Search Results (45)

This paper explores what heritage conservation might become when it listens differently—when it opens itself to relational, situated, and community-led practices of care. Beginning with the provocation “Museums? I don’t think this is for us. Museums are far too clever for us,” voiced in the context of an ecomuseum, I interrogate the assumptions that underpin conventional heritage conservation: expert authority, linear temporality, and the desire to stabilize. Drawing on new materialism theories, I question the disciplinary logics that produce heritage as a human centred practice that look at objects as static and conservation as a neutral act. In contrast, I present ecomuseums not as policy model but as conceptual disruption—territories of care that emerge from entanglements of memory and place, becoming, therefore, an active force that are engaged in sustainable practices. In thinking with ecomuseum practices, I consider how conservation would look if shifted from colonial to liberative practices, from control to attention, from fixity to fluidity. I explore conservation as a field of relations—affective and unfinished. Finally, I offer a call for heritage practitioners to reimagine conservation not as the act of keeping things the same, but as an ongoing negotiation with change in a pluriversal world. Full article

This paper proposes a scheduling approach for multi-type spacecraft operational tasks that can be interleaved, considering constrained space–ground telemetry, tracking, and command (TT&C) resources, as well as task splitting. A mixed-integer linear programming model is formulated to maximize the total task completion reward under service time-window constraints for splittable and unsplittable routine tasks, continuous tracking requirements, coupling relationships between routine and continuous tracking tasks, temporal logic dependencies, visibility constraints, and non-overlapping scheduling conditions. To improve solution efficiency and scheduling performance, a heuristic algorithm that combines priority rules with partial backtracking is developed. Task priorities are determined based on completion rewards, due times, execution durations, and temporal relationships, and scheduling is refined to avoid conflicts with predefined constraints. A partial backtracking mechanism guided by task release times enables effective adjustment when TT&C requirements cannot be satisfied. Comparative experiments with CPLEX and four heuristic algorithms validate the effectiveness of the proposed method. Full article

This paper proposes an online human-aware behavior planning method to enable Unmanned Aerial Vehicles (UAVs) to dynamically satisfy high-level linear temporal logic (LTL) task descriptions from human collaborators. The proposed method has the potential to be applied to the industrial human–UAV collaboration. The specific process is as follows. Firstly, the global high-level task of the UAV is described using the formal language of LTL, which can usually be issued by human collaborators in natural language. Secondly, the task description is transformed into a deterministic Rabin automaton, and then the state values of the automaton are given by the distance from the accepted states. Thirdly, the Markov decision process is used to construct the probabilistic behavior model of the UAV offline. Based on this model, a finite state automaton in the finite horizon is dynamically constructed online, and the Cartesian product system within the horizon is constructed, in which the expected arrival states within the horizon are set by the minimum state values. Finally, aiming at maximizing the reachability probability of the expected states, the value iterative algorithm is introduced to solve the optimal behavior plan online within the finite horizon. After it, an infinite horizon planning and execution algorithm is formed. Experiments and analysis show that the results of the online behavior planning are consistent with the task logic at the semantic level, indicating the correctness of the proposed method. Moreover, the proposed method can effectively alleviate the state explosion caused by the global probabilistic model checking and improve the efficiency of plan generation. Full article

This paper addresses the challenging task of automatic scene classification in art films, a genre characterized by symbolic visuals, asynchronous audio, and non-linear storytelling. We propose Styloformer, a multimodal transformer architecture designed to integrate visual, auditory, textual, and curatorial signals into a unified representation space. The model combines cross-modal attention, stylistic clustering, influence prediction, and canonicality estimation to handle the semantic and historical complexity of art cinema. Additionally, we introduce a novel module called Historiographic Navigation, which embeds ontological priors and temporal logic to support interpretive reasoning. Evaluated on multiple benchmarks, Styloformer achieves state-of-the-art performance, including 91.85% accuracy and 94.31% AUC on the MovieNet dataset—outperforming baselines such as CLIP and ViT. Ablation studies further demonstrate the importance of each architectural component. Unlike general-purpose video models, our system is tailored to the aesthetic and narrative structure of art films, making it suitable for applications in digital curation and computational film analysis. Styloformer represents a scalable and interpretable approach to understanding artistic media, bridging machine learning with art historical reasoning. Full article

A context-aware system is a system that adapts its behaviours in response to changes in the system’s environment (i.e., context). Ensuring the correctness of such a system is difficult because the state of the environment changes frequently in an unpredictable manner according to the laws of physics. Hence, formal verification techniques like model-checking and theorem proving do not work in many cases. Runtime Verification (RV) is a lightweight formal verification technique that consists of checking at runtime whether the execution of the system violates the requirements of the system. The Calculus of Context-aware Ambients (CCA) is a process calculus for modelling context-aware systems and reasoning about their behaviours. This paper proposes an RV tool for CCA, called ccaRV. Given a model of a system in CCA and a property of the system written in LTL (Linear Temporal Logic), ccaRV verifies automatically at runtime if the execution of the system violates the property. We propose a semantic approach to RV, where the RV mechanism is defined at the semantics level and not as an add-on. A consequence of this is that there is no need for generating a monitor from the property specification nor for the instrumentation of a system during verification. We define a labelled reduction relation for CCA, where the labels are used to capture the execution traces at the semantics level. Then we extend LTL with spatial operators and context expressions in order to formulate properties about the system context. We use a case study of the MQTT (Message Queue Telemetry Transport) protocol to evaluate the proposed RV approach. The results show that the ccaRV tool is scalable and its decisions are accurate. Full article

This article introduces an innovative hybrid methodology that integrates deterministic Mixed-Integer Linear Programming optimization with stochastic Agent-Based Simulation to address the PDP-TW. The approach is applied to real-world operational data from a luggage-handling company in Lisbon, covering 158 service requests from January 2025. The MILP model generates optimal routing and task allocation plans, which are subsequently stress-tested under realistic uncertainties, such as variability in travel and service times, using ABS implemented in AnyLogic. The framework is iterative: violations of temporal or capacity constraints identified during the simulation are fed back into the optimization model, enabling successive adjustments until robust and feasible solutions are achieved for real-world scenarios. Additionally, the study incorporates transshipment scenarios, evaluating the impact of using warehouses as temporary hubs for order redistribution. Results include a comparative analysis between deterministic and stochastic models regarding operational efficiency, time window adherence, reduction in travel distances, and potential decreases in CO₂ emissions. This work provides a contribution to the literature by proposing a practical and robust decision-support framework aligned with contemporary demands for sustainability and efficiency in urban logistics, overcoming the limitations of purely deterministic approaches by explicitly reflecting real-world uncertainties. Full article

Neurodegeneration is increasingly recognized not as a linear trajectory of protein accumulation, but as a multidimensional collapse of biological organization—spanning intracellular signaling, transcriptional identity, proteostatic integrity, organelle communication, and network-level computation. This review intends to synthesize emerging frameworks that reposition neurodegenerative diseases (ND) as progressive breakdowns of interpretive cellular logic, rather than mere terminal consequences of protein aggregation or synaptic attrition. The discussion aims to provide a detailed mapping of how critical signaling pathways—including PI3K–AKT–mTOR, MAPK, Wnt/β-catenin, and integrated stress response cascades—undergo spatial and temporal disintegration. Special attention is directed toward the roles of RNA-binding proteins (e.g., TDP-43, FUS, ELAVL2), m6A epitranscriptomic modifiers (METTL3, YTHDF1, IGF2BP1), and non-canonical post-translational modifications (SUMOylation, crotonylation) in disrupting translation fidelity, proteostasis, and subcellular targeting. At the organelle level, the review seeks to highlight how the failure of ribosome-associated quality control (RQC), autophagosome–lysosome fusion machinery (STX17, SNAP29), and mitochondrial import/export systems (TIM/TOM complexes) generates cumulative stress and impairs neuronal triage. These dysfunctions are compounded by mitochondrial protease overload (LONP1, CLPP), UPR maladaptation, and phase-transitioned stress granules that sequester nucleocytoplasmic transport proteins and ribosomal subunits, especially in ALS and FTD contexts. Synaptic disassembly is treated not only as a downstream event, but as an early tipping point, driven by impaired PSD scaffolding, aberrant endosomal recycling (Rab5, Rab11), complement-mediated pruning (C1q/C3–CR3 axis), and excitatory–inhibitory imbalance linked to parvalbumin interneuron decay. Using insights from single-cell and spatial transcriptomics, the review illustrates how regional vulnerability to proteostatic and metabolic stress converges with signaling noise to produce entropic attractor collapse within core networks such as the DMN, SN, and FPCN. By framing neurodegeneration as an active loss of cellular and network “meaning-making”—a collapse of coordinated signal interpretation, triage prioritization, and adaptive response—the review aims to support a more integrative conceptual model. In this context, therapeutic direction may shift from damage containment toward restoring high-dimensional neuronal agency, via strategies that include the following elements: reprogrammable proteome-targeting agents (e.g., PROTACs), engineered autophagy adaptors, CRISPR-based BDNF enhancers, mitochondrial gatekeeping stabilizers, and glial-exosome neuroengineering. This synthesis intends to offer a translational scaffold for viewing neurodegeneration as not only a disorder of accumulation but as a systems-level failure of cellular reasoning—a perspective that may inform future efforts in resilience-based intervention and precision neurorestoration. Full article

Smart contracts, as a critical application of blockchain technology, significantly enhance its programmability and scalability, offering broad application prospects. However, frequent security incidents have resulted in substantial economic losses and diminished user trust, making security issues a key challenge for further development. Since smart contracts cannot be modified after deployment, flaws in their design or implementation may lead to severe consequences. Therefore, rigorous pre-deployment verification of their correctness is particularly crucial. This paper explores the symmetry in control flows and state transitions of Solidity smart contracts and leverages this inherent structural symmetry to develop a normalized state transition model based on a finite state machine. The FSM model is subsequently formalized into a Promela model with the Spin model checker. By integrating manually defined Linear Temporal Logic formulas with those generated by Smart Pulse, the Promela model is formally verified in Spin to ensure the correctness and security of smart contracts. This approach establishes a systematic verification framework, providing effective support to enhance the reliability and security of smart contracts. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

Accurate traffic light status detection and the appropriate response to changes in that status are crucial for autonomous driving systems (ADSs) starting from SAE Level 3 automation. The dilemma zone problem occurs during the amber phase of traffic lights, when the ADS must decide whether to stop or proceed through the intersection. This paper proposes a methodology for developing a runtime monitor that addresses the dilemma zone problem and monitors the autonomous vehicle’s behavior at traffic lights, ensuring that the ADS’s decisions align with the system’s safety requirements. This methodology yields a set of safety requirements formulated in controlled natural language, their formal specification in linear temporal logic (LTL), and the implementation of a corresponding runtime monitor. The monitor is integrated within a safety-oriented software architecture through a modular autonomous driving system pipeline, enabling real-time supervision of the ADS’s decision-making at intersections. The results show that the monitor maintained stable and fast reaction times between 40 ms and 65 ms across varying speeds (up to 13 m/s), remaining well below the 100 ms threshold required for safe autonomous operation. At speeds of 30, 50, and 70 km/h, the system ensured correct behavior with no violations of traffic light regulations. Furthermore, the monitor achieved 100% detection accuracy of the relevant traffic lights within 76 m, with high spatial precision (±0.4 m deviation). While the system performed reliably under typical conditions, it showed limitations in disambiguating adjacent, irrelevant signals at distances below 25 m, indicating opportunities for improvement in dense urban environments. Full article

A remarkable feature of manifestly covariant quantum gravity theory (CQG-theory) is represented by its unconstrained Hamiltonian structure expressed in evolution form. This permits the identification of the corresponding dynamical evolution parameter advancing the quantum-wave equation for the $4-$scalar quantum wave function defined on an appropriate Hilbert space. In the framework of CQG-theory, such a temporal parameter is represented by a $4-$scalar proper time s identifying a canonical variable with conjugate quantum operator. The observable character of the evolution parameter is also established through its correspondence with the quantum representation of the cosmological constant originating from non-linear Bohm quantum–vacuum interaction, which is shown to admit an intrinsic functional dependence on s. These conclusions overcome the conceptual limitations about the so-called “problem of time” mentioned in alternative approaches to quantum gravity available in the literature. Hence, the outcome permits one to promote CQG theory as a viable mathematical setting for the establishment of a theory of quantum gravity consistent with the logical and physical principles of both general relativity and canonical quantum mechanics. Full article

Cultural Heritage Sites (CHS) serve as tangible evidence of regional human–environment interactions and spatial representation of historical memory. The research developed a Xinjiang CHS database and integrated geographic information technology and historical geography research methods to examine the spatio-temporal distribution evolution characteristics and geographic influencing factors in the arid region. It utilized the nearest neighbor index, kernel density estimation, the center of gravity model, and standard deviation ellipse to explore the spatio-temporal evolution law. Furthermore, it employed spatial overlay and qualitative text to analyze the geographical influence mechanism of the CHS. The results showed the following: (1) The CHS spatial distribution showed a pattern of “multicore agglomeration-linear extension”, concentrated in 13 key cities and four major areas that extended along the Silk Road routes. (2) The CHS diachronic development fluctuated in a pattern of “three peaks and three valleys”. The spatial center of gravity has shifted from southern Xinjiang to northern Xinjiang, manifesting a concentrated-diffused characteristic along the northeast–southwest axis. (3) The spatial selection followed the rules of “preferring lower terrain” and “proximity to water”. The elevation distribution of CHS has shifted from mid-high elevations to low elevations. The proportion of CHS on low-slope terrain increased from 78.6% in the Pre–Qin period to 93.02% in Modern History. 93.02% of CHS in Modern History were distributed within the 10 km buffer zone of rivers. (4) Climate aridity and human activities formed a dynamic influence mechanism; natural factors constructed the base pattern of CHS distribution, and human activities drove the dynamic adjustment. The findings revealed the historical trajectory and driving logic of the evolution of CHS in Xinjiang and provided a scientific basis for cultural heritage protection and ecological governance. This study had limitations in terms of the limited research scope and the lack of comprehensive quantitative analysis of influencing factors. Full article

Stream processing frameworks have become key enablers of real-time data processing in modern distributed systems. However, robust and scalable mechanisms for verifying temporal properties are often lacking in existing systems. To address this gap, a new runtime verification framework is proposed that integrates linear temporal logic (LTL) monitoring into stream processing applications, such as Apache Spark. The approach introduces reusable LTL monitoring patterns designed for seamless integration into existing streaming workflows. Our case study, applied to real-time financial data monitoring, demonstrates that LTL-based monitoring can effectively detect violations of safety and liveness properties while maintaining stable latency. A performance evaluation reveals that although the approach introduces computational overhead, it scales effectively with increasing data volume. The proposed framework extends beyond financial data processing and is applicable to domains such as real-time equipment failure detection, financial fraud monitoring, and industrial IoT analytics. These findings demonstrate the feasibility of real-time LTL monitoring in large-scale stream processing environments while highlighting trade-offs between verification accuracy, scalability, and system overhead. Full article

Tunnel construction, a critical aspect of railway engineering, is a repetitive process with distinct linear characteristics. While the Linear Scheduling Method (LSM) is widely used for scheduling optimization in linear projects, it struggles to accommodate dynamic construction sequences, reverse construction, and flexible team allocation. Minimizing the project duration is a primary objective in tunnel construction scheduling optimization. To optimize tunnel construction, we propose a duration-shortening method using additional working surfaces, adaptable to multi-segment and multi-team scenarios. A dynamic optimization model is developed for tunnel construction scheduling, integrating LSM, soft logic, Work Breakdown Structure (WBS), and Resource Breakdown Structure (RBS) within a dynamic scheduling framework. This model analyzes logical relationships, work continuity, temporal and spatial constraints, and resource variation, focusing on reverse construction. The Mixed-Integer Programming (MIP) approach is used to build the mathematical model, solved with both exact algorithms and Genetic Algorithms (GA), and implemented in Python 3.12.7. Both algorithms perform well, with the GA excelling at handling complex constraints. Case studies confirm the method’s effectiveness in optimizing durations, devising flexible schedules, and improving efficiency and practicality. This research provides both theoretical insights and practical guidance for tunnel construction scheduling optimization in railway engineering. Full article

Automated verification through the analysis of system models is often recommended to evaluate the correctness of safety-critical systems. Failures in such systems can have serious effects on both individuals and hardware. Historically, many automated verification techniques, including model checking, have been proposed and used to ensure the reliable development of real-time multi-agent systems (RTMASs). This paper investigates the use of Satisfiability Modulo Theories-based bounded model checking (SMT-based BMC) for the existential fragment of deontic epistemic metric temporal logic (EMTLKD), interpreted on models generated by dense-timed deontic interpreted systems (DTDISs). Specifically, we address the translation of the existential model checking problem for EMTLKD into the existential model checking problem for a variant of a deontic epistemic linear temporal logic (${\mathrm{ELTLKD}}_{\mathrm{q}}$). Additionally, we present an SMT-based BMC approach tailored for ${\mathrm{ELTLKD}}_{\mathrm{q}}$ and illustrate its effectiveness using a faulty train controller system as a representative example. Our approach is particularly applicable to engineering domains requiring high-assurance verification, such as railway control, autonomous vehicle coordination, and industrial automation. By formally verifying both timing and compliance constraints, our method supports engineers and system designers in developing more reliable and certifiable safety-critical systems before deployment. Full article