Search Results (144)

Technologies such as cellular automata, genetic algorithms, and artificial intelligence have greatly expanded the range of possible architectural and urban solutions. The challenge is no longer only how to generate form, but how to interpret, evaluate, and select among multiple outcomes produced within computationally open-ended systems. When such generated alternatives are understood as collections, selection can be approached as a curatorial act. This article introduces the concept of algo-scapes: potentially extensive algorithmic landscapes whose meaning emerges only through processes of human discernment, comparison, and choice. To examine the broader logic of such selection, the study adopts a two-phase comparative design based on two parallel surveys. In the first phase, responses from 32 collectors of material objects are used to examine how collections are expanded and evaluated under conditions of differentiation, coherence, and spatial limitation. In the second phase, responses from 50 architects, urban planners, and interior designers are analyzed in order to determine whether analogous patterns can be identified in the ways spatial systems are developed, modified, and brought to temporary states of completion. The findings suggest that the strongest similarity between the two domains lies not in identical criteria of evaluation, but in a shared structure of selective growth based on addition, differentiation, limitation, and temporary completeness. While collectors place greater emphasis on uniqueness, aesthetic value, and personal attachment, architects, urban planners, and interior designers prioritize systemic fit, improvement, and contextual coherence. The article argues that the logic of collection can serve as a useful interpretive model for understanding architectural, urban, and algorithmic design systems, provided that it is understood structurally rather than literally. In this sense, algo-scapes are not meaningful simply because they can generate many alternatives, but because they require meta-level criteria through which meaningful configurations can be selected from potentially open-ended fields of possibility. Full article

This article introduces the Evidence Propagation Calculus (EPiC), an operational framework for first-order reasoning built on a simple but productive observation: familiar inference patterns such as Modus Ponens and Modus Tollens behave like the movement of evidential markers across a structured graph. Positive evidence at an antecedent propagates forward to the consequent; negative evidence at a consequent propagates backward. When both markers coexist at a node, the system is locally inconsistent but not operationally broken. To make this observation precise, EPiC grounds reasoning in a four-valued evidential domain $\mathbb{V}=\{N,T,F,B\}$, where N denotes absence of evidence, T positive evidence, F negative evidence, and B their coexistence. Each logical connective is assigned a local evidential table, and inference is treated uniformly as the progressive restriction of admissible configurations under an evidential order: inadmissible values are eliminated, minimal surviving values are selected as the next effective evidential states, and the resulting restrictions propagate across shared variables. Compound formulas are decomposed into families of local unary and binary constraints through auxiliary variables, making the propagation process explicit and structurally uniform. Within this setting, Modus Ponens, Modus Tollens, and polarity-switching negation are not postulated as primitive rules. They emerge as derived consequences of the same local table calculus. The framework distinguishes different operational routes of justification. In some cases, positive support reaches the target formula directly through successive local restrictions. In others, propagation first stabilizes the relevant components and the target occurrence is then fixed by the corresponding connective table. Consistency is not a second basic notion of justification but a distinguished property of certain justified outcomes. The article establishes local and global soundness, conservativity over the classical fragment, and a conditional adequacy result. It further develops a translation between decomposed formulas and informational graphs, with a reverse reconstruction theorem for well-formed graphs. The result is a unified operational account of first-order reasoning situated between model-theoretic and proof-theoretic approaches, in which semantics, propagation, and graphical structure are mutually supporting rather than independently layered. Full article

This paper introduces and investigates a novel two-parameter sequence, termed the biparametric Appell extension (B-App-Ex) and denoted by ${\mathcal{B}}_n(x;\lambda ,\alpha )$. Standard classical Appell sequences often lack sufficient structural parameters, which can limit their operational flexibility in certain advanced spectral schemes. To address this limitation, we construct an enhanced operational framework by integrating a binomial structural kernel ${(1+w)}^{\lambda }$ with a linear exponential scaling $e^{\alpha xw}$ entirely within the Appell class. We provide a rigorous logical deduction of the fundamental properties of this sequence, including its explicit power series representation, a characteristic three-term recurrence relation, and a governing second-order differential equation (DEq.). A significant contribution of this work is the establishment of analytically exact connection and inverse connection formulas between the B-App-Ex basis and various classical orthogonal polynomial (COP) families. Numerical verification via a collocation-based projection framework demonstrates that these algebraic kernels achieve near-machine epsilon precision (≈${10}^{-15}$), remaining stable even for high-order approximations. Furthermore, by isolating the dilation factor $\alpha$, we establish an $O\left(N\right)$ computational complexity that offers a reduction in latency by approximately two orders of magnitude compared to classical matrix-based transformations. The results demonstrate that the proposed biparametric (Bip.) extension offers a versatile and highly optimized analytical template for modeling complex dynamic systems where structural shifting and spatial scaling must be tuned simultaneously. Full article

Accurate design and performance assessment of solar thermal domestic hot water systems coupled with a heat pump auxiliary typically requires transient simulation, as the system’s behavior depends on multiple interactions among collector characteristics, storage stratification, control logic, weather, and draw-off timing. Monthly methods such as the f-chart are useful for first-pass estimates, but they do not resolve stratification, thermostat operation, or demand timing, and they may become inaccurate for stratified thermostat-controlled systems. Direct comparisons of locally inspectable symbolic and black-box surrogate families for this system class remain limited. A 10,982-case development dataset was generated from minute-resolved annual MATLAB simulations, parameterized by collector area, optical efficiency, and first- and second-order loss coefficients. Three surrogate families were benchmarked under a unified protocol, random forest-assisted shape-constrained symbolic regression (SR), feed-forward artificial neural network (ANN) models, and Automatic Learning of Algebraic Models for Optimization (ALAMO), with the f-chart used as a monthly reference method. The targets were the 12 monthly solar fractions under the direct solar heat definition and the corresponding annual mean solar fraction, evaluated on the same independent 991-case test set. SR achieved the lowest average error (mean absolute percentage error, MAPE = 0.82%; root mean square error, RMSE = 0.006), followed by the ANN (MAPE = 2.07%, RMSE = 0.028) and ALAMO (MAPE = 3.67%, RMSE = 0.060), with Nash–Sutcliffe efficiency (NSE) values above 0.98 for all models. Evaluation times were 0.0026–0.124 s per target, compared with about 1000 s for one full-year simulation. These results define the study as a common protocol benchmark within the studied simulator-defined envelope. SR gives the strongest accuracy with local symbolic inspectability, the ANN remains the flexible retrainable option, and ALAMO provides compact algebraic evaluation with the shortest learned model runtime. Full article

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article

Det Norske Misjonsselskap (Norwegian Missionary Society, NMS) was founded in Stavanger, Norway, in 1842. Having established its first mission field in Africa, it then made plans to work in Asia. In 1902, the first missionaries were sent out to Hunan, an inland, culturally isolated, and conservative Chinese province that experienced particularly strong anti-foreigner and anti-Christian waves. This article argues that the NMS developed a distinctive, pragmatic strategy of political accommodation—rooted in its Pietistic Lutheran social ethos and a Norwegian pioneering spirit—to ensure its institutional survival in Hunan. Examining the NMS’s responses to two major political turning points, the Anti-Christian Movement (1924–1927) and the New Life Movement (1934–1937), the article reveals three key findings: First, the NMS’s proclaimed “neutrality” was not merely a passive stance but an active survival tactic, evolving from a claim grounded in Norway’s geopolitical neutrality into a strategic rhetoric for navigating local political risks. Second, the missionaries’ “excessive expectations” of the Nationalist government, particularly during the New Life Movement, stemmed from a structural cognitive bias shaped by their deep institutional embedding in the KMT-governed local order. Third, their ultimate withdrawal was less a simple political misjudgement than the logical endpoint of a survival model that lacked a contingency plan for revolutionary change. By tracing this specific case, the article contributes to the historiography of Christianity in modern China by illuminating the diversity of missionary strategies beyond the dominant Anglo-American coastal narratives. Full article

This paper investigates the consensus problem for a class of uncertain nonlinear multi-agent systems (MASs) subject to external disturbances with unknown control directions (UCDs). A novel control scheme integrating Nussbaum-type gain is proposed to actively compensate for UCDs, while fuzzy logic systems (FLSs) are embedded in a feed-forward compensator to approximate unknown nonlinear dynamics, thereby achieving global stability. The proposed distributed control laws ensure global asymptotic convergence for both first- and second-order MASs through Lyapunov stability analysis. By implementing a strategic reparameterization technique, this scheme systematically reduces computational complexity, requiring each agent to adapt only a minimal parameter set. Moreover, the framework is extended to address complex formation control tasks. Comprehensive simulations validate the efficacy of the theoretical findings. Full article

Background: Soft tissue sarcomas are rare and highly heterogeneous malignant tumors, often asymptomatic in the early stages. Accurate diagnosis and reliable assessment of the risk of metastasis, classified as low, intermediate, or high, are therefore essential for effective clinical decision-making. However, the application of Artificial Intelligence (AI) approaches to these diseases is often limited by the small size and quality of available datasets, which can compromise model robustness and reliability. Methods: The use of formal methods, based on mathematical modeling and logical verification, can be an alternative to AI techniques. When integrated with radiomics, formal methods provide a structured and interpretable approach to support disease diagnosis. Results: The proposed methodology yielded encouraging results, in line with those reported in the literature. A process was conducted to extract several first- and second-order radiomic classes, which were then selected based on their significance. The resulting models were evaluated using standard performance metrics and obtained 80% accuracy, 83% precision, and 83% recall. Conclusion: The transparency of formal methods improves the interpretability of models and radiomic features, allowing new links with clinical practice to be discovered. The proposed approach is presented as a feasibility and proof-of-concept framework aimed at improving interpretability. Given the very small cohort size, performance metrics should be considered preliminary and descriptive, as they require validation on larger external datasets before any clinical applicability can be claimed. Full article

The goal of the article is to demonstrate the common threads and methods of studying digital storytelling as a unified, second-order aesthetic code. Just as the category of translation, when applied to digital literature, was expanded into a more complex set of methods known as media translation, the article applies similar logic to the notion of intertextuality and proposes an augmented form of “digital“ or “media intertextuality“. Games, interactive fiction, hypertext fiction, story generators, and other born-digital forms are all “texts” that share evolutionary poetics and intertextual strategies extending beyond language into multimodal, procedural, and embodied affordances. Drawing on the concept of structural quotation and semiotic calques, this article suggests that intertextuality should operate across multiple extra-linguistic registers: visual, procedural, and embodied. Neither evolutionary continuity nor broad intertextuality have been sufficiently emphasized in current game studies outside the literary angle. In several examples and case studies—from Zork II to World of Warcraft—this paper demonstrates how repetition with difference, brought about by intertextual links, generates evolutionary continuity and intertextual richness. In this dialogical ecology, AAA blockbusters and experimental works are worth studying together, even if, within the discourse of digital entertainment, they are currently at war. The former push the boundaries of expressive possibility, whereas the latter accrue cultural capital by reworking and critiquing shared codes. Full article

Amidst rapid urbanization and modernization, numerous traditional villages in China face severe challenges, including landscape homogenization and the erosion of their distinctive characteristics. Addressing this issue requires a method capable of systematically identifying, analyzing, and reconstructing both the landscape and its underlying cultural features. This study proposes a digital analytical approach that integrates multimodal artificial intelligence with landscape language theory to address the homogenization of cultural landscapes in traditional Chinese villages. Taking Xinye Village in Zhejiang Province as a case study, the research systematically decodes its landscape spatial narratives and underlying cultural genes. This framework systematically deconstructs village landscapes across four levels: “vocabulary, context, grammar, and semantics”. The village image database is first automatically recognized and statistically analyzed by computer vision technology, which extracts 31 core landscape vocabulary items from three main categories and nine subcategories. Second, Retrieval-augmented Generation technology is employed to synthesize from the constructed domain-specific corpus, a natural context structured around Yuhua Mountain and Daofeng Mountain, as well as a cultural context based on ancestral hall order, connected through folk activities, and idealized by farming and reading passed down through generations. Building on this framework, a multimodal model was used to examine the spatial composition and combinatorial laws of landscape features. Six essential dimensions—spatial layout, visual order, element combination, functional relationships, circulation layout, and scale correlations—revealed the spatial grammar of shuikou landscape. Lastly, the semantic values conveyed by the landscape vocabulary were thoroughly analyzed across three dimensions—form, function, and culture—by integrating a knowledge base. This work creates a landscape language atlas of Xinye Village by combining these studies and using a linguistic model of “character-word-sentence-paragraph”. By methodically deciphering the clan’s cultural code of “farming and reading passed down through generations”, this clearly reconstructs the spatial narrative logic from micro-elements to macro-patterns. This research not only advances the study of landscape language in traditional villages from qualitative description toward a systematic, digital, and interpretable paradigm but also provides an operational theoretical and methodological foundation for the in-depth interpretation, conservation, and transmission of traditional village cultural landscapes. Full article

This paper discusses a multimodal AI system applied to legal reasoning for tax law. The results given here are very general and apply to systems developed for other areas besides tax law. A central goal of this work is to gain a better understanding of the relationships between LLMs (Large Language Models) and automated theorem-proving methodologies. To do this, we suppose (1) two cases for the theorem-proving system: one where it has a countable number of total meanings for its countable number of atoms and the other is where it has an uncountable number of total meanings for its countable number of atoms, and (2) LLMs can have an uncountable number of token meanings. With this in mind, the results given in this paper use the downward and upward Löwenheim–Skolem theorems and logical model theory to contrast these two AI modalities. One modality focuses on syntactic proofs and the other focuses on logical semantics based on LLMs. Particularly, one modality uses a rule-based first-order logic theorem-proving system to perform legal reasoning. The objective of this theorem-proving system is to provide proofs as evidence of valid legal reasoning when enacted laws are applied to particular situations. These proofs are syntactic structures that can be presented in the form of narrative explanations of how the answer to the legal question was determined. The second modality uses LLMs to analyze and transform a user’s tax query so this query can be sent to a first-order logic theorem-proving system to perform its legal reasoning function. The main goal of our application of LLMs is to enhance and simplify user input and output for the theorem-proving system. Using logical model theory, we show how there can exist an equivalence between laws represented in logic of the theorem-proving system, fixed in time when the theorem-proving system was set up, and new semantics given by LLMs. These results are based on logical model theory and Löwenheim–Skolem theorems. Full article

Efficient coordination within the supply chain of prefabricated construction remains a significant challenge due to the high level of interdependence among supply chain participants, the complexity of information flows, and the sensitivity of construction processes to communication delays. This study proposes an integrated methodological framework that combines fuzzy logic and social network analysis (SNA) to evaluate the structural stability and interaction dynamics of supply chain participants. First, a synthetic indicator—link stability—is introduced to quantify the robustness of relationships between supply chain actors. Link stability is defined as a function of five determinants: collaboration level, trust level, communication quality, adoption of digital tools, and effectiveness of dispute resolution. Fuzzy logic is applied to calculate this indicator for each pair of participants, reducing subjectivity in expert assessments. Second, the link stability matrix is used to compute a wide set of centrality measures, including degree, betweenness, closeness, eigenvector, PageRank, information, harmonic, and second-order centralities. These metrics reveal the structural influence of each actor within the network and allow for the identification of core, semi-peripheral, and peripheral roles. A heatmap demonstrates a highly centralized network structure dominated by managerial and design roles. The results contribute to improving supply chain resilience, enhancing communication pathways, and supporting decision-making in prefabricated construction projects. Full article

The rapid expansion of private universities in the past a few decades has created a unique sector in Chinese higher education system. Unlike public research-oriented institutions, Chinese private universities are tuition-dependent, resource-constrained, and primarily vocation-oriented. Lacking the prestige of academics, the campus physical environment in these institutions becomes a key strategic asset for student recruitment, retention, and performance. However, academic research addressing these contexts remains scarce. This study aims to develop a reliable measurement tool—the University Campus Environment Satisfaction Scale (UCESS)—specifically tailored to assess student satisfaction with the physical environment in Chinese private universities. Based on 1050 valid questionnaires from 4 representative universities in Guangdong province, exploratory and confirmatory factor analyses revealed a hierarchical structure comprising 10 first-order factors and 3 second-order dimensions: (1) Safety and accessibility; (2) Core living and learning environment; and (3) Developmental and amenity resources. The findings reveal that students in Chinese private universities prioritize tangible living, teaching and safety conditions over higher-level developmental amenities, reflecting a layered satisfaction logic. Furthermore, this study demonstrates the differentially weighted relationships between campus elements and overall campus satisfaction, providing administrators with a scientific diagnostic tool to optimize resource allocation and implement student-centered planning strategies. Full article

In U-shaped automated container terminals (U-shaped ACTs), automated guided vehicles (AGVs) need to frequently interact with yard cranes (YCs), and separate scheduling of the two devices will affect terminal efficiency. Therefore, this study explores the coordinated scheduling problem between the two devices. To solve this problem, a high-precision simulation model of the U-shaped ACTs is established, which incorporates real operational logic. Second, an Improved Non-dominated Sorting Genetic Algorithm II based on Proximal Policy Optimization (INSGAII-PPO) is proposed. The algorithm uses PPO to realize dynamic genetic operator selection and makes related improvements, which improve the multi-objective optimization ability of NSGAII, and solve the collaborative scheduling problem by combining simulation. Finally, a hybrid weighted Technique for Order Preference by Similarity to Ideal Solution with preferences is proposed to select the final solution. The experimental results show that the scheme obtained by INSGAII-PPO exhibits better convergence and diversity, and offers significant advantages compared with the comparison algorithms. Moreover, the energy consumption and waiting time of the final solution selected by the proposed method are reduced by 3.42% and 4.87% on average. The proposed method has the capability of providing a theoretical reference for the AGVs and YCs collaborative scheduling of U-shaped ACTs. Full article

This paper proposes an adaptive fuzzy fixed-time control (AF-FxT-C) scheme for a piezoelectric-driven microinjector. The inherent hysteresis of the piezoelectric actuator is treated as an unknown nonlinearity. A fuzzy logic system is employed to approximate this hysteresis, along with other lumped disturbances, while an adaptive law is designed to improve approximation accuracy. To address the challenge of inconsistent initial states caused by frequent start-stop operations, a fixed-time control law is developed via a second-order backstepping approach. This guarantees that the upper bound of the system’s settling time is independent of the initial conditions, which is a claim rigorously substantiated by a theoretical stability analysis. The simulation and experimental results validate the effectiveness of the proposed method. The method also maintains robust tracking performance across reference signals of varying frequencies and amplitudes, demonstrating its potential for industrial microinjection applications. Full article