Mathematics serves as the fundamental language of science, technology, and modern civilization. This comprehensive report examines the extensive real-world applications of mathematical principles across diverse fields including engineering, medicine, finance, technology, environmental science, and social sciences. Through detailed analysis of case studies and contemporary examples, this research demonstrates how mathematical concepts ranging from basic arithmetic to advanced calculus, statistics, and discrete mathematics directly impact daily life and drive innovation in the 21st century. The report provides evidence-based documentation of mathematical applications, supported by extensive literature review and current research findings. Key findings indicate that mathematical literacy and application are essential for addressing complex societal challenges including climate change, healthcare optimization, financial stability, and technological advancement.

The article describes the assessment of the impact of artificial intelligence on the efficiency of diagnostics and repair of household appliances. The relevance of the topic lies in the growing complexity of modern equipment, which nowadays includes many different sensors, microprocessors and control systems. The novelty of the study lies in the study and correlation with predictive maintenance of such key performance indicators as the accuracy of fault detection, reduction in repair time and optimization of maintenance costs. The study analyzed data from sensors, error codes and historical usage logs. A comparison of traditional diagnostics and diagnostics using AI was also conducted. The purpose of the study is to demonstrate how to reduce operating costs and improve service quality by applying automation and machine learning methods. The study used a comparative method, statistical data processing and case study analysis. The results confirm the potential of AI in predictive maintenance and its optimization. The conclusions describe the economic benefits of AI integration and its implementation prospects in the field of household appliance repair. The article will be useful for service companies, engineers and researchers studying AI - based solutions in technical diagnostics and maintenance.

This article analyses the effectiveness of non-mass (niche) strategies in freight transportation, examining their potential as a tool to counter dumping and ensure the competitiveness of small and medium-sized enterprises (SMEs). The study aims to evaluate the potential of niche, client-oriented approaches in creating a distinctive value proposition that enables companies to avoid price competition and remain resilient in the face of market conditions. The research employs a comparative analysis of mass and non-mass strategies, as well as an empirical examination of case studies from companies that use specialised logistics models. Key performance indicators such as profitability margin, customer loyalty and operational resilience have been evaluated. The results show that non-mass strategies, such as geographic specialisation, service differentiation, personalised pricing and integration into clients' business processes, provide higher profit margins than mass models, which depend on the scale of operations. The study also demonstrates that operational flexibility enables businesses to adapt to market shocks, such as fluctuations in fuel prices or seasonal variations in demand. A practical analysis of case studies confirms that companies focusing on niche market segments can maintain profitability, even during periods of intense price competition. The research's practical value lies in offering strategic recommendations to small and medium freight operators, with the aim of reducing their dependence on price-based competition and increasing business sustainability. The proposed strategic matrix can be used to select the optimal positioning model according to the scale of operations and level of specialisation. The study's scientific novelty lies in the systematisation of principles for applying non-mass strategies in freight transport under dumping conditions, the development of a strategic positioning matrix that considers scale, specialisation and integration with client processes, and the proof of its effectiveness through real-world case analysis, which highlights the advantages of client-oriented approaches.

The Engaged Research discourse has been evolving, seeking to strengthen relationships between academic research and society. While social and health sciences have largely facilitated engagements with communities, learnings from this process can be applied to other fields, especially as research shifts towards more inter- and transdisciplinary approaches in engagement. This research thus addresses the need for contextual analysis of Engaged Research in South Africa, exploring this approach to research, in practice. The research examines ten case studies that represent examples of Engaged Research collated through a collaborative process with the South African Agency for Science and Technology Advancement. Analyses of these cases emphasised the use of bottom-up Engaged Research approaches, which included fostering collaborative and inclusive processes seeking to actively engage communities throughout the research process. A relational lens applied in this examination (of the ten case studies) reinforces the relevance of community-based participatory research in an African context and consequently drives Engaged Research that may have value for concerned communities. This study provides key insights into challenges of Engaged Research in the country, pointing towards the need for approaches that encompass power-sharing between researchers and communities and programmes that are backed by capacity-building commitments from tertiary education institutions.

Translanguaging enables educators and learners to adapt in the learning process of English as a Foreign Language (EFL) by utilizing their entire linguistic repertoire. This qualitative case study aims to analyze the use of translanguaging focusing on strategies and pedagogical function by three pre-service teachers during their teaching practice (PPL) in junior high schools. Data were collected through semi structured interviews and video observations, which were then analyzed using qualitative case study analysis. The results show that teachers often use various languages such as Indonesia and Javanese, semiotics resources also used to encourage learning. Another strategy that found is the use of simple translation (translative) method and complex translating (interwoven) method. In addition, there are pedagogical functions of translanguaging that include class management, improving student understanding and participation, explaining concepts of the material and instructions. These results show the importance of translanguaging practices by pre-service teachers to improve and strengthen student learning in the EFL context.

Abstract: Recent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open-ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain-specific information that was not well-represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human-AI collaboration between teachers and researchers using an AI-generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human-AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human-AI collaboration to address students' STEM+C challenges: the teacher can use the AI-generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross a difficulty threshold, and (2) the point of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer-based learning environments that provide feedback.

Agriculture consumes over 70% of global freshwater while generating substantial diffuse pollution that threatens water security and sustainable development objectives. The grey water footprint (GWF) concept quantifies the freshwater volume required to assimilate agricultural pollutants to acceptable environmental standards, providing a crucial metric for sustainable water resource management. This comprehensive review synthesizes current knowledge on agricultural GWF assessment, examining conceptual foundations, calculation methodologies, and practical applications across diverse farming systems. We analyze the evolution from simplified Tier-1 approaches to sophisticated process-based models incorporating hydrological dynamics, pollutant fate and transport, and ecosystem interactions. The review encompasses nitrogen and phosphorus from fertilizers, pesticide contamination, and livestock-derived pollutants, revealing substantial methodological challenges including data scarcity, temporal-spatial variability, and pollutant interaction effects. Comparative analysis of global case studies demonstrates order-of-magnitude differences in GWF estimates depending on calculation methods, regional conditions, and management practices. Advanced modeling frameworks integrating SWAT, HYDRUS, CROPWAT and machine learning algorithms show promise for improving accuracy and policy relevance, though standardization remains limited. Future developments should prioritize: (1) multi-pollutant interaction modeling under varying environmental conditions, (2) climate-adaptive GWF frameworks incorporating extreme weather scenarios, and (3) standardized global methodologies enabling cross-regional comparisons and policy integration. This review establishes GWF as an essential tool for achieving water-food-environment nexus sustainability, supporting evidence-based agricultural policy and contributing to SDG implementation at multiple scales.

Masculine Identity Reconstruction and Sexual Behavior Transformation Among Men Living With HIV in Indonesia-Malaysia Border Region: A Social Ecological Phenomenological Study.

Background: Traditional healthcare models have long focused on the patient-provider relationship. However, this structure often overlooks the critical role that family and friend caregivers play in managing chronic conditions, disability, and age-related frailty. The Caregiver Rx Program in Canada is designed to redefine healthcare by incorporating a “triad of care” approach, where caregivers are recognized as essential partners in care, contributing to holistic, person-centered health outcomes. Supported by the Ministry of Health’s Patients as Partners initiative, Caregiver Rx addresses constraints caregivers face in being identified, referred, and integrated into healthcare as partners. The program leverages social prescribing as a model to embed caregiver support in healthcare systems and drive system-wide transformation. Target Audience: This workshop is designed for healthcare providers, policymakers, caregivers, community leaders, and researchers involved or interested in integrated care and caregiver inclusion. Participants will gain insights into social prescribing, caregiver-inclusive practices, and co-design methodologies that support collaborative, community-centered care models. Overview of Initiative: The Caregiver Rx Program is one of Canada’s pioneering social prescribing initiatives, aimed at increasing caregiver identification and enhancing referrals for support within healthcare. Over the past decade, Caregiver Rx has promoted systemic change by establishing partnerships with primary care providers, training over 600 healthcare professionals, and creating referral tools to facilitate caregiver identification and engagement. A core feature of the program is its co-design approach, where family caregivers, healthcare and community providers and policymakers collaborate to shape and implement integrated care pathways. This involvement positions caregivers as partners in care, engaging them on advisory boards, focus groups, and training initiatives to ensure solutions are grounded in their lived experiences and address their needs. The program employs a PDSA (Plan-Do-Study-Act) approach, resulting in successful outcomes such as over 1,000 provider engagements, a 150% year-over-year increase in referrals to the BC Caregiver Support Line, and the establishment of new referral pathways across systems. By focusing on caregivers’ real-life needs, Caregiver Rx demonstrates how integrated care can advance health equity, build resilient communities, and improve health outcomes. Workshop Activities: This interactive 60-minute workshop emphasizes active participant engagement through discussions, group activities, and case study analysis: Introduction (15 minutes): Overview of the Caregiver Rx Program, its goals, and its approach to caregiver inclusion, with a focus on co-design and social prescribing. Presentation of Case Studies (15 minutes): Real-world examples and case studies highlighting the program’s impact, adaptability, and tools used. Group Work (20 minutes): Participants discuss applying caregiver-inclusive models in their contexts, considering referral pathways, co-design, and partnerships. Feedback and Discussion (15 minutes): Groups share insights, followed by open discussion on challenges, adaptations, and benefits in diverse settings. Key takeaways and action steps, with an invitation for follow-up collaboration. Outcomes: The workshop will deliver actionable take-home messages: Recognition: Understand caregivers’ critical role as partners in care and the benefits of a triadic care model. Application: Gain tools to implement co-design and social prescribing principles. Implementation: Identify strategies for embedding caregiver support in healthcare systems. Sustainability: Leave with resources, replicable models, and contacts to foster caregiver-centered healthcare systems.

This study examines healthcare system strains in rapidly aging societies through a comparative analysis of Puerto Rico, Cuba, Japan, the Philippines, and South Korea. While existing research documents global aging and physician migration trends, few studies explore how these challenges manifest in conjunction with each other. Puerto Rico presents a critical case, with 24% of its population aged 65+, severe physician migration, and systemic underfunding under U.S. Medicaid structures. Using a structured comparative case methodology, we analyze policy responses across four nations with divergent approaches: Cuba, Japan, the Philippines, and South Korea. Data from government reports, academic literature, and World Health Organization (WHO) datasets show that (1) proactive medical education investments outperform reactive measures, (2) dedicated long-term care financing is essential but structurally unavailable in Puerto Rico, and (3) territorial status in the case of Puerto Rico, constrains policy innovation. Conventional aging frameworks are challenged by revealing how high-income territories can exhibit low systemic adaptability. Proposed are targeted reforms for Puerto Rico, including Medicaid restructuring and workforce incentives, with broader implications for aging societies under constrained sovereignty. This study fills a critical space in understanding how geopolitical contexts shape healthcare system vulnerabilities.

Background: Italian family health legislation has evolved progressively, shifting in recent decades toward models prioritizing family relationships as integral to overall well-being. This paradigm shift led to the creation of Family Centers within traditionally clinical-oriented Family Counseling services to enhance accessibility and provide comprehensive support for families across all life stages. Conceived initially as community-oriented spaces, Family Centers are managed collaboratively with networks of family volunteer organizations, allowing them to identify community needs and offer flexible, holistic support. This study explores the implementation and impact of Family Centers (FCs) in Lombardy, Italy, addressing the need for integrated social and healthcare models that provide coordinated, family-centered support. Approach: In collaboration with ASST Spedali of Brescia and the Design Department of the Politecnico di Milano, this study sought to clarify the FC’s role, identity, and relationship with Family Counseling Services; identify barriers and facilitators in integrating third-sector organizations and volunteers into service delivery; and explore strategies for fostering effective inter-professional collaboration. A multi-phase methodology was employed, including a review of regulatory frameworks, an analysis of international case studies, and a literature review on family-oriented care models. Twelve semi-structured interviews with key stakeholders, including FC volunteers, managers, and related professionals, were conducted to capture insights on operational challenges and opportunities. Findings were further validated through three workshops, fostering “collective reflexivity” among participants to guide the FC’s future development. Results: The study identified critical barriers—such as unclear territorial positioning, limited resources, and resistance to new approaches—as well as facilitators, including complementary and innovative strategies for addressing diverse family needs. The two-year pilot (2020–2022) demonstrated that, with a clear regulatory framework and active family involvement, FCs can serve as a cornerstone of integrated care, enhancing service continuity and co-delivery effectiveness. Implications: This research offers valuable insights for refining and scaling co-production models, positioning FCs as pivotal to Italy's future social and healthcare policies. By promoting a “think-family” and community-based welfare approach, FCs have the potential to sustainably address complex family dynamics, advancing the discourse on integrated care within evolving health and social service systems.

Background: Education and training in integrated care have been the focus of many presentations at previous events, and the subject of recent publications related to the need for globally-aligned competencies for the integrated care workforce. IFIC's Education and Training SIG was founded in 2017 with an aim to build a global network of researchers and educators focused on best practices for workforce development in integrated care. During the pandemic, that SIG became inactive; however, many of its members continued their work either independently or in collaboration with SIG colleagues. A recent study led by the IFIC Academy team focused on identifying the state of education and training in integrated care across seven countries. The findings from that study and other recent publications on this topic will be discussed in this workshop, which has been designed to re-invigorate the SIG on Education and Training by building upon findings, attracting new partners who wish to engage in this movement, and identifying next steps for this group as we respond to the global workforce crisis. When we discuss workforce training and education in this context, we will include formal education (beginning with undergraduate education curriculum) through graduate degrees and beyond into continuing education and professional development for health and social care providers in the workplace, as well as education and development for caregivers and patient/person self-management of care in communities. The importance of involving people, patients, and carers in developing, designing, delivering, and evaluating curriculum will be a theme interwoven throughout this workshop. Audience: Educators, leaders, and managers who want to be part of the movement towards a global community of practice in Integrated Care Education and Training. We also wish to raise education and development for those vital practitioners who are not ‘formal’ professionals, but who support people on a day to day basis and are central to experiences of person-centered care. Approach: We propose a 90 min. workshop (formerly called a "world café session,") structured as follows: I. Introduction (10 minutes) - Brief introduction of speaker(s) and background/context of the former SIG II. Overview of Published Research and IFIC Report (15 minutes) 1. Scope of Work to Date 2. Key Statistics and Recommendations III. Small Group Work - Interactive Case Study Analysis, Discussion, and Reflection (20 min) 1. Macro, Meso, Micro Levels - barriers and opportunities 2. Strategies to Address Opportunities 3. Develop recommendations for next steps for global workgroup VI. Large Group Reflection and Planning (20 min) 1. Share small group main takeaways and recommendations VII. Developing Next Steps as a Large Group (20 min) VIII. Closing (5 min) Outcomes: After group discussions, key takeaways from group discussions will be summarized in the large group, and notes will be captured as the large group plans the next steps to continue this work after the conference. Information and follow up opportunities will be provided in closing remarks and notes on the workshop will be shared with participants.

Artificial intelligence (AI) is increasingly deployed in nuclear power operations to enhance safety and resilience, yet systematic empirical assessments of its impact on risk governance remain limited. This study addresses this gap by developing and applying a resilience governance framework based on the triad of robustness, redundancy, and rapidity to evaluate AI applications through an analysis of case studies from Chinese nuclear power plants (2020–2025). The findings demonstrate that AI enhances system robustness via proactive policy formulation and emergency planning, enables redundancy in operational systems, and significantly improves response speed during emergencies through AI-driven inspection robotics. However, the integration of AI is neither universally effective nor without risk; limitations include context-specific performance variability and emerging technical and ethical challenges. The study concludes with recommendations for adaptive regulatory frameworks, rigorous validation of safety-critical AI systems, and inclusive global governance mechanisms to ensure equitable participation in the AI-driven evolution of nuclear safety. This work advances the discourse from conceptual promise to empirically grounded, risk-informed governance.

Despite unprecedented political endorsement, nature-based solutions (NbS) consistently fail to achieve the systemic transformation required for climate and biodiversity crises. This implementation deadlock stems from a profound triple strategic gap: a translational evidence gap between fragmented science and actionable design, a strategic design gap in misaligned institutions, and a fundamental theoretical integration gap disconnecting ecological principles from socio-economic solutions. This study forges and validates the symbiosis framework—an interdisciplinary blueprint designed to bridge this triple gap. Employing design science research, we: (1) synthesize ecological theory with institutional economics to distill three core design principles—functional reciprocity, nested modular network architecture, and strategic leverage and foundational support; (2) translate these into a conceptual model and strategic implementation blueprint; and (3) validate the framework through comparative analysis of global NbS case studies. The resulting framework provides a novel translational logic, moving beyond critique to offer a prescriptive design tool. It enables practitioners to diagnose systemic failures and design interventions that emulate ecological intelligence while applying institutional design principles: cultivating reciprocal partnerships, structuring resilient networks through polycentric governance, and strategically targeting catalytic leverage points and foundational assets. We conclude that scaling NbS requires a paradigm shift from managing isolated symptoms to architecting symbiotic systems. The symbiosis framework provides the essential interdisciplinary blueprint for this shift.

The rapid proliferation of generative Artificial Intelligence (AI), algorithmic communication systems, and digital personalization technologies has fundamentally reshaped organizational information environments. As AI-generated content becomes increasingly indistinguishable from human-authored communication, organizations now operate in a landscape where truth is dynamically constructed, strategically contested, and algorithmically mediated. At the same time, rising political, cultural, and ideological polarization has fractured shared social realities, producing epistemic fragmentation among employees, consumers, stakeholders, and the broader public. This paper examines how truth is negotiated at the intersection of AI-driven communication and polarized social contexts. Drawing upon Foucauldian perspectives on power–knowledge relations, Habermasian theories of communicative rationality, and narrative-based organizational studies, this research conceptualizes organizational truth as a multidimensional, power-infused, and technologically co-produced phenomenon. Using a multi-method qualitative design, including critical discourse analysis of AI-generated corporate communication and comparative case studies in the technology and media sectors, the study investigates how organizations construct truth-claims, legitimize narratives, and navigate polarized audiences. Findings reveal that AI systems increasingly function as strategic truth-makers, shaping narratives based on optimization logics rather than factual coherence. AI-generated messaging contributes to the formation of “segmented realities,” where stakeholder groups receive personalized, and sometimes conflicting, narrative frames. Moreover, the automation of communication amplifies managerial power, marginalizes dissent, and introduces ethical tensions around transparency, bias, and accountability. The analysis shows how organizations must balance the efficiency offered by algorithmic communication with the need to preserve trust, authenticity, and discursive openness. This paper contributes to emerging scholarship on truth, AI, and organizational communication by proposing a conceptual framework for understanding truth as a negotiated, co-produced, and contextually contingent construct within AI-mediated environments. The study offers practical implications for leaders, emphasizing the importance of ethical AI governance, inclusive dialogue, algorithmic transparency, and responsible narrative management. The paper concludes by identifying pathways for future research on AI-driven truth-making, polarization, and organizational legitimacy in increasingly fragmented knowledge ecosystems.

ABSTRACT Food loss and waste (FLW) within agri‐food supply chains significantly impact the consumer economy, depleting natural resources and contributing to greenhouse gas emissions, thereby posing a critical challenge to sustainable development. Digital transformation is increasingly recognised as a key lever for reducing FLW in agri‐food supply chains, yet existing research presents mixed and often conflicting findings regarding its sustainability impacts. Drawing on Institutional theory and the Resource‐Based View, we examine how sustainability‐oriented institutional pressures interact with firm‐level resources and capabilities to facilitate effective digital transformation. The study is based on a qualitative multiple case study analysis of 18 Italian agribusinesses operating at various stages of the agri‐food supply chain. Data were gathered through semi‐structured interviews and analysed using an iterative coding process. The findings highlight that institutional pressures alone are insufficient to drive companies toward digitalisation and achieve positive sustainability outcomes. Instead, the critical role of top management and communication capabilities in supporting organisational sensemaking processes helps agribusinesses develop a shared understanding of the value of digitalisation, decreases resistance to change and facilitates the translation of digital technologies into daily practices. Ultimately, this fosters improved coordination, knowledge sharing, trust‐based collaboration and reduces FLW. From a policy perspective, these results suggest that public initiatives promoting digital transformation should go beyond technological incentives to also include measures supporting training and capability development within firms.

ABSTRACT Permeability is a fundamental parameter governing the long-term durability of concrete structures, as it controls the ingress of water and aggressive ions, such as chlorides and sulphates, which initiate deterioration mechanisms including microcracking, expansive reactions, and reinforcement corrosion. This review synthesises the primary mechanisms influencing concrete permeability, with emphasis on pore structure evolution, microcrack formation, and mix design parameters. Strategies for permeability mitigation, including optimised aggregate grading and incorporation of supplementary cementitious materials, are critically examined in relation to durability enhancement. A comprehensive evaluation of Non-destructive Testing (NDT) techniques, Acoustic Emission (AE), Electrical Resistivity (ER), Resonance Frequency Testing (RFT), and Ultrasonic Pulse Velocity (UPV), is presented, highlighting their underlying principles, sensitivity to permeability-related parameters, and applicability under laboratory and field conditions. A comparative case study analysis demonstrates how environmental exposure, moisture conditions, and specimen characteristics influence the interpretation of NDT results. The findings underscore that no single technique is sufficient for reliable permeability assessment; instead, integrated multi-method approaches, supported by appropriate calibration, provide improved diagnostic confidence. This review offers a structured framework to support informed selection and implementation of permeability evaluation strategies in both new construction and existing infrastructure.

The emerging neurotechnology industry, led by firms like Neuralink, is rapidly transitioning from purely medical applications to the broader consumer market of human enhancement. This pivot introduces unprecedented ethical and business risks, particularly concerning the commercialization of neural data and the long-term sustainability of implanted devices. This paper investigates these challenges to develop a strategic framework for building consumer trust and ensuring corporate viability. This study employs a multi-faceted approach, combining a case study analysis of the corporate failure of Second Sight Medical Products with a comparative review of the business strategies of key neurotech players in 2026 (Neuralink, Synchron, Paradromics, Blackrock Neurotech). It further analyzes the nascent regulatory landscape for neural data privacy, including the General Data Protection Regulation (GDPR) and emerging U.S. state-level “neurorights” legislation. The analysis reveals a critical gap in current business models and regulatory frameworks. The Second Sight case, where patients were left with unsupported, “bricked” retinal implants, serves as a stark warning against prioritizing short-term innovation over long-term corporate responsibility. Traditional one-time sale models are inadequate for permanent implants, while pure subscription models introduce profound ethical hazards. The commercialization of neural data, the most intimate of all personal information, represents a pivotal source of both value and systemic risk. This paper proposes a novel strategic framework for sustainable business models in the neurotech sector. It moves beyond conventional models to advocate for hybrid structures, such as tiered service agreements, industry-backed legacy support consortia, and escrow-based continuity funds. The framework is designed to help firms navigate the neuro-data economy by aligning profitability with the ethical imperatives of long-term patient support and robust data privacy, thereby building the consumer trust essential for market acceptance.

Topicality. In the context of global competition, increasing requirements for the quality of digital products, a digital transformation of quality management is undergoing a digital transformation driven by the active introduction of artificial intelligence technologies. Traditional approaches to quality assurance are insufficient to address modern challenges associated with environmental changes and the need to process large amounts of data. Such circumstances necessitate a fundamental transformation of approaches to quality assurance (QA) of digital products under the influence of artificial intelligence technologies. The impact of the digital economy on the process of ensuring the quality of digital products necessitates a transition from a reactive error correction model to proactive customer experience management (CX). The experience of leading technology companies (Uber, Airbnb, Duolingo) demonstrates examples of the use of machine learning algorithms to automatically detect “frustration signals”, monitor business anomalies and create hyper-personalized interfaces. To create digital products that precisely meet consumer needs, the convergence of marketing technologies and quality management is a necessary condition. This approach turns quality into a marketing tool, focusing on meeting customer requirements and sustainable development. Aim and tasks. The aim is to justify the need to transition from a reactive model of digital product quality assurance to a proactive user experience management system based on artificial intelligence technologies. The aim of the study necessitated the following tasks: to justify the existing crisis of reactive QA and prove the economic necessity of proactivity; to argue for the need to use predictive analytics to predict future events and bottlenecks in the customer experience; to consider the possibilities of using artificial intelligence as a diagnostic tool to improve the quality of a digital product; to carry out a comparative review of behavioral analytics tools classified by availability model and application specificity, to justify hyper-personalization in ensuring the quality of a digital product. Materials and Methods. The theoretical basis of the study is the analysis of scientific publications, analytical reports and case studies of leading technology companies in the field of digital marketing, quality management and the use of artificial intelligence. To achieve the goal, methods of comparative analysis, generalization and systematization of scientific approaches to quality management of digital products were used, as well as elements of content analysis of publications dedicated to the implementation of artificial intelligence solutions in QA and UX. The article systematizes methods of automated detection of "frustration signals", hyper-personalization tools that allow you to identify and eliminate interaction problems before the client realizes them. Research results. The article examines the conceptual principles of the transformation of quality management in the field of digital products. The transformation of approaches to quality assurance (QA) of digital products under the influence of artificial intelligence technologies is substantiated. The authors propose a transition from the reactive error correction model to proactive customer experience management (CX). The analysis of case studies of leading technology companies on the practical use of machine learning algorithms in the process of ensuring product quality and hyper-personalization is carried out. A personalization cycle is proposed, which involves the active use of artificial intelligence in the process of analyzing data on user behavior. The feasibility of a synergistic combination of AI with machine learning algorithms, which will allow personalization on the required scale, is proven. Based on the results of the analysis of practical cases, it is determined that the use of machine learning algorithms to detect "frustration signals" allows digitizing the emotional state of users and transforming subjective feelings into accurate engineering data. The need for convergence of marketing technologies and QA engineering practices in the process of ensuring product quality is substantiated. Conclusion. The conducted research led to the conclusion that in the conditions of digital transformations, the traditional model of ensuring the quality of a digital product, which is based on reactive principles, is ineffective. The approach to meeting the needs of consumers based on personalization requires digital product developers to ensure quality at the stage of its development. Using artificial intelligence to transition to a proactive quality management model will allow diagnosing and predicting possible future problems related to product quality. This approach emphasizes preventing problems, rather than identifying them after the product is used. One of the important features of the use of artificial intelligence in ensuring the quality of a digital product is its ability to automatically detect and classify “frustration signals”. The effectiveness of using machine learning systems in recognizing user behavior patterns has been proven. As a result, companies gain the opportunity to diagnose and solve problems in the user experience, as well as retain existing customers and attract new ones.

Long-distance observations (LDOs)-the visual or photographic documentation of terrestrial objects from distances often exceeding the geometric horizon-are an emerging interdisciplinary field. However, a robust scientific basis that integrates empirical records with atmospheric physics is currently lacking. This study establishes such a methodology by combining a systematic typology of LDOs with a quantitative analysis of their controlling physical factors. The methodology integrates numerical simulations with analyses of photographic case studies from dedicated observer communities, including record-setting observations from Turkey (493km), South America (484km), and Central Europe (over 200km). Analysis demonstrates that extreme visual range is primarily enabled by strong atmospheric refraction within thermal inversion layers. Key findings reveal the critical role of atmospheric transparency, turbulence, and illumination geometry in modulating observational success. It is concluded that LDOs serve a dual purpose: (i) as natural experiments for studying stratified atmospheric states and (ii) as a valuable, community-driven citizen-science practice that provides high-resolution spatio-temporal data complementary to formal meteorological monitoring.