Search Results (334)

This study develops a cash flow growth pathway model to analyze and predict the dynamic and nonlinear lifecycle trajectories of software SMEs. Specifically, it identifies lifecycle stages using cash flow patterns, maps transition pathways empirically, and develops a forecasting framework to predict subsequent lifecycle states using prior-period financial information. By extending the conventional lifecycle framework, the model captures heterogeneity within transitional and contractionary phases, which are particularly relevant in the software industry. The results show that cash flow-based lifecycle scoring is economically meaningful and significantly distinguishes high- and low-growth firms. Moreover, the transition pathway analysis indicates that firm development is not strictly sequential. Firms exhibit downward transitions and meaningful recovery pathways, in addition to strong persistence in expansionary stages. In the forecasting analysis, predictive performance varies substantially across alternative models and class-imbalance treatments. Resampling-based models consistently outperform those estimated on the original dataset, indicating that class imbalance is a critical issue in lifecycle prediction. Among the alternative specifications, the best-performing model achieves strong predictive performance, suggesting that prior-period lifecycle states and financial characteristics contain meaningful forward-looking information. This study contributes to the literature by combining lifecycle theory, cash flow analysis, pathway modeling, and predictive analytics within a unified framework. It also offers practical implications for managers, investors, and policymakers by providing a preliminary basis for monitoring transition-related risks and identifying firms with recovery potential in the software sector. Full article

Digital transformation is reshaping the mining industry by improving resource efficiency and environmental performance. However, empirical evidence explaining how, why, and under which organizational and institutional conditions digitalisation enhances sustainable operational performance (SOP) in Sub-Saharan African mining remains limited, despite the region’s strategic role in global mineral supply. This study examines the effect of digitalisation on sustainable operational performance using longitudinal panel data from 48 mining companies operating in Sub-Saharan Africa between 2013 and 2022. Digitalisation is conceptualized as a multidimensional organizational capability and measured through a Digitalisation Index. The index was systematically derived from corporate annual environmental, social and governance reports using transparent coding procedures and Principal Component Analysis, enhancing measurement transparency and reproducibility. SOP is measured using a composite index encompassing operational efficiency, equipment utilization and maintenance effectiveness, resource utilization and environmental sustainability, and occupational health and safety. Fixed effects panel regression serves as the primary estimator, while the two-step System Generalized Method of Moments addresses endogeneity and dynamic persistence, with robustness analyses confirming result stability. The findings show that digitalisation significantly enhances sustainable operational performance by transforming digital resources into organizational capabilities that strengthen operational resilience, optimize resource allocation, and improve sustainability outcomes. By integrating the Resource-Based View, Dynamic Capabilities Theory, the TOE framework, and the Natural Resource-Based View into a unified explanatory framework, this study advances theory while providing practical guidance for digital capability development and Industry 4.0 investment and informing policies that strengthen digital infrastructure, institutional readiness, and regulatory support for sustainable mining in Sub-Saharan Africa. Full article

This paper develops a unified asymptotic theory for inverse-probability-weighted conditional U-statistics of arbitrary fixed order in the presence of missing-at-random responses and infinite-dimensional functional covariates. The target is a conditional higher-order functional generated by a measurable response kernel and evaluated locally on a separable Banach space. Localization is formulated through delta sequences, providing a common framework for kernel, partition, regressogram, orthogonal series, and related smoothing procedures without recourse to finite-dimensional density arguments. For bounded kernels, we establish uniform almost-complete convergence over pseudo-compact functional domains and obtain a sharp decomposition into deterministic localization bias and stochastic fluctuation. The latter is governed by the localized-kernel variance, the envelope of the delta sequence, the metric complexity of the indexing domain, and the small-ball concentration of the functional covariate. Unbounded kernels are treated under explicit weighted moment, truncation, and summability conditions. The feasible theory quantifies the additional perturbation induced by estimating the propensity score and identifies conditions under which this first-stage uncertainty is asymptotically negligible. Pointwise distributional theory is derived through a denominator linearization combined with the Hoeffding decomposition of the centered localized kernel. The Gaussian limit is driven by the first projection, while the higher-order canonical components are shown to be negligible under explicit local-mass, moment, and noncancellation assumptions. This yields oracle-equivalent feasible inference, a consistent first-projection variance estimator, and asymptotically valid studentized confidence intervals. A finite-grid adaptive comparison principle is also developed for data-driven resolution selection. The scope of the theory is illustrated through conditional rank functionals, discrimination with incomplete labels, metric-learning criteria, and functional prediction. Synthetic and semi-synthetic studies based on functional classification, phoneme log-periodograms, and growth trajectories document the finite-sample interaction between covariate-dependent label observation, local information loss, propensity estimation, and inverse-weighting variance. Full article

Organizations increasingly face the challenge of integrating sustainability into strategic planning and governance systems while ensuring stakeholder participation, accountability, and long-term value creation. This study develops a stakeholder-based governance model by examining how strategic planning processes contribute to the integration of sustainability within organizational management. A mixed-methods approach was employed, combining documentary analysis of institutional planning instruments, surveys administered to students, professors, and administrative staff, and semi-structured interviews with seven senior institutional leaders responsible for strategic decision-making. The empirical evidence was obtained from an Ecuadorian higher education institution operating in the context of an emerging economy. The results revealed high levels of stakeholder support for sustainability integration, with positive perceptions reported by professors (89.8%), students (83.6%), and administrative staff (74.0%). The findings also highlighted the importance of leadership commitment, stakeholder engagement, governance mechanisms, performance indicators, monitoring, and continuous improvement in supporting long-term institutional sustainability. Based on these results, a stakeholder-based governance model is proposed that integrates Strategic Planning, Stakeholder Theory, Sustainable Governance, and Organizational Sustainability into a unified management framework. The study concludes that sustainability can be strengthened when governance structures and strategic planning processes operate as interconnected mechanisms that guide organizational decision-making, performance evaluation, and long-term institutional development. This study proposes an evidence-informed governance model derived from a mixed-methods case study conducted at a private technological university in Ecuador. Full article

This paper integrates Adaptive Information Processing (AIP), the Window of Tolerance (WoT), and Hobfoll’s Conservation of Resources (COR) theory into a unified framework for understanding trauma and therapeutic change. We propose that COR, which we conceptualize clinically as a Reservoir of Adaptive Resources (RoAR), predicts and explains whether experiences fall within or outside the WoT, and is therefore a decisive factor determining whether they are processed adaptively or maladaptively. Clinically, this framework reframes resourcing as a core mechanism of change across all phases of treatment, rather than as preparatory work alone, one that operates within and beyond Eye Movement Desensitization and Reprocessing (EMDR). The framework further proposes that resource sufficiency, relational containment, and safe access to maladaptive material may operate as shared contributing mechanisms of change across therapeutic modalities. Full article

We develop a framework in which quantum field equations are reconstructed from a multi-branch Hamilton–Jacobi structure supplemented by density functionals on configuration space. Each branch is defined by a solution of a functional Hamilton–Jacobi equation, while the associated density encodes measure-theoretic and dynamical information. Under suitable analytical assumptions, we show that the superposition of these branchwise contributions yields a wave functional satisfying the functional Schrödinger equation. The construction is established in a mathematically controlled setting, including finite-dimensional regularizations and a continuum limit under appropriate convergence hypotheses. Explicit examples demonstrate the exact reconstruction of both free and interacting scalar quantum field equations. Extensions to fermionic and gauge fields are outlined, highlighting the role of graded structures and constraints. Beyond the reconstruction result, the framework provides a structural reinterpretation of quantum theory. Classical dynamics is encoded in the space of Hamilton–Jacobi solutions, while quantum phenomena arise from the coherent superposition of these classical configurations through density and phase. From a geometric viewpoint, the construction is naturally related to multisymplectic structures and admits a cohomological interpretation. This approach establishes a direct bridge between classical variational principles and quantum field theory, offering a unified perspective on the emergence of quantum behavior from structured ensembles of classical configurations. Full article

The quantum–classical transition is one of the most frequently invoked concepts in modern physics. Yet the notion of classicality itself is far from unique. Depending on the physical context, classical behavior may be associated with decoherence, the semiclassical limit, thermodynamic averaging, suppression of correlations, emergence of collective order, or geometric simplification of statistical state space. These viewpoints are often presented as if they described a single phenomenon, although they emphasize different physical mechanisms and different operational criteria. In this article, we examine the principal notions of classicality that appear across quantum theory, statistical physics, condensed matter physics, and information geometry. We compare the corresponding mechanisms of classical emergence and analyze the physical quantities commonly used to characterize them, including coherence, entanglement, fluctuations, correlation length, Fisher information, statistical complexity, and information-geometric curvature. We argue that many apparently distinct routes toward classical behavior share a common structural feature: a reduction of effective fluctuation freedom (REFF). From this perspective, classicality may be interpreted as an emergent regime in which the accessible fluctuation manifold becomes progressively constrained, stabilized, or geometrically simplified. This viewpoint naturally unifies decoherence, semiclassical localization, thermodynamic averaging, decorrelation, and collective organization within a common conceptual framework. Rather than representing a unique physical process, the quantum–classical transition appears as a family of related mechanisms through which complex quantum fluctuation structure gives rise to effective macroscopic classical behavior. Full article

A fundamental challenge in space-based optical surveillance constellation design is the quantitative evaluation of coverage capability across the entire low-Earth-orbit (LEO) space. A phase-volume coverage model in the 4D $(a,i,\Omega ,u)$ phase-space is proposed, and the multi-coverage ratio $R_{multi}$, mean covering satellite count ${\overline{N}}_{sat}$, and Observation Geometry Quality Index (OGQI) are introduced to overcome the limitations of single-metric coverage evaluation. A theoretical motivation for the relationship between OGQI components and orbit determination error covariance is provided via Fisher information matrix theory. Spherical-shell 3D spatial and phase-space 4D orbital frameworks are compared: the two frameworks are shown to be mathematically non-equivalent, and phase-space evaluation provides orbit-type-based diagnostic capability unavailable in spatial methods. Within the Walker-Delta framework with Sun-synchronous dawn–dusk orbits, camera pointing and constellation configuration are systematically optimized, yielding a preferred altitude of 1700 km and a $P=6$-plane layout with coverage saturation at $T\ge 18$. Validation against the Space-Track TLE catalog (22,471 LEO objects) yields 95.49% coverage and 83.47% multi-coverage within a 1-h window. The phase-volume framework unifies coverage evaluation and constellation design in a common orbital element space, enabling systematic space-based optical surveillance constellation design. Full article

Generative artificial intelligence (GenAI) is widely considered to hold transformative potential for public administration, but its adoption in local governments remains uneven, weakly institutionalized, and shaped by informal employee experimentation. This study advances the literature by examining GenAI adoption during a rare moment of institutional restructuring in Vietnam, where the 2025 administrative reform abolished the district tier and shifted responsibilities to ward- and commune-level government. To address the unique dynamic of GenAI adoption, this research extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating trust in technology, perceived risk, technological awareness, and perceived organizational support. Survey data were collected from 302 civil servants across eight post-merger ward- and commune-level administrative units in a transitioning Vietnamese province. The study employs a dual-methodological approach, integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) with fuzzy-set Qualitative Comparative Analysis (fsQCA) to evaluate both net effects of individual variables and the underlying causal complexity. The PLS-SEM results show that performance expectancy, social influence, technological awareness, and perceived risk are positively associated with GenAI adoption intention, while effort expectancy and trust in technology operate indirectly through performance expectancy rather than exerting a direct effect. This suggests that, in the context of local government reform, trust matters primarily when it strengthens employees’ belief that GenAI can improve work performance. Perceived organizational support attenuates the positive relationship between perceived risk and adoption intention, suggesting a buffering role in reducing risk-driven experimentation under institutional uncertainty toward more structured use. The fsQCA findings reveal causal asymmetry and equifinality: adoption intention emerges through multiple pathways anchored in perceived usefulness, social influence, and facilitating conditions, whereas non-adoption is associated with low awareness and weak organizational support. The study contributes to public administration, digital governance and technology acceptance research by showing how GenAI adoption is shaped not only by individual perceptions, but also by institutional change. Full article

Accurate network traffic prediction is important for the autonomy, resilience and resource orchestration of 6G and AI-native communication infrastructures, while also supporting green networking and digital twin network applications. However, existing data-driven prediction models face several limitations: over-reliance on massive labeled data, physically implausible predictions, black-box non-interpretability and over-parameterization that impairs edge deployment. To address these issues, this paper proposes a Physics-Informed Network Traffic Prediction (PINTP) framework for graph topology network traffic prediction, which formalizes network traffic evolution as Graph-based Advection–Diffusion–Reaction (ADR) equations and embeds physical regularization into the neural architecture. The framework adopts a hybrid differentiation paradigm unifying automatic differentiation for temporal dynamics and spectral graph theory-derived operators for discrete spatial topologies, and designs a physics-constrained composite loss function with data-driven collocation to balance data fidelity and physical consistency. Experiments are conducted in two complementary settings: a 100-node synthetic random-graph benchmark that evaluates the full graph-topological formulation, and a topology-unavailable real-world telemetry proxy based on Alibaba Cluster Trace v2018 for evaluating sparse-label physics-informed temporal regularization. Comparative analysis with mainstream baselines, including Multilayer Perceptron (MLP), Spatio-Temporal Graph Convolutional Network (STGCN), Graph WaveNet, Transformer, Temporal Convolutional Network (TCN), and XGBoost, shows that the proposed PINTP/PINN implementation achieves a test $R^2$ of 0.898 and MSE of 0.000723 on the 100-node synthetic graph benchmark, close to the strongest Transformer result ($R^2=0.900$, MSE = 0.000710), while using substantially fewer trainable parameters. PINTP/PINN also outperforms Graph WaveNet, STGCN and TCN in this setting, indicating that physics-informed regularization can remain competitive as graph size increases. On the Alibaba proxy task, PINTP/PINN achieves the strongest result among the evaluated models with a test $R^2$ of 0.963. In an independent Alibaba ablation protocol, physical regularization (e.g., $\lambda =10.0$) reduces the mean squared error by 89.15% compared with pure data-driven models and helps mitigate overfitting. This work presents a systematic PINTP framework for graph topology network traffic prediction, achieving competitive prediction accuracy with high parameter efficiency and a degree of physical interpretability. It helps address several limitations of traditional data-driven models, indicates potential for future deployment-oriented studies on real-time network management and resource-constrained edge analytics, and provides an interpretable modeling route for physics-informed network analytics in next-generation communication systems. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

Contemporary cyber threats increasingly employ multi-stage and behaviourally adaptive strategies that challenge static intrusion detection and non-adaptive deception mechanisms. Existing approaches typically treat threat detection, deception deployment, and adversarial attribution as separate functions, limiting timely response and underusing the behavioural evidence generated during attacker interaction. This study develops and evaluates a theory-informed computational and operational framework for autonomous cyber deception. The principal research artefact is a reusable closed-loop architecture rather than a single predictive model: it specifies the interacting components, interfaces, data and control flows, decision rules, and feedback mechanisms that connect detection, deception, telemetry, and attribution. Methodologically, the study follows an engineering design-and-evaluation approach comprising problem and requirement identification from the literature, architectural synthesis, component-level mathematical modelling, prototype implementation, and controlled cyber-range evaluation. In this context, modelling refers to the distinct computational models embedded within the framework: a hybrid detection model combining supervised classification, anomaly detection, and temporal sequence analysis; a Markov Decision Process and reinforcement-learning policy model for selecting and reconfiguring deception actions under engagement, intelligence-gain, resource, and containment objectives; and similarity-based and Bayesian attribution models for estimating MITRE ATT&CK techniques from incomplete behavioural evidence. The component models were developed offline using the NSL-KDD, CICIDS2017, UNSW-NB15, and ToN-IoT datasets, while the integrated prototype was evaluated separately in a controlled enterprise-like cyber range using reconnaissance, brute-force, exploitation, and multi-stage attack scenarios. The reported classification metrics were calculated from the labelled cyber-range evaluation events, not by pooling the four benchmark datasets. On this integrated cyber-range evaluation set, the system achieved 95.4% detection accuracy, 93.6% precision, 94.7% recall, and a 94.1% F1-score, with a mean detection latency of 85 ms. It also achieved 100% honeypot deployment reliability, 92% dynamic reconfiguration success, 88% fingerprinting resistance, and attacker engagement durations of up to 280 s. The attribution component demonstrated end-to-end generation of ATT&CK-aligned technique hypotheses from deception-derived telemetry; however, the present archived evaluation does not support per-technique or baseline-comparative performance claims. These findings show that specialised models and operational services can be coordinated within a unified adaptive defence process, while also identifying the additional class-level and ablation evidence required for rigorous attribution validation. Full article

System models and artifacts continuously require validation and refinement to address imprecise specifications and early-stage requirements in order to derive executable simulations. We propose a multi-layer approach for the automated formalization and execution of partial generative specifications derived from high-level descriptions. The first layer processes model seeds—potentially produced by large language models—that capture coarse structural information such as node relations, ordering, and timing estimates. Rather than requiring fully specified executable models from generative sources, we restrict their role to producing partial specifications, which are then completed through formal synthesis. We implement a synthesis engine based on Boolean satisfiability that constructs executable control flow structures from these partial specifications while enforcing structural consistency and execution semantics. Satisfiability modulo theories are further used to verify temporal properties and establish simulation baselines. The resulting models are then transformed into a set-theoretic discrete event system specification, enabling executable simulation via generated code artifacts. This pipeline establishes a unified pathway from partial generative artifacts to formally validated and executable models. It enables reliable and interpretable exploration of design alternatives and experimentation under formally grounded structural and temporal constraints, while providing a foundation for integrating generative modeling with rigorous execution semantics. Full article

Mobile applications are increasingly promoted as instruments for improving agricultural information access, advisory delivery, market participation, and decision support, particularly for smallholder farmers in developing nations. Research, however, has been dominated by general technology-acceptance and diffusion constructs, while the design-sensitive and infrastructural mechanisms studied in human–computer interaction (HCI) have received comparatively little attention. In this paper we develop a parsimonious HCI model of mobile application use in agriculture. Drawing on the technology acceptance model, the unified theory of acceptance and use of technology, diffusion of innovations, socio-technical systems theory, and human–computer interaction for development (HCI4D), the model proposes that agricultural application use is driven by five antecedent domains: perceived agronomic value, inclusive usability and accessibility, contextual and cultural fit, trust and transparency, and social and institutional embeddedness. Each plays a distinct role across three use stages: adoption intention, sustained use, and decision impact. Contextual constraints (infrastructure and farmer characteristics) moderate these relationships. We develop six testable propositions from the model. The model is conceptual: it is offered as a framework for empirical testing rather than as a validated account of farmer behavior. The paper contributes an HCI-sensitive specification of mobile application use under agricultural field conditions: a “field-ready” conception of mobile HCI. Full article

Live-streaming commerce increasingly relies on live streamers as frontline retail actors, yet the identity structures through which streamers consistently define and enact their personal brand remain theoretically underdeveloped. Drawing on semi-structured in-depth interviews with 13 live streamers across diverse content categories on Chinese platforms (Douyin and Kuaishou), selected through purposive and referral sampling, this qualitative study adopts a theory-guided thematic analysis informed by Kapferer’s Brand Identity Prism. The analysis identifies six interrelated personal brand identity dimensions: Personality-Based Identity, Symbolic and Aesthetic Identity, Relational Orientation Identity, Value-Based Identity, Audience-Aligned Identity, and Content-Oriented Identity. Three theoretical contributions are advanced. First, four of Kapferer’s original facets require contextual deepening in live-streaming commerce, where real-time interaction, continuous audience feedback, and platform affordances substantively reshape how identity operates. Second, Audience-Aligned Identity reconceptualizes Kapferer’s reflection and self-image facets as a unified dimension, grounded in the inseparability of these processes in synchronous interactive retail. Third, Content-Oriented Identity extends existing personal branding frameworks by theorizing content production as a key mechanism of brand legitimation in real-time digital retail environments. The findings offer a conceptual foundation for future scale development and empirical investigation, and provide practical guidance for streamers, platform developers, and brand managers in digital retail contexts. Full article