Abstract: This study responds to the call to apply the Impression Management Narrative Reporting (IMNR) Index to a larger sample, extending its application to the top 40 JSE-listed companies in South Africa. While prior literature has established the presence of impression management (IM) in narrative reports of JSE-listed companies, no study has quantitatively measured its level using multiple IM tactics combined into a single metric. Using content analysis, secondary data were collected from CEO letters to shareholders in annual or integrated reports. A purposive sample of 26 companies was analysed following the IMNR Index methodology. The findings reveal a median IMNR score of 5.20 (out of 8), indicating high levels of IM. Tone manipulation emerged as the dominant tactic (median 0.90), followed by rhetoric (0.83) and readability (0.81), while comparison was the least used tactic (0.38). These results confirm that CEOs of JSE-listed companies engage in IM strategies, consistent with Agency Theory and Signalling Theory. The study demonstrates the practical relevance of the IMNR Index in emerging market contexts and contributes to the literature by providing quantitative evidence of IM prevalence in CEO communications. Investors should approach CEO letters with scepticism, as high IM levels and tone manipulation may obscure underlying performance. Regulators should consider enhanced disclosure guidelines and potential assurance requirements for narrative reports. Audit committees must exercise active formal oversight to ensure integrity, balance, and faithful representation in these disclosures.

Abstract: Activity-travel patterns provide a behavioral description of daily mobility and support travel-demand forecasting, dynamic origin-destination estimation, and activity-based simulation. With the growth of passively collected mobility data, large-scale reconstruction has become increasingly feasible. However, these data are often incomplete and lack activity semantics, making it difficult to directly obtain complete activity-travel chains from raw observations. This paper reviews activity-travel pattern reconstruction from the perspectives of data collection and mathematical modeling. It first defines the reconstruction task by linking partial mobility observations with latent activity-travel chains. It then discusses major mobility data sources and explains how different observation mechanisms affect model design. Existing methods are grouped into model-driven, data-driven, and hybrid approaches, and their assumptions, advantages, and limitations are compared. Evaluation methods are further summarized at the element, chain, and population levels. The review suggests that future studies should focus on complete-chain inference, uncertainty representation, model transferability, and standard benchmarks. This survey provides an integrated framework for understanding and advancing activity-travel pattern reconstruction.

Abstract: We present a topological perspective on the so-called Past Hypothesis, understood as a proposed explanation for the low-entropy character of the early universe, by analyzing the accessible phase space of quantum fields in a closed Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) universe. On spatial slices with \(S^3\) topology, field configurations must satisfy global boundary conditions, leading to a discrete mode spectrum and an infrared (IR) cutoff set by the curvature radius, \(R(t)\). By comparing the resulting compact spectrum with the volume-matched flat-space continuum at fixed physical ultraviolet (UV) cutoff, we show that compact spatial topology possesses a lower configurational entropy capacity for accessible momentum configurations than its non-compact counterpart. This restriction is strongest at early times, when \(R(t)\) is small, and is progressively relaxed as cosmic expansion densifies the allowed spectrum. Our result identifies compact spatial topology as a geometric regulator of IR phase space and suggests a kinematical mechanism by which closed topology may contribute to the low entropy assumed in the Past Hypothesis.

Abstract: The use of environmental nucleic acids (eNA), both DNA and RNA, as a means for surveillance has been a fixture in the scientific literature for many years. The application of environmental screening for genomic signatures of organisms of interest - particularly those of diagnostic concern, is a promising yet still under-utilised tool for sample screening. While the literature tends to focus on the use of high-throughput sequencing (HTS) to detect organisms of interest using metagenomic or metatranscriptomic sampling, this approach is not cost-competitive with more traditional targeted molecular test methods. While eNA collection typically requires less effort than field surveys, sample collection still does require a significant effort and is typically confined to one-off or periodic sample collection. Consequently, eNA sampling still has not gained significant traction in practical settings despite its popularity in ecological research. To address these issues in a biosecurity context, we report here the development of a testing protocol to monitor irrigation water for the presence of pepino mosaic virus (PepMV) that also includes an endogenous Sphingomonas control. We employed passive sampling through the immersion of filtering devices into the water system to perform sample collection with minimal hands-on effort, while simultaneously developing and validating molecular methods for the recovery of RNA competent for both PCR and high-throughput sequencing. We demonstrate the ability to detect PepMV when viruses are only transiently present in the water system and developed a capsid-integrity PCR protocol for differentiating between intact and denatured (non-viable) virus particles. This work presents a low-cost and low-effort technique for proactive screening of commercial greenhouse facilities to facilitate early detection of harmful crop pests and pathogens.

Abstract: Organizations and individuals increasingly delegate consequential action—email, iden-tity, payments, hiring, clinical triage—to autonomous AI agents supplied by many inde-pendent vendors. No vendor holds end-to-end visibility into the delegator's intent, ac-tions, and consequences, so each optimizes its own metrics behind an opaque boundary. The result is coherence debt: the accumulating, largely invisible gap between what a principal expects its delegated actions to produce and what they actually produce. Be-cause AI acts at machine speed, this debt compounds faster than fragmented human oversight can detect it, and its consequences arrive with greater gravity and less warning. This article states the problem precisely, surveys the current state of practice and the emerging architectural response, and sets out what remains to be built and demonstrated. It formalizes coherence debt as unreconciled, severity-weighted divergence, and shows—via a requisite-variety argument—that governance bolted on externally cannot close the gap in principle. It then distinguishes two loci of the problem: intra-system co-herence debt, which the emerging Mindful Machines paradigm addresses by making governance an intrinsic architectural property (a Digital Genome, an autopoietic control system, and a continuous Discover–Reflect–Apply–Share loop), and boundary coherence debt, which no current approach closes. It proposes the Sovereignty Boundary Ledger, a six-layer reference architecture that extends intrinsic-governance discipline across the trust boundary to agents the principal cannot rewrite, and it specifies concretely what is already demonstrated and what is required to demonstrate the approach at enterprise scale.

Abstract: The international system has witnessed a fundamental transformation in economic relations among major powers during the second decade of the twenty-first century. Global supply chains have shifted from instruments of economic integration and peace promotion—consistent with the liberal approach—to geopolitical weapons deployed in pursuit of strategic and security objectives. This study addresses a critical research gap: the absence of an integrated theoretical framework explaining the mechanisms through which economic interdependence transforms into systematic economic weaponization. It does so through a critical review of the complex interdependence theory developed by Keohane and Nye and the proposal of a new conceptual model that introduces “logistical security” as a pivotal dimension in the structure of the multipolar global system. The study employs an integrative analytical methodology combining theoretical analysis with applied case studies of three principal cases: the semiconductor war between the United States and China, the weaponization of energy supplies in the Russian European context, and the global food crisis following the Russo-Ukrainian war. The study concludes that the current structure of global supply chains produces what can be termed “structural asymmetry in interdependence,” which enables parties occupying central nodal positions within these chains to deploy them as instruments of pressure and coercion. The study culminates in proposing the Multi-Level Logistical Security (MLLS) model as an analytical and applied framework that transcends existing theoretical shortcomings.

Abstract: This critical narrative review and theoretical framework explains the conditional role of music in learning by shifting the focus from whether music directly improves academic achievement to how it regulates learning readiness. Learning readiness refers to the proximal psychological–neural conditions surrounding entry into a specific learning task, including emotional stability, attentional accessibility, motivational activation, cognitive-load fit, and interpersonal safety. Drawing on research on musical emotion, the reward system, cognitive load, learning engagement, and classroom interaction, the review proposes a path model linking musical features, emotion–reward–cognition mechanisms, learning readiness, and learning processes or outcomes. Music is more likely to facilitate low-load tasks, emotion-startup tasks, and collaborative-expression tasks, whereas it may interfere with tasks involving high language load or high executive-control demands. Educational applications should therefore be designed around the task, the learner’s state, individual differences, and the classroom context.

Abstract: This paper presents a deep reinforcement learning approach for generating conflict-avoidance maneuvers that aim to maintain well-clear separation from an intruder aircraft while enabling subsequent recapture of the planned flight path. The proposed method is applicable to automated Detect-and-Avoid functions in lost-link scenarios as well as in autonomous flight. Pairwise encounters are used to train the neural-network policy. The performance of the trained policy is compared with that of a conventional heuristic path-stretch algorithm. Results show that the two approaches achieve comparable performance, with only minor differences in separation and efficiency metrics. In addition, the trained neural networks identify an emergent maneuver strategy that is not available to the heuristic method unless explicitly encoded.

Abstract: Adversarial machine learning is an important area of research in computer science, focusing on understanding and mitigating attacks that make use of the vulnerabilities of machine learning models. In such attacks, adversaries aim to exploit these vulnerabilities in order to harm model's utility or violate its privacy or availability. These attacks include evasion, poisoning, exploratory, and explainability. Evasion, poisoning, and explainability attacks aim to harm the models' utility, while exploratory attacks violate the privacy of the models. To reduce the negative impacts of these attacks, a plethora of defense mechanisms have been proposed. In this survey, we review a substantial body of works and propose a comprehensive and novel taxonomy of defense strategies. We divide defense systems into eight main categories including: training-based, architecture-based, uncertainty-based, detection-based, optimization-based, transformation-based, information-theoretic, and hybrid approaches. For each category, we analyze and summarize key techniques and algorithms. Moreover, we investigate the limitations associated with these defenses, such as model generalization, robustness-accuracy trade-off, and adaptability challenges. By highlighting the strengths and weaknesses of existing defenses, this survey aims to enlighten future research towards more robust and efficient adversarial defense mechanisms.

Abstract: Payment infrastructures are security-critical networked systems whose control status may change as attacks, remediation, and recovery unfold. In such environments, PCI DSS requirement applicability and control satisfaction are not fixed properties of a final assessment point, but may vary across intermediate operational states. Endpoint-oriented standards assessment can consequently fail to retain violations that arise during malware infection, lateral movement, segmentation failure, service disruption, or subsequent restoration. This paper introduces SCF-PCI, a formal and executable framework for path-sensitive PCI DSS valuation over adversarially evolving payment-system states. The framework defines a PCI valuation domain, separates requirement-check applicability from satisfaction, and assigns state-indexed valuations to each reached state. These valuations are then lifted to execution paths, whilst endpoint loss is characterised through restoration, applicability-window closure, and domain exit. SCF-PCI is implemented in OPA Rego using OSCAL-represented PCI DSS control artefacts and evaluated across representative payment-network attack scenarios involving malware infection, remote compromise, DDoS disruption, spyware-assisted fraud, and recovery paths. The results show that state-indexed valuation preserves compliance-relevant security violations that endpoint-only assessment fails to retain. The framework contributes a standards-based assurance method for network and system security analysis in adversarial payment environments.

Abstract: Background/Objectives: Anaphylaxis is a severe systemic allergic reaction that may compromise multiple organ systems and requires prompt recognition and treatment. In childhood and adolescence, food allergy is one of the leading triggers, and clinical presentation may vary according to biological factors, including sex. This study aimed to analyze sex-related differences in pediatric patients with suspected anaphylaxis treated in a tertiary hospital. Methods: A retrospective, observational, descriptive ep-idemiological study was conducted in the pediatric emergency department of a ter-tiary hospital in Madrid, Spain, between 2015 and 2022. Sociodemographic variables, clinical history, symptoms, triage level, Pediatric Assessment Triangle classification, suspected allergens, hospital admission, and final diagnosis were analyzed. Results: A total of 368 patients were included; 65.2% were boys and 57.3% were younger than five years. In the unadjusted analyses, several differences emerged between boys and girls—in pharyngeal involvement, diarrhea, triage priority, and the allergens egg and shellfish. However, none of these differences remained statistically significant once corrected for multiple comparisons. Tree nuts, cow's milk protein, and egg were the most frequently suspected triggers overall. Conclusions: After correction for multiple comparisons, no robust evidence of sex-related differences was found in clinical presentation, triage severity, short-term management, or suspected triggers. The novel contribution of this study is that several apparently plausible sex-related signals were not robust to multiplicity adjustment. Pediatric emergency nursing assessment and triage should therefore remain primarily guided by the child’s clinical presentation and severity rather than by sex alone, while future adequately powered studies should examine whether more subtle sex-related patterns have clinical relevance.

Abstract: (1) Background: Hom-type algebras, proposed by Yau, generalize classical algebras via twisting maps. Dendriform and tridendriform algebras, introduced by Loday and Vallette, decompose associative multiplications and play significant roles in algebraic K-theory and operad theory. Rota-Baxter operators, originating from analysis and probability, have become a vital bridge connecting multiple disciplines. The π-graded structure, a classical tool in algebra, decomposes algebraic objects into direct sums indexed by a group π. (2) Methods: We systematically investigate the properties and construction methods of Rota-Baxter operators on π-graded Hom-algebras, and establish the derivation relations among π-graded Hom-tridendriform algebras, π-graded Hom-dendriform algebras and π-graded Hom-algebras. (3) Results: We prove that the generalized form, namely the π-graded Rota-Baxter system, is equivalent to π-graded Hom-dendriform algebras. Several iterative construction methods for π-graded Rota-Baxter Hom-algebras are also provided. (4) Conclusions: The structural equivalence between π-graded Rota-Baxter Hom-systems and π-graded Hom-dendriform algebras is established, providing a unified framework for these algebraic structures.

Abstract: Recent advances in graph generative modeling have increasingly reframed graph generation as the problem of learning probability paths that transport simple reference distributions toward complex graph distributions. This time-dependent perspective unifies a broad spectrum of generative paradigms, including finite-step denoising, masking, editing, and refinement methods, as well as continuous-time flows, score-based and diffusion models, Schrödinger bridge formulations, and continuous-time Markov processes over discrete graph states. In this survey, we present a unified temporal and transport-based framework for graph generation, organizing existing methods according to their time parameterization, state space, probability-path construction, evolution dynamics, training objectives, and sampling mechanisms. By systematically comparing discrete-time and continuous-time formulations, we reveal fundamental connections between seemingly disparate model families and highlight the design trade-offs governing scalability, structural fidelity, controllability, interpretability, and computational efficiency. We further review evaluation protocols, benchmark datasets, and key application domains, and identify emerging challenges in permutation symmetry, graph validity, large-scale generation, reproducibility, and fair empirical comparison.

Abstract: The maximum sphere packing density in $\mathbb{R}^4$ is $\pi^2/16$, achieved uniquely by the $D_4$ root lattice. The proof establishes a local Voronoi cell bound: every packing cell satisfies $\mathrm{vol}(V_c)\ge 8$, via a four-layer argument (shell localisation, root-alignment, radial monotonicity, chamber positivity for all $176$ Weyl-orbit types) with positivity certificates exact over $\mathbb{Q}$.

Abstract: Background: The clinical adoption of large language models (LLMs) in public healthcare faces a structural impasse. On one side stands the prohibitive capital expenditure of centralised high-performance computing infrastructure; on the other, the substantial privacy risks involved in routing sensitive patient data through third-party cloud APIs. Italian Local Health Authorities (Aziende Sanitarie Locali, ASL) operate hundreds of thousands of clinical workstations that remain idle outside peak administrative hours—an untapped computational reserve that, if put to use, could power local AI inference without any external dependency. Objective: We propose and evaluate OmniMed Federated, a decentralised, browser-native architecture that uses the WebGPU API and the WebLLM framework to build a self-organising peer-to-peer (P2P) network for LLM inference across institutional workstations. The primary goal is absolute data sovereignty: patient-identifiable information must never leave the institutional network perimeter under any operational condition. Unlike established federated approaches that require dedicated edge servers, containerised infrastructure, or native software installation, OmniMed Federated operates entirely within the browser on hardware already deployed in ASL workstation fleets—eliminating procurement overhead and enabling immediate institutional adoption without administrative privilege requirements. Methods: We designed an "Edge-First" orchestration layer that harvests idle compute cycles from existing clinical hardware without requiring software installation, administrative privileges, or infrastructure modification. A five-tier escalation model, managed by a lightweight PHP backend (OmniMed Backend Prototype), coordinates node discovery, task assignment, and graceful fallback. The system is optimised for the hardware homogeneity typical of Italian regional procurement frameworks, which produces predictable performance profiles across ASL workstation fleets. Results: A three-node experimental testbed operating on a shared institutional Wi-Fi network achieved a federated throughput of 19.5 tokens/second—a 137% improvement over single-node execution—while reducing peak per-node VRAM consumption by 62%. Node discovery latency was 140 ms. Patient data residency remained 100% local at all times. The system maintained operational continuity during simulated internet blackout conditions. Conclusions: A federated, browser-based compute-sharing model is technically feasible on existing NHS hardware and offers a scalable, GDPR-compliant pathway to meaningful AI capability in resource-constrained public health environments. The architecture described here is operationally live and openly accessible.

Abstract: Every rational Hodge class on a smooth complex projective variety is the cohomology class of a rational algebraic cycle.

Abstract: Background. The main protease (MPro) of coronaviruses (CoVs) is an essential enzyme involved in viral replication and represents an attractive target for antiviral drug discovery. Based on the similar binding pocket residues within the MPro of different CoVs, the study aimed to identify potential inhibitors of SARS-CoV-2 MPro from PDB ID 6M2N, using integrated computational approaches. Methods. Interaction-based pharmacophore modeling, virtual screening, molecular docking, MM-GBSA binding energy calculation, and molecular dynamics (MD) simulation were performed using BIOVIA Discovery Studio. The validated pharmacophore model was utilized to screen the ZINC database, followed by docking and 100 ns MD simulation analyses of the top-ranked compounds. Results. The pharmacophore model 01 demonstrated favourable predictive performance (AUC = 0.781). Virtual screening identified 483 compounds, from which 21 compounds were selected for docking studies. Among them, ZINC95473654 (Lig-1), ZINC95473725 (Lig-2), and ZINC08792368 (Lig-3) exhibited strong binding affinity toward MPro. Lig-1 demonstrated the best docking score and binding free energy along with stable interactions with key catalytic residues HIS41, CYS145, and GLU166. MD simulation analyses further confirmed that Lig-1, Lig-2 and Lig-3 maintained stable conformations. The hydrogen bond distance monitoring and post MD-MM-GBSA results suggest Lig-1 followed by Lig-3 as an inhibitor for Mpro and persistent intermolecular interactions throughout the 100 ns simulation period. Conclusion. The findings suggest that Lig-1, followed by Lig-3, may serve as promising computational lead compounds targeting SARS-CoV-2 Mpro, representing promising candidates for further experimental validation.

Abstract: Cancer incidence exhibits substantial spatial disparities linked to environmental, behavioral, built-environment, healthcare-access, and socioeconomic conditions, yet the spatial scales at which these relationships operate remain insufficiently understood. This study develops an explainable spatial epidemiology workflow that integrates Random Forest, SHapley Additive exPlanations (SHAP), Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to examine county-level incidence for all-site, colon, breast, and skin cancers across Texas, USA. Random Forest and SHAP were used to identify outcome-specific nonlinear predictor relevance, and OLS, GWR, and MGWR were used to compare global, local, and multiscale spatial associations before and after RF-SHAP feature screening. MGWR generally achieved higher model fit than OLS and GWR. Before RF-SHAP screening, MGWR R² values were 0.701 for all-site cancer, 0.516 for colon cancer, 0.499 for breast cancer, and 0.694 for skin cancer, compared with OLS R² values of 0.343, 0.338, 0.257, and 0.368. RF-SHAP reduced predictors by about one-half and consistently improved AICc. The results show that environmental exposures, activity-related conditions, transportation access, screening, food insecurity, and chronic health indicators contribute to spatial differences in cancer incidence. The framework links nonlinear machine-learning evidence with spatially explicit interpretation for transferable epidemiological analysis.

Abstract: In the central Andes, the use of fire in non-domestic contexts is widespread and deeply rooted in tradition. Due to its sacred nature and transformative power, fire has been an important component of ritual performances, past and present, to cleanse and purify, to diseases, to communicate with powerful invisible agents, and above all, to protect the community. Archaeological evidence from the Acari Valley, on the south coast of Peru, demonstrates that fire was used continuously in ritual contexts from as early as the Initial Period up to the time of the Inka state. Throughout this long period of time, fire was used either to signal the beginning or to mark the culmination of rituals.

Abstract: We study the finite-volume nearest-neighbor energy generated by the symmetric reciprocal-ratio penalty, or equivalently the gradient potential \( V(t)=\cosh t-1 \) in logarithmic variables. Uniform convexity places the model in the standard class of noncompact height theories and yields weighted-Laplacian Hessians, strict convexity on fixed-mean slices, ground-state characterization, and coercive finite-graph bounds. The specific \( \cosh \) form gives several closed-form results. In each cohomology class of oriented edge fields there is a unique minimizing representative, characterized by the nonlinear coclosed equation \( \delta\sinh\omega=0 \), and the sector energy admits a quadratic gap. On twisted discrete tori this representative is explicit, so affine fields are the unique minimizers modulo constants and the finite-volume energy density equals \( \sum_{i=1}^d(\cosh a_i-1) \), independent of torus size. For boxes and discrete tori in \( \mathbb{Z}^d \), the spectral gaps are explicit, giving in \( d=3 \)an \( o(L) \) sufficient condition under which the normalized logarithmic field vanishes in averaged \( L^2 \).