Digital transformation (DT) is widely recognised as a strategic imperative for organisations seeking to sustain competitiveness in increasingly digitalised environments. Despite extensive research, empirical evidence on how digital transformation translates into financial and non-financial performance remains inconclusive. This study addresses this gap by proposing and empirically testing a model that links digital maturity to organisational performance through internal process efficiency as a process-based mediating mechanism. The study draws on survey data from 300 managers in Croatian enterprises across multiple industries and tests the proposed model using structural equation modelling (SEM) to examine both direct and mediated relationships. The results show that both managerial and technological dimensions of digital maturity are positively associated with internal process efficiency, which in turn enhances financial and non-financial performance. The mediating role of internal process efficiency has been confirmed, particularly in contexts where strategic readiness alone does not translate into operational outcomes. The findings indicate that digital maturity should not be approached only as a technological upgrade, but as an organisational transformation that requires integrating strategic intent, technological capabilities, and internal process redesign. The proposed model helps decision-makers align digital investments with process optimisation to improve organisational performance. This study advances the literature by introducing a two-dimensional operationalisation of digital maturity and empirically validating the mediating role of internal process efficiency in a transitional economy context. The findings provide novel empirical evidence on this mechanism within a European transitional economy.

Cyber threat intelligence (CTI) analysts must answer complex questions over large collections of narrative security reports. Retrieval-augmented generation (RAG) systems help language models access external knowledge, but traditional vector retrieval often struggles with queries that require reasoning over relationships between entities such as threat actors, malware, and vulnerabilities. This limitation arises because relevant evidence is often distributed across multiple text fragments and documents. Knowledge graphs address this challenge by enabling structured multi-hop reasoning through explicit representations of entities and relationships. However, multiple retrieval paradigms, including graph-based, agentic, and hybrid approaches, have emerged with different assumptions and failure modes. It remains unclear how these approaches compare in realistic CTI settings and when graph grounding improves performance. We present a systematic evaluation of four RAG architectures for CTI analysis: standard vector retrieval, graph-based retrieval over a CTI knowledge graph, an agentic variant that repairs failed graph queries, and a hybrid approach combining graph queries with text retrieval. We evaluate these systems on 3,300 CTI question-answer pairs spanning factual lookups, multi-hop relational queries, analyst-style synthesis questions, and unanswerable cases. Results show that graph grounding improves performance on structured factual queries. The hybrid graph-text approach improves answer quality by up to 35 percent on multi-hop questions compared to vector RAG, while maintaining more reliable performance than graph-only systems.

Electric power systems require accurate, scalable, distributed, and near real-time state estimation (SE) to support reliable monitoring and control under increasingly complex operating conditions. Limited monitoring capabilities can lead to inefficient operation and, in extreme cases, large-scale disturbances such as blackouts. To address these challenges, this paper proposes a vectorized Gaussian belief propagation (GBP) framework for phasor measurement unit-based SE, formulated over factor graphs and specifically designed to support distributed and near real-time monitoring. The proposed framework includes multivariate and fusion-based GBP formulations. The multivariate formulation jointly models related state variables and their measurement relationships, while the fusion-based formulation reduces factor graph complexity by combining multiple measurements associated with the same set of variables, resulting in a structure that more closely reflects the underlying electrical coupling of the power system. The resulting algorithms operate in a fully distributed manner at the bus level and achieve fast convergence and high estimation accuracy, often within a few iterations, as demonstrated by numerical results on systems ranging from 60 to 13659 buses, where the fusion-based formulation achieves single-digit millisecond iteration times on the largest test case.

Audio media – radio, podcasts, audiobooks – structures everyday life: we keep up, wind down, and share moments through long-form listening. Yet for people living with aphasia – a communication disability that affects audio comprehension – unsupported audio often means losing the thread and marring the experience. While accessibility advances have focused on print, web, and audiovisual content, audio-only remains unconsidered; oftentimes optimised for marketisation rather than sustained understanding. We report a three-week in-situ deployment of Re-Connect app, an audio media player which meets the people at the moment of comprehension difficulty. With ten adults living with aphasia, we show how people assemble personal repertoires of small, co-present communication cues that repair in the moment and support recall. Grounded in lived experience, we argue for personal, source-proximate scaffolds that help make long-form audio more understandable and enjoyable.

Advances in generative AI have given rise to a growing industry centred on interactive representations of deceased individuals. Within this emerging “digital afterlife industry”, interactive deadbots (IDBs) are presented as hyper-realistic avatars that use a person’s likeness, voice, and personal data to simulate conversational interactions with them. Rapidly moving from a niche experiment to a mainstream phenomenon, IDBs are poised to reshape the ethical, social, legal, and governance landscapes surrounding death, mourning, and digital legacy. This paper examines the disruptive nature of IDB technology through a multidisciplinary lens, using the concept of indeterminacy as its guiding analytical framework and a novel way to conceptualise the unstable field. Rather than advancing a unified understanding of indeterminacy, we introduce a structured analytical map and provisional taxonomy that distinguishes technological, social, philosophical, legal, and regulatory manifestations of indeterminacy in IDBs. By offering a tentative and necessarily selective map of this fluid and nascent field, we explore how indeterminacy and IDBs intersect. The paper examines how IDBs amplify existing forms of indeterminacy and how indeterminacy itself shapes the development and use of these systems across five domains: technological, social, philosophical, legal, and regulatory.

This response to the letter expands the discussion on the evolving demands of peer review for systematic reviews and meta-analyses. We emphasize that the main concern surrounding artificial intelligence is not its limited and disclosed use for language support, but undisclosed application and insufficient human verification, which may compromise citation accuracy, interpretation, and overall trustworthiness. We also argue that similarity reports should be interpreted contextually, particularly in evidence syntheses where standardized methodological language is unavoidable, and that low similarity does not necessarily exclude manuscript manipulation. Finally, we highlight reference verification as a central research-integrity challenge that should not rest on peer reviewers alone. Preserving the credibility of evidence synthesis requires shared responsibility across authors, reviewers, editors, and publishers.

Wood finger joints are widely used in both structural timber and high-quality furniture due to their ability to create long, continuous members from shorter pieces. The mechanical performance of these joints depends not only on the wood species but also on the geometry of the interlocking teeth and the quality of the adhesive bond. This study explores how the geometry of finger joints affects the tensile behavior and fracture characteristics of beech (Fagus sylvatica L.) and oak (Quercus robur L.). Specimens with varying tooth dimensions were tested using a 50 kN universal testing machine from Shimadzu. Key metrics such as ultimate tensile load, effective cross-sectional area, cohesive stress, energy required to cause failure, and fracture energy (Gc) at 0.5, 1.0, and 2.0 mm displacements were systematically measured. The results revealed that beech specimens achieved ultimate tensile loads up to 21,320 N and cohesive stress of 204 MPa, while oak reached 21,631 N with a cohesive stress of 239 MPa. Fracture energy (Gc) values ranged from 0.036 N/mm for beech to 0.051 N/mm for oak, depending on joint geometry. Results show that both the type of wood and the tooth design, including width and length, play a decisive role in joint performance. In general, longer teeth and larger bonded areas improved tensile capacity and increased resistance to fracture. These findings offer deeper insights into the fracture mechanics of hardwood finger joints and provide practical guidance for optimizing glued connections in furniture and structural timber. The collected data can also support accurate modeling, quality assurance, and numerical simulations in future studies.

Toxicity and harassment are widespread in the video-gaming context. Especially in competitive online multiplayer scenarios, gamers oftentimes send harmful messages to other players (teammates or opponents) whose consequences span from mild annoyance to withdrawal and depression. Abundant prior work tackled these problems, e.g., pointing out the negative effects of toxic interactions. However, few works proposed countermeasures specifically developed and tested on textual messages sent during a match -- i.e., when the"harassment"actually occurs. We posit that such a scarcity stems from the lack of high-quality datasets that can be used to devise"automated"detectors based on natural-language processing (NLP) and machine learning (ML), and which can -- ideally -- mitigate the harm of toxic comments during a gaming session. This work provides a foundation for addressing the problem of toxicity and harassment in video games. First, through a systematic literature review (n=1,039), we provide evidence that only few works proposed ML/NLP-based detectors of toxicity/harassment during live matches. Then, we partner-up with 8 expert League of Legend (LoL) players and create a fine-grained labelled dataset, L2DTnH, containing 1.4k toxic and 13.8k non-toxic messages exchanged during LoL matches. We use L2DTnH to develop a detector that we then empirically show outperforms general-purpose and state-of-the-art toxicity detectors reliant on NLP. To further demonstrate the practicality of our resources, we test our detector on game-related data beyond that included in L2DTnH; and we develop a Web-browser extension that flags toxic content in Webpages -- without querying third-party servers owned by AI companies. We publicly release all of our resources. Our contributions pave the way for more applied research devoted to fighting the spread of toxicity and harassment in video games.

Introduction: Fear of childbirth (FOC) is a common concern during pregnancy that can negatively affect women’s well-being and childbirth experiences. Understanding how different dimensions of FOC relate to one another before and after prenatal interventions may help optimize supportive care. The aim of this study was to examine correlations among specific domains of childbirth fear before and after participation in a prenatal childbirth preparation program. Methods: This prospective longitudinal study included 97 pregnant women with uncomplicated pregnancies who participated in a one-month prenatal childbirth preparation program between November 2024 and February 2025. The intervention consisted of theoretical education and physical exercise sessions, held twice a week. FOC was assessed before and 7 days after the intervention using the Childbirth Fear Questionnaire (CFQ). Spearman correlation coefficients were used to examine relationships among CFQ subscales. Results: Participants had a mean age of 30.5 ± 3.8 years and a mean gestational age of 32.5 ± 3.0 weeks at the time of study entry. Before the intervention, the total CFQ score was most strongly correlated with fear of medical interventions (p = 0.823). After the intervention, the strongest association shifted to fear of pain during vaginal birth (p = 0.859). Conclusion: Following participation in the prenatal childbirth preparation program, the pattern of associations among childbirth fears changed, with fear of medical interventions becoming less dominant and fear of pain during vaginal delivery emerging as a central concern. These findings suggest that prenatal interventions may influence not only the intensity but also the structure of childbirth-related fears, highlighting the importance of addressing multiple fear dimensions simultaneously.

Background Antimicrobial resistance (AMR) is an appreciable public health threat, exacerbated by considerable inappropriate use of antibiotics including for upper respiratory tract infections (URTIs). Whilst there have been high levels of inappropriate prescribing of antibiotics in primary care in South Africa, study findings vary regarding the extent of dispensing of antibiotics without a prescription. Where this occurs, this is typically for patients with urinary tract infections (UTIs) and sexually transmitted infections (STIs). Consequently, there is a need to update knowledge regarding antibiotic dispensing patterns in primary care in South Africa alongside key factors influencing this. The findings can provide future direction to key stakeholders in South Africa grappling with high AMR rates. Methods A previously piloted questionnaire was administered to patients leaving community pharmacies in a rural province using their preferred language. The questionnaire collected data on current antibiotic utilisation patterns alongside their knowledge and attitudes towards AMR. Results 465 patients were interviewed exiting community pharmacies with a medicine. 78.7% of patients who were dispensed antibiotics were dispensed these without a prescription. Perceived STIs were the most common infectious disease where this occurred, with 99.1% of antibiotics issued for this condition dispensed without a prescription. Only 1 out of 116 patients with a perceived STI, received an antibiotic from a prescription issued by an authorized prescriber. The reverse was seen with patients with URTIs where there was very little dispensing of antibiotics without a prescription for these patients. This may be because surveyed patients were prepared to take advice from community pharmacists, who typically offered symptomatic relief to patients with suspected URTIs. This situation contrasts with antibiotics from prescriptions where URTIs were the most common infection where antibiotics were prescribed (59.3%). Questioning patients in their own language enhanced their understanding of key issues. Conclusion There is an urgent need to re-consider community pharmacist activities in South Africa with some countries allowing them to prescribe antibiotics for UTIs. Trained community pharmacists can also potentially engage with patients to help prevent and manage STIs with patients appearing to preferentially seek assistance from community pharmacists for their perceived STIs. Community pharmacists can also potentially work with prescribers to improve their antibiotic use especially for URTIs.

ABSTRACT Background Studying the hyoid bone in dogs is of significant importance in veterinary surgery and anatomy, as it aids in understanding how variations in this structure may affect tongue mobility, swallowing and vocalisation across breeds. Objectives This study aims to provide preliminary insights into the relationship between structural differences in the hyoid bone and breed‐specific functional adaptations in tongue shape and movement by analysing shape variations within the hyoid apparatus. Methods Hyoid bones from computed tomography images of 26 dogs were modelled, and principal component analysis (PCA) was conducted to examine shape variation in the hyoid bones. Additionally, the influences of age and weight on hyoid bone shape were assessed. Results PCA showed that PC1 (42.4%) reflected a relatively conservative pattern related to hyoid and skull morphology, whereas PC2 and PC3 indicated greater individual variation. Brachycephalic breeds exhibited a more dorsoventrally positioned and compact hyoid structure, while mesocephalic breeds showed a more aligned and elongated configuration of the stylohyoid and thyrohyoid bones. No significant correlations were found between hyoid shape and age, weight or Procrustes distance, suggesting a stronger influence of genetic factors. Conclusions Understanding the morphological variation of the hyoid bone in dogs contributes to veterinary anatomy and has practical applications in veterinary medicine, particularly in surgical and rehabilitation practices. Given the hyoid apparatus's critical role in swallowing and vocalisation, insights from this study may enhance clinical approaches to treating conditions linked to hyoid bone morphology.

We study closed-loop stability and suboptimality for MPC and infinite-horizon optimal control solved using a surrogate model that differs from the real plant. We employ a unified framework based on quadratic costs to analyze both finite- and infinite-horizon problems, encompassing discounted and undiscounted scenarios alike. Plant-model mismatch bounds proportional to states and controls are assumed, under which the origin remains an equilibrium. Under continuity of the model and cost-controllability, exponential stability of the closed loop can be guaranteed. Furthermore, we give a suboptimality bound for the closed-loop cost recovering the optimal cost of the surrogate. The results reveal a tradeoff between horizon length, discounting and plant-model mismatch. The robustness guarantees are uniform over the horizon length, meaning that larger horizons do not require successively smaller plant-model mismatch.

Cell-Free Massive Multiple-Input Multiple-Output (CF-MaMIMO) in Open Radio Access Network (O-RAN) promises high spectral efficiency but is limited by frequent Channel State Information (CSI) exchanges, which strain fronthaul/midhaul/backhaul (X-haul) bandwidth and exceed the capabilities of existing approaches relying on uncompressed CSI or heavy predictors. To overcome these constraints, we propose LITE, a lightweight pipeline combining a 1-D convolutional Autoencoder (AE) at the O-RAN Distributed Unit (O-DU) with a Squeeze-and-Excitation (SE)-enhanced Bidirectional Long Short-Term Memory (BiLSTM) predictor at the Near-Real-Time RAN Intelligent Controller (Near-RT-RIC), enabling short-horizon trajectory-unaware forecasting under strict transport and processing budgets. LITE applies 50 % CSI compression and an asymmetric SE-BiLSTM, reducing model complexity by 83.39 % while improving accuracy by 5 % relative to a baseline BiLSTM. With compression-aware training, the Lightweight Intelligent Trajectory Estimator (LITE) incurs only 6 % accuracy loss versus the BiLSTM baseline, outperforming independent and end-to-end strategies. A TensorRT-optimized implementation achieves $147 k$ Queries per Second (QPS), a 4.6x throughput gain. These results demonstrate that LITE delivers X-haul-efficient, low-latency, and deployment-ready channel-gain prediction compatible with O-RAN splits.

We investigate the asymptotic behavior of a proposed ordinary differential equation (ODE) model for Genetic Toggle switches from Gardner et. al. and I. Rajapakse and S. Smale: dxdt=a1+ym−x and dydt=b1+xn−y where a,b,m,n>0 and x(t),y(t)≥0. We also investigate the asymptotic behavior of the Euler discretization of this system: xn+1=a1xn+b11+ynm=f(xn,yn) and yn+1=a2yn+b21+xnn=g(xn,yn), where 1−h=a1, 1−k=a2, ah=b1 and bk=b2, a1,a2∈(0,1) and h,k>0 are steps of discretizations. Here, x and y represent protein concentrations at a particular time in both genes and a,b,m,n>0, respectively, above. We will apply the theory of competitive maps to find the basins of attractions of different equilibrium points and period-two solutions of systems of difference equations.