Search Results (109)

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article

In the first years of schooling, inefficient eye movements can impair the development of reading skills. Nonetheless, the improvement of these abilities has been little investigated in children. This pilot study aimed to verify the effectiveness of Office Based Oculomotor Training (OBOT) in enhancing reading skills in ‘poor’ readers. Twenty-one children (aged 7–12 years) underwent an assessment of reading, visual, and perceptual abilities before and after a training of oculomotor skills (i.e., execution of saccadic movements with symbol charts in various modes and types; 14 participants) or a simple reading exercise (7 participants). The overall duration of the training was six weeks. The results showed a specific improvement, in the group subjected to oculomotor training only, not only in oculomotor abilities but also in reading, visuo-perceptual skills, and the ability to resolve crowding. These primary results suggest that the improvement of oculomotor abilities can lead to an indirect increase in reading in developmental age. Full article

The growing influence of social media on political processes extends beyond electoral campaigns and is rapidly transforming the communication practices of incumbent leaders. We address the gap between populist practices in electoral marketing and the implementation of the Ecuadorian president’s discursive strategies from a geopolitical perspective, with a special focus on the use of two platforms: Instagram and TikTok. While existing scholarship has generally analyzed populist discourse on social media, this article applies theoretical and methodological tools to analyze the grammar of war and the performative strategies used to build leadership in contexts of high social unrest. Grounded in contemporary perspectives. This article reveals how populist leaders mobilize emotions through narratives on digital platforms to frame political crises. Using qualitative critical discourse analysis with multimodal and semiotic tools, we examined 156 posts from the official TikTok and Instagram accounts of Ecuadorian President Daniel Noboa, published between January and July 2024. The findings highlight the strategic use of patriotic symbolism, personalization, and emotional appeals to legitimize executive actions and disseminate polarizing narratives. The proposed framework demonstrates how social media communication simplifies complex crisis scenarios into affect-laden “good versus evil” narratives. This model is transferable to other geopolitical and digital contexts, offering both conceptual and methodological tools for analyzing conflict-driven political communication. Full article

This study investigates how Corporate Social Responsibility (CSR) influences financial performance in the Middle Eastern banking sector through the mediating roles of corporate reputation, employee engagement, and innovation orientation. Drawing on stakeholder theory and the resource-based view, a survey of 297 senior banking executives was analyzed using structural equation modeling. The results show that CSR has both a direct positive impact on financial performance and an indirect effect by strengthening intangible resources. Among the mediators, innovation orientation emerged as the strongest pathway, followed by employee engagement and reputation. Collectively, the model accounted for more than 60% of the variance in financial performance, confirming that socially responsible strategies are not symbolic but yield tangible economic value. In the Middle Eastern banking sector—characterized by regulatory turbulence, cultural expectations, and digital transformation—CSR initiatives such as financial inclusion programs, green financing, and Sharia-compliant services provide both legitimacy and resilience. These findings highlight the strategic importance of embedding CSR into banking practices, showing that socially responsible institutions not only secure reputational gains but also cultivate motivated employees, foster innovation, and achieve sustainable profitability. By situating CSR within the unique context of Middle Eastern banking, this study extends the literature on CSR—performance linkages in emerging markets and demonstrates how intangible capabilities can be mobilized to secure long-term financial sustainability. Full article

This study examines the impact of corporate social responsibility (CSR) decoupling on investment efficiency through the lens of systems thinking, using 34,143 firm-year observations from Chinese listed firms between 2009 and 2022. CSR decoupling is conceptualized as a systemic misalignment between two interrelated governance subsystems: the externally facing legitimacy subsystem and the internally embedded strategic action subsystem. Drawing on legitimacy theory and systems thinking, we find that CSR decoupling significantly reduces investment efficiency, primarily through overinvestment, with no consistent evidence of underinvestment. Furthermore, this effect is amplified in tightly coupled supply chain systems and is especially pronounced in foreign-owned firms. The findings contribute to the integration of systems thinking into CSR and corporate governance research, emphasizing the role of structural coupling strength in shaping the consequences of symbolic–substantive misalignment. The study also offers managerial and policy implications for improving the alignment between external CSR communication and internal strategic execution to enhance investment discipline and long-term value creation. Full article

Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the platform-specific behavior of sensing, actuation, and computational modules while continuously updating place-level semantic representations using real-time execution data. These representations encode not only spatial and contextual semantics but also energy characteristics acquired from prior operational history. By embedding historical energy usage profiles into hierarchical semantic maps, this framework enables more efficient route planning and context-aware task assignment. A shared semantic layer facilitates coordinated planning for both single-robot and multi-robot systems, with the decisions informed by energy-centric knowledge. This approach remains hardware-independent and can be applied across diverse platforms, such as indoor service robots and ground-based autonomous vehicles. Experimental validation using a differential-drive mobile platform in a structured indoor setting demonstrates improvements in energy efficiency, the robustness of planning, and the quality of the task distribution. This framework effectively connects high-level symbolic reasoning with low-level energy behavior, providing a unified mechanism for energy-informed semantic decision-making. Full article

Building a causal-loop diagram (CLD) is central to system-dynamics modeling but demands domain insight, the mastery of CLD notation, and the ability to juggle AI, mathematical, and execution tools. Pipeline Algebra (PA) reduces that burden by treating each step—LLM prompting, symbolic or numeric computation, algorithmic transforms, and cloud execution—as a typed, idempotent operator in one algebraic expression. Operators are intrinsically idempotent (implemented through memoization), so every intermediate result is re-used verbatim, yielding bit-level reproducibility even when individual components are stochastic. Unlike DAG (directed acyclic graph) frameworks such as Airflow or Snakemake, which force analysts to wire heterogeneous APIs together with glue code, PA’s compact notation lets them think in the problem space, rather than in workflow plumbing—echoing Iverson’s dictum that “notation is a tool of thought.” We demonstrated PA on a peer-reviewed study of novel-energy commercialization. Starting only from the article’s abstract, an AI-extracted problem statement, and an AI-assisted web search, PA produced an initial CLD. A senior system-dynamics practitioner identified two shortcomings: missing best-practice patterns and lingering dependence on the problem statement. A one-hour rewrite that embedded best-practice rules, used iterative prompting, and removed the problem statement yielded a diagram that conformed to accepted conventions and better captured the system. The results suggest that earlier gaps were implementation artifacts, not flaws in PA’s design; quantitative validation will be the subject of future work. Full article

Low-power microcontrollers, wireless sensors, and embedded gateways form the backbone of many Internet of Things (IoT) deployments. However, their limited memory, constrained energy budgets, and lack of standardized firmware make them attractive targets for diverse attacks, including bootloader backdoors, hardcoded keys, unpatched CVE exploits, and code-reuse attacks, while traditional single-layer defenses are insufficient as they often assume abundant resources. This paper presents a Systematic Literature Review (SLR) conducted according to the PRISMA 2020 guidelines, covering 196 peer-reviewed studies on cross-layer security for resource-constrained IoT and Industrial IoT environments, and introduces a four-axis taxonomy—system level, algorithmic paradigm, data granularity, and hardware budget—to structure and compare prior work. At the firmware level, we analyze static analysis, symbolic execution, and machine learning-based binary similarity detection that operate without requiring source code or a full runtime; at the network and behavioral levels, we review lightweight and graph-based intrusion detection systems (IDS), including single-packet authorization, unsupervised anomaly detection, RF spectrum monitoring, and sensor–actuator anomaly analysis bridging cyber-physical security; and at the policy level, we survey identity management, micro-segmentation, and zero-trust enforcement mechanisms supported by blockchain-based authentication and programmable policy enforcement points (PEPs). Our review identifies current strengths, limitations, and open challenges—including scalable firmware reverse engineering, efficient cross-ISA symbolic learning, and practical spectrum anomaly detection under constrained computing environments—and by integrating diverse security layers within a unified taxonomy, this SLR highlights both the state-of-the-art and promising research directions for advancing IoT security. Full article

This article explores the literary significance of the bell as an important image in the poetry of Taixu 太虛 (1890–1947), a renowned modern Chinese Buddhist reformer and poet–monk. While the bell has long-held symbolic meaning in Buddhist ritual and Chinese literary traditions, its role in poetry has often been overlooked in favor of material culture studies. This article addresses that discrepancy by examining how Taixu inherited and reinterpreted classical bell imagery to articulate his personal emotions and religious philosophy. Following close analysis of more than sixty of his poems, it argues that Taixu used the bell not merely as a traditional image but also as a vehicle for expressing two core Buddhist concepts: mental purification and transcendence of the mundane. The article also highlights his creative pairing of the bell with other classical Chinese images—such as sunsets, moonlight, mountains, and forests—to form complex imagery groups. Taixu’s skillful execution of this technique exemplifies the considerable literary talent and spiritual insight that enabled him to blend Buddhist doctrine with poetic expression to remarkable effect. Overall, his poetic corpus may be considered as both a continuation and a transformation of classical Chinese poetry traditions, affirming his identity as a modern poet–monk who possessed profound esthetic and philosophical vision. Full article

Background: Chronic illness affects children’s health and disrupts the spatial and temporal aspects of schooling by complicating attendance, interrupting learning routines, and exposing institutional rigidity. While many educational systems treat chronicity as an exception to be managed, this review reconceptualizes it as a pedagogical and symbolic challenge to normative assumptions about inclusion, care, and participation. Objective: To systematically examine how school-based behavioral and psychosocial interventions support children and adolescents with chronic health conditions (CHCs) in inclusive educational settings and to analyze what these interventions reveal about institutional practices of care and recognition. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search across five databases, PubMed, ERIC, PsycINFO, Scopus, and Web of Science, for studies published between January 2010 and April 2025. Of 420 records screened, 28 studies met inclusion criteria. Eligible studies reported on school-based interventions for students aged 5–18 with chronic conditions. Methodological quality was appraised using the Cochrane Risk of Bias 2 tool (for RCTs) and the Joanna Briggs Institute checklist (for quasi-experimental designs). Findings were synthesized narratively and thematically. Results: The included studies addressed asthma, attention-deficit/hyperactivity disorder (ADHD), diabetes, epilepsy, autism, cancer, and food allergies. Interventions ranged from nurse-led management and teacher training to peer education and executive function coaching. Most reported improvements in symptom control, school attendance, academic performance, and psychosocial wellbeing. Several studies also demonstrated how interventions reshaped institutional routines and distributed responsibility for care, challenging rampant assumptions about autonomy, ability, and normativity. Conclusions: School-based interventions for chronic illness operate as health strategies and as symbolic and structural enactments of inclusion. When designed relationally, they modulate schools into responsive institutions where care is integrated in everyday pedagogical and organizational practices. Future research prioritizes longitudinal studies, underrepresented contexts, and the active participation of youth in shaping interventions. Full article

Against the backdrop of economic globalization and the increasing adoption of ESG principles, the phenomenon of Chinese private firms’ actual controllers obtaining foreign residency rights has garnered societal attention. Among the emerging issues, “whether and how actual controllers’ foreign residency rights influence corporate greenwashing behavior” has become a critical theoretical and practical concern. This study examines Chinese privately listed A-share companies in heavily polluting industries from 2010 to 2021, employing text analysis to identify symbolic and substantive environmental behaviors through the lens of environmental information disclosure, thereby constructing a comprehensive greenwashing measurement index system. The findings reveal a significant positive correlation between actual controllers’ foreign residency rights and corporate greenwashing, with this effect demonstrating long-term persistence. Heterogeneity analysis indicates that this relationship is more pronounced in companies where actual controllers exercise direct control compared to those with indirect control. Further tests demonstrate that enhanced internal control quality, increased media scrutiny, and stringent audit supervision can effectively mitigate the greenwashing-promoting effect of actual controllers’ foreign residency rights. The conclusions not only extend the theoretical framework of how executive characteristics influence corporate decision-making but also provide a reference governmental departments can use to improve the environmental regulatory policies of affiliates of holders of overseas residency rights. Full article

Digital predistortion and nonlinear behavioral modeling of power amplifiers (PA) have been the subject of intensive research in the time domain (TD), in contrast with the limited number of works conducted in the frequency domain (FD). However, the adoption of orthogonal frequency division multiplexing (OFDM) as a prevalent modulation scheme in current wireless communication standards provides a promising avenue for employing an FD approach. In this work, a procedure to model nonlinear distortion in wireless OFDM systems in the frequency domain is demonstrated for general model structures based on a sparse Bayesian learning (SBL) algorithm to identify a reduced set of regressors capable of an efficient and accurate prediction. The FD-SBL algorithm is proposed to first identify the active FD regressors and estimate the coefficients of the PA model using a given symbol, and then, the coefficients are employed to predict the distortion of successive OFDM symbols. The performance of this proposed FD-SBL with a validation NMSE of $-47$ dB for a signal of 30 MHz bandwidth is comparable to $-46.6$ dB of the previously proposed implementation of the TD-SBL. In terms of execution time, the TD-SBL fails due to excessive processing time and numerical problems for a 100 MHz bandwidth signal, whereas the FD-SBL yields an adequate validation NMSE of −38.6 dB. Full article

Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries that a more positivist and scientific approach began to emerge regarding the explanation of earthquakes, recognizing their origin as stemming from processes occurring beneath the Earth’s surface. A pivotal moment in the emergence of modern seismology was the Lisbon earthquake of 1755, which marked a significant shift towards scientific inquiry. This means that the question of how earthquakes occur has been resolved; thanks to advancements in scientific, geological, and geophysical research, it is now well understood that seismic events result from the collision and movement of lithospheric or tectonic plates. The contemporary challenge that emerges, however, lies in whether such seismic phenomena can be accurately predicted. In this paper, a systematic attempt is made to use techniques based on Grammatical Evolution to determine the magnitude of earthquakes. These techniques use freely available data in which the history of large earthquakes is introduced before the application of the proposed techniques. From the execution of the experiments, it has become clear that the use of these techniques can allow for more effective estimation of the magnitude of earthquakes compared to other machine learning techniques from the relevant literature. Full article

Robotic Process Automation is a technology that replicates human interactions with user interfaces across various applications. However, testing Robotic Process Automation implementations remains challenging due to the dynamic nature of workflows. This paper presents a novel testing framework that first integrates symbolic execution and concolic testing strategies to enhance Robotic Process Automation workflow validation. Building on insights from these methods, we introduce a hybrid approach that optimizes test coverage and efficiency in specific cases. Our open-source implementation demonstrates that automated testing in the Robotic Process Automation domain significantly improves coverage, reduces manual effort, and enhances reliability. Furthermore, the proposed solution supports multiple Robotic Process Automation platforms and aligns with industry best practices for user interface automation testing. Experimental evaluation, conducted in collaboration with industry, validates the effectiveness of our approach. Full article

Within the scope of digital transformation in nuclear power plants (NPPs), task complexity in human–computer interaction (HCI) has become a critical factor affecting the safe and stable operation of NPPs. This study systematically reviews and analyzes existing complexity sources and assessment methods and suggests that complexity is primarily driven by core factors such as the quantity of, variety of, and relationships between elements. By innovatively introducing Halstead’s E measure, this study constructs a quantitative model of dynamic task execution complexity (TEC), addressing the limitations of traditional entropy-based metrics in analyzing interactive processes. By combining entropy metrics and the E measure, a task complexity quantification framework is established, encompassing both the task execution and intrinsic dimensions. Specifically, Halstead’s E measure focuses on analyzing operators and operands, defining interaction symbols between humans and interfaces to quantify task execution complexity (TEC). Entropy metrics, on the other hand, measure task logical complexity (TLC), task scale complexity (TSC), and task information complexity (TIC) based on the intrinsic structure and scale of tasks. Finally, the weighted Euclidean norm of these four factors determines the task complexity (TC) of each step. Taking the emergency operating procedures (EOP) for a small-break loss-of-coolant accident (SLOCA) in an NPP as an example, the entropy and E metrics are used to calculate the task complexity of each step, followed by experimental validation using NASA-TLX task load scores and step execution time for regression analysis. The results show that task complexity is significantly positively correlated with NASA-TLX subjective scores and task execution time, with the determination coefficients reaching 0.679 and 0.785, respectively. This indicates that the complexity metrics have high explanatory power, showing that the complexity quantification model is effective and has certain application value in improving human–computer interfaces and emergency procedures. Full article