Search Results (1,379)

This study examines how the organizational work climate shapes the effectiveness of supervision on employee performance. While traditional management theory assumes supervision universally enhances productivity, we observe a puzzling paradox: facing identical tasks and wage systems, some firms rely heavily on hierarchical supervision while others thrive with minimal oversight. Through a four-month field experiment across two Chinese agricultural enterprises (5851 observations), we test whether the supervision’s effectiveness depends on the alignment between leadership practices and organizational climate. In formal management firms (FMFs) characterized by hierarchical governance and arm’s-length employment relationships, directive supervision significantly reduces task completion times by 0.126 standard deviations, equivalent to approximately 4.3 s or 2.8% of the average completion time, with this effect remaining stable throughout the workday. Conversely, in network-embedded firms (NEFs) operating through trust-based relational contracts and social norms, identical supervisory practices yield no performance gains, as informal social control mechanisms already ensure high effort levels, rendering formal supervision redundant. These findings challenge the “best practices” paradigm in strategic HRM, demonstrating that HR success requires a careful alignment between leadership approaches and the organizational climate—an effective HR strategy is not about implementing standardized practices but about achieving a strategic fit between supervisory leadership styles and existing work climates. This climate–leadership partnership is essential for optimizing both employee performance and organizational success. Full article

Quantum-enhanced machine learning, encompassing both quantum algorithms and quantum-inspired classical methods such as tensor networks, offers promising tools for extracting structure from complex, high-dimensional data. In this work, we study the training dynamics of Matrix Product State (MPS) classifiers applied to three-class problems, using both fashion MNIST and hyperspectral satellite imagery as representative datasets. We investigate the phenomenon of grokking, where generalization emerges suddenly after memorization, by tracking entanglement entropy, local magnetization, and model performance across training sweeps. Additionally, we employ information-theory tools to gain deeper insights: transfer entropy is used to reveal causal dependencies between label-specific quantum masks, while O-information captures the shift from synergistic to redundant correlations among class outputs. Our results show that grokking in the fashion MNIST task coincides with a sharp entanglement transition and a peak in redundant information, whereas the overfitted hyperspectral model retains synergistic, disordered behavior. These findings highlight the relevance of high-order information dynamics in quantum-inspired learning and emphasize the distinct learning behaviors that emerge in multi-class classification, offering a principled framework to interpret generalization in quantum machine learning architectures. Full article

Architectural heritage, as a highly symbolized medium of cultural expression, plays a vital role in transmitting collective memory and shaping intercultural tourism experiences. Yet, how visitors from diverse cultural backgrounds perceive and emotionally respond to Chinese architectural symbols remains insufficiently understood. This study addresses this gap by integrating architectural semiotics with cross-cultural psychology to examine perceptual differences across three visitor groups—Mainland China and Hong Kong/Macau/Taiwan (C), East and Southeast Asia (A), and Europe/North America (UA)—at eleven representative Chinese heritage sites. Drawing on 235 in-depth interviews and 1500 online reviews, a mixed-methods design was employed, combining semantic network analysis, grounded theory coding, and affective clustering. The findings reveal that memory structures and cultural contexts shape symbolic perception, that cultural dimensions and affective orientations drive divergent emotional responses, and that interpretive pathways of architectural symbols vary systematically across groups. Specifically, Group C emphasizes collective memory and identity, and Group A engages through structural analogies and regional resonance, while Group UA favors aesthetic form and immersive experiences. These insights inform culturally adaptive strategies for heritage presentation, including memory-anchored curation, comparative cross-regional interpretation, and immersive digital storytelling. By advancing a micro-level model of “architectural symbol–perceptual theme–emotional response–perceptual mechanism,” this research not only enriches theoretical debates on cross-cultural heritage perception but also offers practical guidance for inclusive and resonant heritage interpretation in a global tourism context. Full article

The formation of strategies and tactics of destructive impact (DI) at the stages of complex computer attacks (CCAs) largely depends on the content of intelligence data obtained by the intruder about the attacked elements of distributed information systems (DISs). This study analyzes scientific papers, methodologies and standards in the field of assessing the indicators of awareness of the intruder about the objects of DI and symmetrical indicators of intelligence security of the elements of the DIS. It was revealed that the aspects of changing the quantitative and qualitative characteristics of intelligence data (ID) at the stages of CCA, as well as their impact on the possibilities of using certain types of simple computer attacks (SKAs), are poorly studied and insufficiently systematized. This paper uses technologies for modeling the process of an intruder obtaining ID based on the application of the methodology of black, grey and white boxes and the theory of fuzzy sets. This allowed us to identify the relationship between certain arrays of ID and the possibilities of applying certain types of SCA end-structure arrays of ID according to the levels of identifying objects of DI, and to create a scale of intruder awareness symmetrical to the scale of intelligence protection of the elements of the DIS. Experiments were conducted to verify the practical applicability of the developed models and techniques, showing positive results that make it possible to identify vulnerable objects, tactics and techniques of the intruder in advance. The result of this study is the development of an intruder awareness scale, which includes five levels of his knowledge about the attacked system, estimated by numerical intervals and characterized by linguistic terms. Each awareness level corresponds to one CCA stage: primary ID collection, penetration and legalization, privilege escalation, distribution and DI. Awareness levels have corresponding typical ID lists that can be potentially available after conducting the corresponding type of SCA. Typical ID lists are classified according to the following DI levels: network, hardware, system, application and user level. For each awareness level, the method of obtaining the ID by the intruder is specified. These research results represent a scientific contribution. The practical contribution is the application of the developed scale for information security (IS) incident management. It allows for a proactive assessment of DIS security against CCAs—modeling the real DIS structure and various CCA scenarios. During an incident, upon detection of a certain CCA stage, it allows for identifying data on DIS elements potentially known by the intruder and eliminating further development of the incident. The results of this study can also be used for training IS specialists in network security, risk assessment and IS incident management. Full article

This review is devoted to a comprehensive analysis of modern forms of information warfare in the context of digitalization and global interconnectedness. The work considers fundamental theoretical foundations—cognitive distortions, mass communication models, network theories and concepts of cultural code. The key tools of information influence are described in detail, including disinformation, the use of botnets, deepfakes, memetic strategies and manipulations in the media space. Particular attention is paid to methods of identifying and neutralizing information threats using artificial intelligence and digital signal processing, including partial digital convolutions, Fourier–Galois transforms, residue number systems and calculations in finite algebraic structures. The ethical and legal aspects of countering information attacks are analyzed, and geopolitical examples are given, demonstrating the peculiarities of applying various strategies. The review is based on a systematic analysis of 592 publications selected from the international databases Scopus, Web of Science and Google Scholar, covering research from fundamental works to modern publications of recent years (2015–2025). It is also based on regulatory legal acts, which ensures a high degree of relevance and representativeness. The results of the review can be used in the development of technologies for monitoring, detecting and filtering information attacks, as well as in the formation of national cybersecurity strategies. Full article

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like the anterior temporal lobe and temporoparietal junction, and GC to a fronto-striatal procedural network encompassing Broca’s area and the basal ganglia. Moreover, a central theme is the integration of Chomsky’s theories of Universal Grammar (UG), which posits an innate human linguistic endowment, with their neurobiological correlates. This integration analysis bridges foundational models that first mapped syntax (Friederici’s work) to distinct neural pathways with contemporary network-based theories that view grammar as an emergent property of dynamic, inter-regional neural oscillations. Furthermore, we examine the genetic factors influencing ID and GC, including genes implicated in neurodevelopmental and neurodegenerative disorders. A comparative anatomical perspective across human and non-human primates illuminates the evolutionary trajectory of the language-ready brain. Also, we emphasize that, clinically, ID and GC serve as sensitive neurocognitive markers whose power lies in their often-dissociable profiles. For instance, the primary decline of ID in Alzheimer’s disease contrasts with the severe grammatical impairment in nonfluent aphasia, aiding in differential diagnosis. Importantly, as non-invasive and scalable metrics, ID and GC also provide a critical complement to gold-standard but costly biomarkers like CSF and PET. Finally, the review considers the emerging role of AI and Natural Language Processing (NLP) in automating these linguistic analyses, concluding with a necessary discussion of the critical challenges in validation, ethics, and implementation that must be addressed for these technologies to be responsibly integrated into clinical practice. Full article

Social Networking Sites (SNSs) have redefined the dynamics of self-disclosure, enabling users to share personal information in curated and highly visible ways. Existing research often frames this practice through the “privacy paradox,” yet such models overlook the deeper psychological motivations behind online disclosure. Drawing on more than 150 peer-reviewed sources, this paper advances a conceptual distinction between identity and the Self, with emphasis on the expression of the “True Self” in digital contexts. The discussion, informed by psychological perspectives of the self, examines how SNSs facilitate authentic dimensions of identity rarely expressed offline due to fear of judgment. Integrating theoretical frameworks such as Construal Level Theory, perceived control, digital nudging, and social conformity (lemming effect), the review demonstrates that online disclosure reflects not only strategic behavior but also an intrinsic drive for authenticity and self-verification. SNSs thus emerge as spaces of both social performance and authentic self-expression. Full article

This paper investigates the synchronization problem of singularly perturbed complex networks with time delays, in which a novel event-triggered delayed impulsive control strategy is developed. To conserve limited communication bandwidth, a dynamic event-triggered mechanism is proposed based on a Lyapunov function construction, while incorporating both delay and singular perturbation parameter $\mathit{\epsilon }$ information to avoid ill conditioning. Unlike conventional triggering approaches, the proposed mechanism only requires the Lyapunov function to decrease at impulsive instants, thereby relaxing the constraint on the energy function. Moreover, an impulse-assisted variable $\mathit{\theta }$ is introduced to adjust the event-triggered threshold according to the intensity of impulsive control, which reduces the triggering frequency while ensuring synchronization. By employing stability theory and the singular perturbation method, a singular perturbation parameter $\mathit{\epsilon }$-dependent Lyapunov function is constructed to derive sufficient synchronization conditions and provide the design of the impulsive gain matrix. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach. Full article

Background/Objectives: As healthcare increasingly utilizes digital delivery systems, equitable access and engagement are critical, particularly for caregivers of older adults in rural regions. This study examines how education levels and geographic rurality influence health information-seeking in Mississippi, a state with persistent structural inequities, through the theoretical lenses of Digital Divide Theory and Theory of Planned Behavior. Methods: A statewide survey was conducted among caregivers in Mississippi (N = 452) who support adults aged 50+. The survey assessed rurality level, educational attainment, attitudes toward various health information sources, perceived digital accessibility, and reported challenges in obtaining necessary health guidance. Results: Findings challenged conventional assumptions regarding rural digital engagement. Rural caregivers reported higher trust in both internet and interpersonal health information sources. Rurality did not significantly predict internet use or reported difficulty finding information. However, a significant interaction between education and rurality revealed an “Outcome Divide”: while higher education correlated with more positive attitudes toward online health information in urban areas, this association weakened and reversed in highly rural contexts. Conclusions: These results underscore the need for strategies beyond merely improving access to bridge digital health equity gaps. Policy and interventions must address contextual barriers, such as digital health literacy and relevance, limiting the effectiveness of digital tools, even when internet access is available. Promoting digital health literacy, integrating trusted local interpersonal networks, and adapting educational initiatives to rural realities are essential for advancing equitable and effective digital health engagement. Full article

Over the last few years, the number of Brazilian immigrants living in Portugal has risen significantly, motivated by expectations of safety, prosperity, and professional success. However, the integration into the labor market frequently involves adversities such as professional devaluation, precarious working conditions, and experiences of social exclusion. This qualitative study aims to explore the work experiences of Brazilian immigrants in Portugal, with a special focus on how community support and collective resilience shape their ability to cope with adversity. Based on minority stress theory and intersectionality, we conducted 24 semi-structured interviews with Brazilian immigrants from diverse professional backgrounds. Thematic analysis revealed four main themes: (1) precarious integration into the labor market and underemployment, (2) experiences of xenophobia, racism, and discrimination, (3) mental health challenges and emotional exhaustion, and (4) community support and collective resilience. Participants emphasized the importance of informal solidarity networks to overcome institutional barriers and maintain emotional well-being. These results suggest that resilience is not only an individual resource, but a relational process rooted in everyday acts of care and connection. The study highlights the protective role of community in contexts of structural vulnerability and contributes to current discussions on migrant integration and well-being. Full article

This study investigates the adoption of blockchain technology (BCT) and financing decisions for capital-constrained manufacturers in live streaming supply chains, where product quality information is asymmetric. Although BCT can improve information transparency and consumer trust, its high cost hinders widespread adoption. Based on supply chain financing theory, this research uses a game-theoretic model with linear demand to analyze manufacturers’ BCT adoption and financing strategies under different capital conditions, comparing four scenarios: non-adoption and non-financing (NN), adoption and non-financing (NB), adoption with loan financing from Multi-Channel Networks (MCNs) (LB), and adoption with investment cost-sharing financing from MCNs (CB). Results show that BCT adoption increases market demand and manufacturer profits. The LB strategy is optimal when the manufacturer has sufficient capital and the MCN has a low-investment cost-sharing ratio. In contrast, CB is preferred when the MCN bears a higher share of investment costs, regardless of the manufacturer’s capital. The manufacturer’s financing choice also influences MCN cooperation: MCNs favor CB under high commission rates and low cost-sharing ratios but prefer NB if investment costs are high. These results suggest that manufacturers should select financing based on their capital and cost-sharing terms, while MCNs can adjust cooperation strategies according to commission rates and cost-sharing levels. Full article

Urban morphology has long been studied through typologies, spatial configurations, and historical change, yet cities are not static artifacts but dynamic environments continually reshaped by people, infrastructures, and politics. This article brings Actor–Network Theory (ANT) into dialogue with Aldo Rossi’s notion of the locus to rethink urban design as both enduring form and relational process. Building on Manuel Lima’s taxonomy, the study develops a methodological workflow that translates street networks into visualizations, pairing embeddings with topographic maps to highlight structural patterns. Applied to a comparative set of cities, the analysis distinguishes three broad morphological tendencies—archetypal, geometrical, and relational—each reflecting different logics of urban organization. The results show how scale and connectivity condition the interpretability of embeddings, revealing both alignments and divergences between cartographic and topological representations. Beyond empirical findings, the article frames network literacy as a meeting ground for design theory, science and technology studies, and information visualization. It concludes by proposing that advancing urban morphology today requires not only new computational tools but also sustained interdisciplinary collaboration across design, urban studies, and data science. Full article

Semantic communication (SemCom), as a revolutionary paradigm for next-generation networks, shifts the focus from traditional bit-level transmission to the delivery of meaning and purpose. Grounded in the Data, Information, Knowledge, Wisdom, Purpose (DIKWP) model and its mapping framework, together with the relativity of understanding theory, the discussion systematically reviews advances in semantic-aware communication and personalized semantic security. By innovatively introducing the “Purpose” dimension atop the classical DIKW hierarchy and establishing interlayer feedback mechanisms, the DIKWP model enables purpose-driven, dynamic semantic processing, providing a theoretical foundation for both SemCom and personalized semantic security based on cognitive differences. A comparative analysis of existing SemCom architectures, personalized artificial intelligence (AI) systems, and secure communication mechanisms highlights the unique value of the DIKWP model. An integrated cognitive–conceptual–semantic network, combined with the principle of semantic relativity, supports the development of explainable, cognitively adaptive, and trustworthy communication systems. Practical implementation paths are explored, including DIKWP-based semantic chip design, white-box AI evaluation standards, and dynamic semantic protection frameworks, establishing theoretical links with emerging trends such as task-oriented communication and personalized foundation models. Embedding knowledge representation and cognitive context into communication protocols is shown to enhance efficiency, reliability, and security significantly. In addition, key research challenges in semantic alignment, cross-domain knowledge sharing, and formal semantic metrics are identified, while future research directions are outlined to guide the evolution of intelligent communication networks and provide a systematic reference for the advancement of the field. Full article

To enhance the safety risk management and control capabilities for TBM (Tunnel Boring Machine) construction in hydraulic tunnels, this study conducts a correlation analysis and dynamic evolution study of safety risks. Data were collected through multiple channels, including a literature review, on-site records, and expert interviews. Grounded theory was employed for three-level coding to initially identify risk factors, and gray relational analysis was used for indicator optimization, ultimately establishing a safety risk system comprising 5 categories and 21 indicators. A multi-level hierarchical structure of risk correlation was established using fuzzy DEMATEL and ISM, which was then mapped into a Bayesian network (BN). The degree of correlation was quantified based on probabilistic information, leading to the construction of a risk correlation analysis model based on fuzzy DEMATEL–ISM–BN. Furthermore, considering the risk correlations, a safety risk evolution model for TBM construction in hydraulic tunnels was developed based on system dynamics. The validity of the model was verified using the AY project as a case study. The results indicate that the safety risk correlation structure for TBM construction in hydraulic tunnels consists of 7 levels, with the closest correlation found between “inadequate management systems” and “failure to implement safety training and technical disclosure”. As the number of interacting risk factors increases, the trend of risk level evolution also rises, with the interrelations within the management subsystem being the key targets for prevention and control. The most sensitive factors within each subsystem were further identified as adverse geological conditions, improper construction parameter settings, inappropriate equipment selection and configuration, weak safety awareness, and inadequate management systems. The control measures proposed based on these findings can provide a basis for project risk prevention and control. The main limitations of this study are that some probability parameters rely on expert experience, which could be optimized in the future by incorporating more actual monitoring data. Additionally, the applicability of the established model under extreme geological conditions requires further verification. Full article

The research on the optimization and transformation of rural “production-living-ecological” functions (PLEFs) is of great significance for rural revitalization and sustainable development. Existing studies predominantly evaluate rural PLEFs at the macro level, with few micro-village case studies, and particularly empirical studies in China’s plain agricultural areas. This study takes Caiwu Village, a rural revitalization demonstration village in Yuanyang County, Henan Province, China, as a typical case. First, we constructed a village PLEF classification system based on micro-scale land use types. Then, methods such as GIS spatial analysis, actor network analysis, and satisfaction fuzzy comprehensive evaluation were comprehensively used to systematically analyze the process, mechanism, and effects of the rural PLEF transformation in Caiwu Village. Our research indicates the following: (1) Caiwu Village has gone through three stages of transformation: traditional agriculture leading, ecological agriculture starting, and agriculture-tourism integration development, indicating a shift from traditional agricultural production functions to modern production-ecological composite functions. (2) The PLEF transformation in Caiwu Village resulted from the synergy of multiple actors, including governments, cooperatives, villagers, and water-soil resources, through administrative recruitment and market recruitment. Finally, (3) there are significant differences in the effects of rural PLEF transformation based on villagers’ perception. Specifically, villagers report the highest satisfaction with ecological function, followed by living function, and the lowest satisfaction with production function. This study provides empirical evidence for understanding the differentiated transformation of PLEFs in villages in plain agricultural areas. The research results can provide decision-making references for optimizing and improving the PLEFs of Caiwu Village and other similar villages. Full article