessible via web browsers and XR devices, allows users to navigate a 3D representation of the continental United States. The paper addresses the current limitations in hydrological visualization, methodology, and system architecture while discussing the challenges, limitations, and future directions to extend its applicability to a wider range of environmental management and disaster response scenarios. Future application potential includes climate resilience planning, immersive disaster preparedness training, and public education, where stakeholders can explore scenario-based outcomes within a virtual space to support real-time or anticipatory decision making. Full article

Background/Objectives: The active straight leg raise requires intricate coordination between the hip, knee, pelvis, and spine. Despite its complexity, limited research has explored the relationship between lower limb raising velocity and trunk muscle motor control during an active straight leg raise in healthy individuals. This study aimed to explore the potential effects of increased lower limb raising velocity on core muscle contractions during active straight leg raises. Methods: Six healthy adult men (mean age: 24.5 ± 2.5 years) participated in this study. Electromyography signals were recorded using surface electrodes placed on the rectus abdominis, external oblique, and internal oblique/transverse abdominis muscles. The participants performed active straight leg raises at three different velocities: 3 s, 2 s, and as fast as possible (max). The electromyography data were analyzed from 250 ms before to 1000 ms after movement initiation, with muscle activity expressed as a percentage of the maximal voluntary isometric contraction. Statistical analyses were conducted using non-parametric tests, including the Friedman test for overall differences, followed by pairwise Wilcoxon signed-rank tests with Bonferroni correction for multiple comparisons (p < 0.05). Results: During the 250 ms before movement initiation, the internal oblique/transverse abdominis, external oblique, and rectus abdominis muscles showed greater activity in the max condition compared to the 3 s and 2 s conditions (Friedman test, p < 0.05), but no significant differences were found in pairwise comparisons (Wilcoxon test, p > 0.05). Similarly, during the 500 ms after movement initiation, internal oblique/transverse abdominis activity was higher in the max condition, with no significant pairwise differences observed. Conclusions: Faster lower limb raising velocities during active straight leg raise may enhance core stability by activating anticipatory and sustained internal oblique/transverse abdominis, external oblique, and rectus abdominis activity on the raised limb side. Training to promote this activation could improve dynamic stability in rapid or asymmetric movements. Full article

This study investigates the long-term effects of demographic trends and admission rules on common properties in the Province of Trento, Italy, which we refer to as historical commons. Historical commons have evolved into socio-ecological systems over the centuries, meaning that communities governed collectively natural resources and lands essential for community survival. Communities and the admission rules that determine their composition are an important constituting element of historical commons because they have developed local ecological knowledge and practices of sustainable use of natural resources. Our study hypothesizes that commons continuity is endangered because of the declining trend of the size of communities being influenced by demographic trends coupled with admission rules. Grounding our research in systems dynamics, we use empirical data including demographic projections and existing admission rules to simulate their effect on the site of the community using the Province of Trento, Italy, as our study region. To achieve that, three types of historical commons are identified: open, semi-open, and closed, each with different admission criteria based on inheritance and/or residency. Results indicate that inheritance-based admission rules can significantly reduce the number of commoners over time, potentially endangering the continuity of these self-governance institutions. The study discusses the results in light of the literature on historical commons’ continuity to evaluate different policies affecting the size of the community grounding on different mental models. The study concludes that a simulation approach can promote an anticipatory approach to the co-design of policies to ensure inclusive continuity of historical commons. Full article

Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model’s performance in five scenarios, emphasising how dopamine levels vary depending on the robot’s situation and stimuli perception. Moreover, we show the model’s integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot. Full article

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio–visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio–visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio–visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus. Full article

Tautosyllabic segment sequences exhibit greater gestural overlap than heterosyllabic ones. In Spanish, it is presumed that word-final consonants followed by a word-initial vowel undergo resyllabification, and generative phonology assumes that canonical CV.CV# and derived CV.C#V onsets are structurally identical. However, recent studies have not found evidence of this structural similarity in the acoustics. The current goal is to investigate anticipatory and carryover vowel nasalization patterns in tautosyllabic, heterosyllabic, and resyllabified segment sequences in Spanish. Nine native speakers of Peninsular Spanish participated in a read-aloud task. Nasal airflow data were extracted using pressure transducers connected to a vented mask. Each participant produced forty target tokens with CV.CV# (control), CVN# (tautosyllabic), CV.NV# (heterosyllabic), and CV.N#V (resyllabification) structures. Forty timepoints were obtained from each vowel to observe airflow dynamics, resulting in a total of 25,200 datapoints analyzed. Regarding anticipatory vowel nasalization, the CVN# sequence shows an earlier onset of nasalization, while CV.NV# and CV.N#V sequences illustrate parallel patterns among them. Carryover vowel nasalization exhibited greater nasal spreading than anticipatory nasalization, and vowels in CV.NV# and CV.N#V structures showed symmetrical nasalization patterns. These results imply that syllable structure affects nasal gestural overlap and that aerodynamic characteristics of vowels are unaffected across word boundaries. Full article

This study examines the dynamic relationship between the business cycle and the construction sector activity in 27 EU countries, focusing on Poland. Using the cross-correlation function (CCF) and a set of economic- and construction-related variables, including GDP growth, construction production, building permits, and construction operating time by backlog, quarterly data from 2000Q1 to 2023Q2 (94 quarters in total) are analyzed. Beyond the CCF analysis, causality is also examined using Toda–Yamamoto tests to explore the nuanced temporal relationships between GDP growth and construction activity proxies. The research uncovers synchronized positive lag max results for construction production, suggesting a harmonized response to broader economic changes, especially within 9 to 11 quarters. In contrast, building permits and construction time by backlog show divergent positive lag max values, suggesting the need for tailored, localized strategies. Negative lag max values emphasize the anticipatory role of the construction sector as an early indicator of economic change. Overcoming methodological challenges, this study provides insights critical for policymakers and researchers, promoting a nuanced understanding of economic synchrony and guiding informed strategies for sustainable development. Future recommendations include refining localized strategies based on lag patterns for optimal economic management. Full article

This study belongs to an emerging area of research seeking ways to depolarize societies in the short run (around events such as elections) as well as in a sustainable fashion. We approach the depolarization process with a model of three homophilic groups (US Democrats, Republicans, and Independents interacting in the context of upcoming federal elections). We expand a previous polarization model, which assumed that each individual interacts with all other individuals in its group with mean-field interactions. We add a depolarization field, which is analogous to the Blume–Capel model’s crystal field. There are currently numerous depolarization efforts around the world, some of which act in ways similar to this depolarization field. We find that for low values of the depolarization field, the system continues to be polarized. When the depolarization field is increased, the polarization decreases. Full article