Key Characteristics Research Articles

Employee ownership trusts: an employee ownership success story

ABSTRACT Since its creation in 2014, the employee ownership trust (EOT) has become a widespread form of employee ownership in the UK, and has transformed the country’s employee ownership scene. Given that it is over a decade since its inception into UK legislation, and with the EOT now firmly established as a key business succession tool, it is timely to take a closer, critical look at the role, characteristics, and potential dangers of the UK EOT model. This paper will briefly outline and discuss the key characteristics of EOTs, how the EOT came about, and why it has been so successful. Using several unique data-sets of UK employee-owned businesses, it will profile key features of EOTs, and show how the flexible approach of the EOT provides the potential to sidestep many of the obstacles faced by other forms of employee ownership.

Use of mobile health units in natural disasters: a scoping review

Abstract Background Mobile Health Units have become important resources for healthcare delivery to dispersed populations following natural disasters. However, criticism regarding their operational flexibility, health coverage and cost-effectiveness remain unaddressed. There are few studies evaluating their usefulness in natural disasters and deployment reports have never been included in peer-reviewed publications. With an expected rise in weather-related disasters, knowledge about the impact of MHUs on addressing health needs is needed. This study aimed to elucidate the use of mobile health units in natural disasters as described in the literature. Methods A scoping review was conducted, searching twenty-six databases and websites. Documents detailing operational characteristics and practices of mobile health units deployed to natural disasters, published between 2000 and 2022 in English, were included. Findings were analysed using thematic content analysis with the World Health Organization Classification and Minimum Standards for Emergency Medical Teams as a guiding framework. Results Nearly 3000 documents were screened, yielding thirteen documents eligible for inclusion. The literature highlighted seven themes: key characteristics, operational availability, services, benchmark indicator, staff, self-sufficiency and pre-deployment preparations. The reports cover earthquakes, floods, tsunamis, hurricanes, typhoons, cyclones, landslides and mudslides. Mobile health units were described to improve access to outpatient healthcare for populations with limited access to routine services. However, limitations related to mobility, logistics, referral capacity, health coverage and communication posed significant challenges. Conclusions Data on the use of mobile health units in natural disasters is scarce with inconsistent reporting of key aspects, stressing a need for uniform reporting. In response to inaccessible fixed healthcare facilities, mobile health units were described to address the normal burden of disease rather than emergency care. Coordination, transportation, referral systems and data collection were highlighted as the main areas of improvement. Trial registration Not applicable.

Abstract B033: Patient-derived 3D organoids for drug screening in cutaneous neurofibroma: A personalized approach to treatment discovery

Abstract Neurofibromatosis type 1 (NF1) is a common genetic disorder, affecting approximately 1 in 3,500 individuals worldwide. Mutations in the NF1 gene impair the function of the tumor suppressor protein neurofibromin, which plays a crucial role in regulating cell proliferation and differentiation. Alterations of NF1 lead to dysregulation of the RAS signaling pathway and uncontrolled cell proliferation. Approximately 90% of NF1 patients develop cutaneous neurofibromas (cNFs)—benign skin tumors Although benign, cNFs cause substantial morbidity and quality of life deterioration for affected individuals. No approved therapies for cNFs currently exist, with surgery as the only non-curative treatment option. Drug development for cNFs is complicated by a lack of models that recapitulate the complexity of their composition. We have developed a platform to rapidly establish and screen patient-derived 3D cNF organoids. By mirroring key characteristics of the original tumors, including cellular complexity and heterogeneity, cNF organoids match patient tumor’s characteristics at the cellular and molecular level. We are conducting drug screenings on individual tumor from every patient to identify compounds that inhibit cNF tumor cell proliferation, and identifying biomarkers predictive of drug response. We have enrolled 22 NF1 patients undergoing cNF removal as part of their clinical care. Preliminary drug screening results indicate variable drug sensitivities among patients, with notable dose-dependent responses to Onalespib (an Hsp90 inhibitor) and Digoxin (a Na+/K+ ATPase inhibitor). By integrating drug response data with proteogenomic profiles, we aim to leverage a machine learning-based biomarker discovery pipeline, to identify specific transcripts, proteins, and phosphosites associated with drug sensitivities. Citation Format: Huyen Thi Lam Nguyen, Alberto Mendoza Valderrey, Summer Norris, Andre Panossian, Sara Gosline, Paul Piehowski, Hutchinson Chelsea, Alice Soragni. Patient-derived 3D organoids for drug screening in cutaneous neurofibroma: A personalized approach to treatment discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B033.

The nexus of firms characteristics and tax avoidance – do independent directors have a role? Evidence from Bangladesh

PurposeThis research investigates the moderating impact of independent directors on the relationship between tax avoidance and the key characteristics of the listed firms, namely profitability, company size and leverage in an emerging nation setting.Design/methodology/approachThe generalised method of moments (GMM) technique, dynamic ordinary least squares and second-generation unit root test are used in this study. A long-term cointegration of the variables is supported by the Kao residual cointegration test.FindingsThe current study finds that a company’s profitability, firm size and independent directors have a considerable favourable impact on corporate tax avoidance. In addition, independent directors have a negative and significant moderating effect on the association between firm size, profitability and tax avoidance. This relationship implies that the presence of independent directors can lessen the tax avoidance practice of business firms.Research limitations/implicationsOur findings highlight the importance of including independent directors to improve corporate governance, reduce tax avoidance and make better-informed investment decisions. It also highlights the role of policymakers and regulatory bodies in implementing laws, providing guidance and transparency and ensuring active shareholder engagement.Originality/valueAs far as the authors are aware, this is the first research conducted in Bangladesh to examine the influence of independent directors on a company’s likelihood of engaging in tax avoidance.

High Accuracy of Epileptic Seizure Detection Using Tiny Machine Learning Technology for Implantable Closed-Loop Neurostimulation Systems

Background: Epilepsy is one of the most common and devastating neurological disorders, manifesting with seizures and affecting approximately 1–2% of the world’s population. The criticality of seizure occurrence and associated risks, combined with the overwhelming need for more precise and innovative treatment methods, has led to the development of invasive neurostimulation devices programmed to detect and apply electrical stimulation therapy to suppress seizures and reduce the seizure burden. Tiny Machine Learning (TinyML) is a rapidly growing branch of machine learning. One of its key characteristics is the ability to run machine learning algorithms without the need for high computational complexity and powerful hardware resources. The featured work utilizes TinyML technology to implement an algorithm that can be integrated into the microprocessor of an implantable closed-loop brain neurostimulation system to accurately detect seizures in real-time by analyzing intracranial EEG (iEEG) signals. Methods: A dataset containing iEEG signal values from both non-epileptic and epileptic individuals was utilized for the implementation of the proposed algorithm. Appropriate data preprocessing was performed, and two training datasets with 1000 records of non-epileptic and epileptic iEEG signals were created. A test dataset with an independent dataset of 500 records was also created. The web-based platform Edge Impulse was used for model generation and visualization, and different model architectures were explored and tested. Finally, metrics of accuracy, confusion matrices, and ROC curves were used to evaluate the performance of the model. Results: Our model demonstrated high performance, achieving 98% and 99% accuracy on the validation and test EEG datasets, respectively. Our results support the use of TinyML technology in closed-loop neurostimulation devices for epilepsy, as it contributes significantly to the speed and accuracy of seizure detection. Conclusions: The proposed TinyML model demonstrated reliable seizure detection in real-time by analyzing EEG signals and distinguishing epileptic activity from normal brain electrical activity. These findings highlight the potential of TinyML in closed-loop neurostimulation systems for epilepsy, enhancing both speed and accuracy in seizure detection.

Power System Security Monitoring: Quantum Computing Aided Anomaly Detection and Early Warning System

Power system security is a critical concern for modern energy grids, where both natural disturbances and human-made events can lead to system failures or significant disruptions. Effective monitoring is essential to maintain stability, reduce risks, and ensure the system’s reliable operation. This study aims to develop an advanced security monitoring framework for power systems using quantum computing to enhance anomaly detection and establish an early warning system. It focuses on identifying and mitigating disturbances through a robust model that improves system security and resilience. The dataset used for this study is sourced from phasor measurement units (PMUs), which are critical for identifying anomalies. The z-score normalization approach was used to preprocess the dataset. Wavelet transforms and statistical techniques are combined to extract key characteristics, which aids in finding patterns and trends that were necessary for spotting abnormalities. The stochastic fractal search-driven quantum-enhanced scalable support vector regression (SFS-QE-SSVR) is designed to improve prediction accuracy by optimizing support vector regression models. The quantum enhancement boosts the algorithm’s performance by accelerating the search process for optimal parameters. The system detects anomalies based on deviations from normal patterns and triggers early warnings, allowing operators to take preventive actions before critical failures occur. The model demonstrates superior accuracy in detecting anomalies and cyber-attacks, outperforming traditional machine-learning methods. In a comparative analysis, the suggested method is assessed with various evaluation measures, such as [Formula: see text]-score (98.15%), recall (97.10%), precision (98.35%) and accuracy (98%). The result demonstrated that the SFS-QE-SSVR method improves the prediction accuracy by optimizing support vector regression models. The proposed quantum-enhanced approach significantly enhances power system security and efficiency, providing a reliable solution for modern energy grids.

Imperial Origins of the Large Corporation: How the Department of War Helped Build the First US Railroads

In this article, I argue that leaders of the U.S. Department of War and U.S. Army developed the organizational form and management practices of the modern corporation, decades before the advent of the railroads. Following Mark R. Wilson’s call to “bring the military in” to organizational analysis, I show how leaders of the U.S. military developed modern management practices and organizational structures as a way of maintaining control over officers, soldiers, and workers over long distances, as they provided the organized violence necessary for domestic imperialist expansion. By the time that elite merchants and real estate interests in the Atlantic port cities of the U.S. became interested in building railroads, in the late 1820s and 1830s, the U.S. Army already evidenced the key characteristics of modern business enterprise as defined by Alfred Chandler: a multi-unit organization coordinated by a hierarchy of professional, salaried, career-oriented middle and top managers. All the characteristic coordination mechanisms of the corporation: staff and line hierarchies, divisional and departmental structure, and bureaucratic systems of information gathering, surveillance, and control, were developed by the state in the course of building a continental empire.

Strain-Tunable Electronic Properties of 2D-B9: A Borophene Allotrope with Mechanical Flexibility

Abstract In this work, we explore the structural, mechanical, and electronic properties of 2D-B9, a borophene allotrope with a unique bonding structure and promising potential for strain engineering. Through first-principles calculations, we investigate the material's stability, revealing a robust phonon spectrum and favorable mechanical flexibility, including isotropic behavior and a moderate Young’s modulus. The electronic structure of 2D-B9 features key characteristics such as a van Hove singularity (vHS) and a Dirac cone, which can be dynamically tuned via strain. Under tensile strain, the vHS shifts downward, while compressive strain causes it to rise, with the vHS aligning with the Fermi level at 10% compression. This strain-induced tuning of the electronic structure is further confirmed by examining changes in Fermi velocity, which is found to be similar to that of graphene at 9 × 105 m/s, indicating high electronic mobility. These results highlight the potential of 2D-B9 for applications in flexible electronics, quantum devices, and other technologies where strain-sensitive electronic properties are essential.

TRANSFORMATION OF TRADITIONAL TO MODERN LEADERSHIPS IN THE VUCA ENVIRONMENT

Objective: This thesis aims to examine the VUCA phenomenon's impact on leadership by tracing its evolution and aligning it with key characteristics of five leadership styles. It highlights the shift from traditional to modern leadership and the need for leadership traits that in response to the complexities and ambiguities of the external environment. Theoretical Framework: Contemporary research expands leadership beyond authority, and this paper studies how leaders have adapted to these changes by analyzing the shift in leadership roles in response to the VUCA environment over recent decades. Method: Literature review-based method emloyed to explain the ongoing shift from traditional to modern, the changes in role of leaders over the past decades by studying how different leadership styles can respond to the changes in VUCA environment Results and Discussion: The findings show that effective leadership today requires not only authority but also the ability to influence, reassure, and support teams through uncertainty. This shift helps firms better address VUCA challenges and navigate future uncertainties. Research Implications: This paper stresses the importance of flexibility in leadership theories to navigate the VUCA environment. It emphasizes key leadership traits that help businesses, from SMEs to large enterprises, adapt to today’s dynamic business landscape, fostering open-mindedness in leaders and advocating for tailored strategies to ensure business survival. Originality/Value: This study enriches academic leadership theory, offering new perspectives on change and the survival of traditional leadership in modern times.

Analysis of Circuit Configurations Suitable for Self-Supplied AC-DC Converters Using Thin-Film Piezoelectric Generators and Multilayer Energy Storage Supercapacitors

The improvement of microelectronic technologies and the practical application of some new materials has resulted in the realization of various highly efficient thin-film energy harvesters in the last few years. Self-powered supplies intended to work with thin-film harvesters have been developed. This type of power supply with integrated various thin-film harvesters has proven to be very suitable for providing electrical energy for wearable electronic sensor systems, with practical applications for implementing personalized medicine through continuously monitoring an individual’s state of health. The application of wearable electronics in medicine will become increasingly important in the next few years, as it can support timely decision-making, especially in high-risk patients. This paper presents a review and comparative analysis of the optimal circuit configurations used to design power supply devices with discrete and integrated components, obtaining electrical power from various thin-film piezoelectric generators, and storing electrical energy in low-power multilayer supercapacitors. Based on an analysis of the principle of operation of the selected circuit configurations, analytical expressions for the basic static and dynamic parameters have been obtained, taking into account the peculiarities of their integration with the biomedical signal processing system. Advantages and weaknesses are analyzed through simulation testing for each configuration, as the prospects for improvement are outlined. Also, for each group of circuit configurations, the key parameters and characteristics of recent high-impact papers, especially those focusing on low-power applications, are presented and analyzed in tabular form. As a result of the analysis of the various circuit configurations, some analytical recommendations have been defined regarding the optimal selection of passive and active elements, which can contribute to a better understanding of the design principles of battery-free power supplies converting electrical energy from some specific recently developed thin-film energy harvesters.

Calculations of micropulse mode parameters regarding the age and type of appearance by Fitzpatrick scale for selective micropulse individual retinal therapy (SMIRT) on serial lasers

Purpose: to develop formulas for selecting parameters for each patient regarding their age and type of appearance by the Fitzpatrick scale. To obtain the formulas after testing the selective micropulse mode by autofluorescent (AF) results would allow to treat patients using the technology of selective micropulse individual retinal therapy (SMIRT) without preliminary testing on serial laser systems.Materials and methods: 97 patients with acute central serous chorioretinopathy, aged 30–65, with type 1 to type 4 appearance by the Fitzpatrick scale were enrolled in the study. The obtained results after the testing of selective micropulse mode (spot diameter – 100 microns, micropulse duration 50–150 μsec, duty cycle – 0.5–5 %, pulse packet duration – from 10 to 50 ms (1–5 pulses per packet), power –1–2 W) were analyzed. Laser system Iridex IQ 577 was used in the trial. For each laser spot, the probability of damage detection (PDD) was calculated with shortwave AF (488 nm) data.Results. On analyzing 4685 spots by AF findings, PDD logistic regression function was constructed using the likelihood maximization method on a training sample depending on micropulse mode parameters, age and type of appearance by Fitzpatrick scale. The resulting formula was inverted to predict the required power, pulse duration, and number of pulses to achieve the required PDD level. Errors in the logistic regression coefficients were obtained by bootstrapping. The regression quality was assessed using Pearson’s chi-squared test. While comparing the ogistic function and computer modeling, it has been shown that age and type of appearance are key personal characteristics which must be taken into account when selecting parameters of micropulse mode treatment.Conclusion. The authors have developed formulas for selecting parameters of selective micropulse mode regarding the patient’s age and type of appearance by Fitzpatrick scale without preliminary testing for treatment with SMIRT technique on serial laser systems.

Farmers’ Willingness to Achieve Energy Self-Sufficiency in Kosovo

Agriculture accounts for a significant economic share in less-developed countries, especially Kosovo, where there is a lignite-dominated energy supply. Lignite’s partial replacement with locally produced renewable energy sources could play an essential role in reducing farmers’ costs and preparing the country for EU accession. Using a sample of 120 farmers, the Best–Worst Scaling (BWS) technique was used to assess farmers’ preferences for renewable energy applications and to measure the importance of seven key characteristics associated with the willingness to become energy self-sufficient. The results show a significant preference for “lower energy costs” and “environmental friendliness”. Using cluster analysis, it is shown in a statistically reliable way that while the decisions of smaller farms are influenced by economic factors and the role of energy self-sufficiency is negligible, a non-negligible share of larger farms already have already adopted self-sufficiency in energy production (solar panels, byproducts) and also consider the environment and convenience aspects necessary in their decisions. Farmers play an important role in local economic development. Therefore, regulatory schemes with differentiation by farm size may play an important role in promoting local energy management in Kosovo and similar less developed countries.

Citrulline regulates macrophage metabolism and inflammation to counter aging in mice.

Metabolic dysregulation and altered metabolite concentrations are widely recognized as key characteristics of aging. Comprehensive exploration of endogenous metabolites that drive aging remains insufficient. Here, we conducted an untargeted metabolomics analysis of aging mice, revealing citrulline as a consistently down-regulated metabolite associated with aging. Systematic investigations demonstrated that citrulline exhibited antiaging effects by reducing cellular senescence, protecting against DNA damage, preventing cell cycle arrest, modulating macrophage metabolism, and mitigating inflammaging. Long-term citrulline supplementation in aged mice yielded beneficial effects and ameliorated age-associated phenotypes. We further elucidated that citrulline acts as an endogenous metabolite antagonist to inflammation, suppressing proinflammatory responses in macrophages. Mechanistically, citrulline served as a potential inhibitor of mammalian target of rapamycin (mTOR) activation in macrophage and regulated the mTOR-hypoxia-inducible factor 1α-glycolysis signaling pathway to counter inflammation and aging. These findings underscore the significance of citrulline deficiency as a driver of aging, highlighting citrulline supplementation as a promising therapeutic intervention to counteract aging-related changes.

Graph Foundation Models: Concepts, Opportunities and Challenges.

Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains. Meanwhile, the field of graph machine learning is witnessing a paradigm transition from shallow methods to more sophisticated deep learning approaches. The capabilities of foundation models in generalization and adaptation motivate graph machine learning researchers to discuss the potential of developing a new graph learning paradigm. This paradigm envisions models that are pre-trained on extensive graph data and can be adapted for various graph tasks. Despite this burgeoning interest, there is a noticeable lack of clear definitions and systematic analyses pertaining to this new domain. To this end, this article introduces the concept of Graph Foundation Models (GFMs), and offers an exhaustive explanation of their key characteristics and underlying technologies. We proceed to classify the existing work related to GFMs into three distinct categories, based on their dependence on graph neural networks and large language models. In addition to providing a thorough review of the current state of GFMs, this article also outlooks potential avenues for future research in this rapidly evolving domain.

Realizing the Ultrafast Recovery of the Monolayer MoS2-Based NH3 Sensor by Gas-Ion-Gate.

Over the past decade, the two-dimensional material MoS2 has attracted great attention due to its room temperature stability, high surface area-to-volume ratios, abundance of active sites at the edges, and exceptional surface sensitivity to the environment, which are key characteristics for applications in chemical sensing. However, the slow recovery of ammonia (NH3) sensors based on MoS2 materials at room temperature has restricted their further development. This paper presents a novel approach to address this limitation by demonstrating a monolayer MoS2-based NH3 sensor with rapid recovery capabilities, leveraging O2- generated through gas-ion-gate (GIG) technology. After being modulated by GIG, the recovery time of the monolayer MoS2 device is about 2.04 s, representing a substantial improvement─approximately 157 times faster than that without GIG modulation. The rapid recovery speed is attributed to the chemical interactions between the O2- ions generated by GIG and the surface-adsorbed NH3+, which expedite the rapid desorption of NH3+. Furthermore, the device retains periodic recovery properties, even after multiple modulation cycles, indicating that the material remains undamaged. This method successfully resolves the critical issue of slow recovery in monolayer MoS2-based NH3 sensors at room temperature.

Thin Film Piezoelectric Acoustic Emission Sensor with High Sensitivity Up To 650∘C.

This paper reports a high-temperature acoustic emission (AE) sensor enabling couplant-free and waveguide-free attachments on structures operating up to 650◦C. The microfabricated sensor is constructed using silicon carbide as the substrate and aluminum nitride (AlN) as the piezoelectric film. The piezoelectric coefficient (d33) of AlN is measured using piezoresponse force microscopy as 3.62 pm/V. The sensor exhibits an impedance response of ∼1kΩ in the 100 kHz to 300 kHz frequency range, which is below the input impedance of conventional AE systems, causing a slight reduction in amplitude. Following the sensitivity and impedance characterizations, the sensor is tested inside a furnace at temperatures ranging from room temperature up to 650◦C. Pencil lead break and ball drop tests are used to simulate AE sources. The sensor is dry-coupled to the test surface using high-temperature wires and a stainless-steel fixture. The sensor sensitivity decreases slightly with increasing temperature, with a maximum reduction of 6 dB at 650◦C. The sensor is evaluated for detecting creep damage in 316L stainless steel and demonstrated performance comparable to conventional sensors attached with waveguides. Compared with conventional bulk AE sensors, the key characteristics of this AlN-based thin-film AE sensor are its high-temperature functionality and couplant-free attachment, enabling direct placement near critical systems under elevated temperatures. This positioning mitigates the influences of long wave paths introduced by waveguides, enhancing the sensor's effectiveness in detecting the initiation and progression of damage. The developed sensor leverages the advantages of microfabrication, offering benefits such as mass production, low cost, and a compact footprint.

Inch‐sized K[NH2(CH2COO)2]: a High‐Performance UV Nonlinear Optical Crystal Enabled by Novel Flexible π‐Conjugated Building Units, [NH2(CH2COO)2]−

AbstractHigh‐performance ultraviolet (UV) nonlinear optical (NLO) crystals must exhibit three critical properties: a large second‐harmonic generation (SHG) response, adequate birefringence, and a short UV cutoff edge. However, these key characteristics are often interdependent and challenging to achieve simultaneously within a single crystal. In this study, a novel non‐centrosymmetric compound, K[NH2(CH2COO)2] is introduced, synthesized from the innovative flexible π‐conjugated group [NH2(CH2COO)2]−. Benefiting from the excellent microscopic optical properties of the flexible π‐conjugated [NH2(CH2COO)2]− groups, despite the suboptimal arrangement of groups within the compound, K[NH2(CH2COO)2] still demonstrates a remarkable balance in its linear and nonlinear optical performance. This includes a strong SHG response of 3 times that of KDP, suitable birefringence of 0.088@1064 nm, and a short UV cutoff edge at 208 nm. Emphatically, high‐quality, inch‐sized single crystals (up to 60 × 46 × 30 mm3) are successfully grown using a water solution technique, paving the way for practical applications of this promising material.

Variant mode and effects analysis for effective product family expansion under modular architecture

A product family built on a modular architecture has the advantage of efficiently expanding its product range through selective differentiation of modules while reusing others. However, the purposes and capabilities of existing module variants are often unclear, making their reuse difficult and leading to unnecessary creation of additional variants. This study introduces Variant Mode and Effects Analysis (VMEA), a structured framework for module differentiation that discloses key variant characteristics such as the mode, cause, and impact of differentiation. The framework provides a step-by-step process for capturing these characteristics and a documentation scheme for their records. We demonstrated the effectiveness of the framework through a case study of an automotive suspension system. The accumulated records of the module variants serve as a catalogue for reuse, enhancing their reusability. Furthermore, it offers valuable insight into architectural reforms by identifying compatibility bottlenecks and historical differentiation patterns.

Retrospective Analysis of Premature Infant Characteristics: Risk Factors, Complications, and Predictive Insights

Premature birth remains a significant global health challenge, contributing to high rates of neonatal morbidity and mortality. This retrospective study analyzes the key characteristics, risk factors, complications, and predictive outcomes affecting preterm infant outcomes. Utilizing established theoretical frameworks—including the Developmental Origins of Health and Disease (DOHaD), the Bioecological Model, and the Neurodevelopmental Framework this research examines the interplay of prenatal, perinatal, and postnatal influences on neonatal health. A comprehensive analysis of neonatal morbidity trends from 2018 to 2020 highlights both advancements and persistent challenges in neonatal care. By integrating clinical data with theoretical insights, this study provides a refined understanding of the factors influencing preterm infant health. The findings offer valuable implications for improving neonatal risk assessment models, optimizing clinical interventions, and enhancing long-term developmental outcomes for preterm infants.

СКЛАДОВІ ІМІДЖУ ВИДАВНИЦТВА: НА ПРИКЛАДІ ВИДАВНИЦТВА «НАШ ФОРМАТ»

<p><strong><em>Research objective</em></strong><em>. The study aims to conduct a comprehensive analysis of the components shaping the image of the publishing house «Nash Format» and to determine their influence on the development and maintenance of the brand in the modern Ukrainian book market. Special attention is paid to evaluating brand perception among the target audience and assessing the effectiveness of implemented PR strategies.</em></p><p><strong><em>Research methodology.</em></strong><em> The study employs a comprehensive approach, incorporating content analysis and case study methods. Content analysis was used for a systematic examination of materials reflecting the public activity of the publishing house, including its communication on social media, advertising campaigns, and press releases. This approach enabled the assessment of the brand's key characteristics, audience engagement levels, and core messages shaping its image. The case study method provided an in-depth exploration of specific events critically impacting the publisher's reputation. </em><em>In particular, it examined situations related to leadership changes, the introduction of corporate volunteering, and responses to crisis challenges.</em></p><p><strong><em>Results.</em></strong><em> The research identified several key components shaping the image of «Nash Format». First, a strategic branding approach focused on promoting high-quality literature, particularly in the nonfiction and business genres, provides a competitive advantage. Second, active digital communication, particularly through social media, is a crucial factor in engaging the target audience and enhancing brand recognition. Additionally, participation in social initiatives, such as corporate volunteering, positively influences the publisher's reputation. However, some communication gaps were noted in crisis situations, such as during leadership transitions.</em></p><p><strong><em>Novelty.</em></strong><em> This article systematizes the components of «Nash Format's» image for the first time, considering the integration of digital communications and social initiatives. It outlines the interaction mechanisms of internal and external factors influencing brand formation and maintenance, enabling an evaluation of the effectiveness of applied PR tools.</em></p><p><strong><em>Practical significance.</em></strong><em> The findings of this study can be utilized by other publishing houses to develop and optimize their PR strategies. Particular emphasis is placed on the effective use of new media to enhance brand recognition and on crisis communications aimed at maintaining reputational stability.</em></p><p><strong><em>Key words:</em></strong><em> publishing house image, branding, PR strategies, content analysis, case study, media communications, social responsibility.</em></p>