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School-based food relief (SBFR) programs respond to students' immediate food needs by alleviating hunger. In Queensland, Australia, SBFR was expanded during the COVID-19 pandemic emergency response to address a perceived increase in food insecurity. This paper reports on an evaluation of programs supported by the Y Queensland, which aimed to highlight successful delivery practices and determine whether food insecurity improved. An explanatory mixed-method, multi-case study methodology was used to explore the effects of the program across four schools from the perspectives and experiences of the school staff. A descriptive survey supplemented the case study methodology to collect information from all other schools receiving SBFR support regarding program operations, types of SBFR provided, perceived program impact, and continued need for support. A total of 56 surveys and six in-depth interviews were collected from staff working in 46 schools supported by the program. The evaluation identified five key characteristics of effective program delivery: welcoming physical environments, relationships that promote social connection, inclusion of all to reduce stigma, adaptability to feedback and observation, and collaboration with the community. Programs with strong delivery characteristics improved food security and school connectedness, impacts that were especially valuable during COVID-19 disruptions. This study demonstrates how successful SBFR programs can operate as adaptable, equity-focused interventions that create supportive environments, reduce stigma, and strengthen social connectedness while addressing food insecurity during a public health emergency.

This paper examines the erosion of Public Key Cryptography (PKC) security under adaptive adversarial optimisation driven by artificial intelligence. The problem addressed is the growing mismatch between algorithm-centric cryptographic security models and operational attack realities, where adversaries exploit implementation-level observability rather than breaking cryptographic primitives. The methodology integrates a reproducible bibliometric analysis of Web of Science records, qualitative evidence from twenty expert interviews and three industry workshops, and a technical synthesis of AI-enabled attack mechanisms across the cryptographic lifecycle. Results show that existing research is structurally concentrated on algorithmic robustness, with no significant focus on AI-driven attack vectors, while 82% of practitioners attribute private key compromise to AI-augmented optimisation and side-channel inference. The paper's contribution is fourfold: (1) identification of a systemic research gap in AI-enabled cryptographic attacks; (2) development of an adaptive adversarial threat model spanning key generation to validation; (3) empirical validation of implementation-layer compromise mechanisms; and (4) formulation of AI-aware cryptographic resilience requirements extending beyond post-quantum approaches. The findings demonstrate that cryptographic security must be reconceptualised as an adaptive, system-level property rather than a function of algorithm strength alone.

In the context of urban slum settings in Africa, there is limited knowledge about how food preparers make decisions as to the types of meals they serve in their households. Understanding what goes into food preparers' daily choices and the complexity of providing nutritious food in resource-scarce environments are crucial to addressing the challenges of food insecurity and malnutrition. This study explored perspectives on what food is prepared in households, the factors influencing meal choices and how decisions are made about what to serve and when. This qualitative study was conducted among persons living in two urban slums in Ibadan, Nigeria, who were most responsible for food preparation in their households. Trained research assistants conducted 30 in-depth semi-structured interviews in the local languages. Data collected were transcribed verbatim, then translated into English. The data were coded with NVivo software (Version 12) and analysed using thematic analysis. Of the 30 food preparers interviewed, 28 were women. Data analysis identified three major themes: what is eaten and how it is prepared; gendered decision-making and normative beliefs; and adapting food preparation to an environment of food scarcity. The food preferences described by all participants were similar, with most expressing a desire to make calorie-dense meals. Traditional "swallows", carbohydrate-rich dough-like dishes made from starchy roots, tubers or grains, were regarded as central to most meals, although it was highlighted that children often wanted processed foods, such as white rice and noodles. Decision-making over what to prepare was gendered, with men's preferences being favoured in addition to those of children. Food preparation was time-consuming and complicated by a lack of refrigeration and limited storage. This study offers valuable insights into the resourcefulness and resilience of slum-dwelling women in planning and preparing diverse meals in the context of living in resource-limited environments and balancing traditional family hierarchies. The findings will contribute to the development of contextually relevant interventions to improve access to nutritious foods, enhance food security, and ultimately contribute to the nutritional well-being of urban slum dwellers.

The substantial growth of Internet video streaming has greatly increased resource utilization, particularly bandwidth and energy consumption, especially in systems secured through watermarking. However, the issue of decarbonization has received limited attention. As Quality of Experience (QoE) becomes a central focus, the complexity of these challenges intensifies. Adaptive bitrate algorithms often prioritize video quality while neglecting the associated energy costs, frequently selecting the highest available bitrate and thereby increasing energy consumption. Achieving a balance between QoE and energy efficiency requires careful optimization across multiple factors, including server-side processing, network stability, and bandwidth utilization. While watermarking enhances content protection and ensures secure delivery, it introduces additional computational overhead that increases energy consumption. This study aims to achieve seamless, high-quality video streaming by managing the trade-off between streaming security and QoE while minimizing energy impact. The findings underscore the importance of balancing robust security mechanisms with energy-aware optimization to achieve sustainable, secure, and high-quality video streaming. Numerical results obtained from real-world streaming scenarios validate the efficiency of the proposed method. Even with a 10% increase in the number of connected users, the proposed system reduces origin egress traffic by 25%. At the same time, CDN traffic increases by 23% due to the delivery of higher-bitrate video streams, indicating a 13% improvement in perceived video quality rather than a mere redistribution of system load.

Driver Monitoring Systems (DMS) are poised to become a standard safety feature in new vehicles, with regulations in regions such as the European Union mandating their inclusion. Yet little is known about how privacy and security concerns influence their acceptance. This study investigated attitudes towards DMS among 9025 licensed drivers from nine countries (Germany, Spain, France, Japan, Poland, Sweden, United Kingdom, United States, and China). Participants rated their acceptance, concerns (including data collection, secondary use, and perceived insecurity), and behavioural intention to use DMS. Through Latent Profile Analysis, five distinct user profiles were identified: Enthusiasts, Somewhat enthusiastic, Moderate, Sceptical, and Resistant, each exhibiting systematic differences in acceptance, concerns, and behavioural intentions. Cross-national comparisons revealed significant cultural variations, with Enthusiasts and Somewhat enthusiastic profiles being more prevalent in China, whereas Resistant and Sceptical profiles were disproportionately represented in France, United Kingdom, and Sweden. Notably, age and gender effects were significant, as older drivers and women were more likely to belong to the resistant profile. These findings underscore the necessity for targeted interventions, transparent data handling policies, and culturally tailored communication strategies to enhance user acceptance of DMS and the widespread adoption as part of the broader transition towards automated and connected driving systems.

Climate change intensifies plant stress factors and threatens global food security and therefore demands sustainable land management practices. Engineered biochar nanocomposites (EBNCs) are the synergistic combination of nanotechnology to improve carbon sequestration and sustainable approach for the precise functionality of biochar (BC) to amend soil properties. This comprehensive review examines the design, fabrication, and mechanisms of EBNCs to mitigate both biotic and abiotic stresses in plants. We analyze recent advances in EBNC synthesis through physical, chemical, and biological integration routes for incorporating metal/metal oxide nanoparticles (NPs) i.e., Ag, Fe, ZnO, TiO2, and graphene into BC matrices. EBNCs induced mechanisms of stress alleviation are enhanced water retention, ion homeostasis, heavy metal (HM) immobilization, antimicrobial activity, induced systemic resistance (ISR), and scavenging reactive oxygen species (ROS). The applications of EBNCs significantly improve crop performance under drought, salinity, HM contamination, or pathogen stress. However, production costs, environmental trade-offs, potential toxicity, and regulatory policies are research gaps and require further careful consideration. Future research should focus on developing smart, stimuli-responsive EBNCs with controlled-release properties, integration of omics and artificial intelligence tools for optimized formulations, and circular economic approaches. EBNCs are increasingly recognized as multifunctional materials with potential applications in climate-resilient and sustainable agriculture.

Rodent populations in agricultural environments serve as critical reservoirs for zoonotic diseases, posing significant risks to food security and public health. Transmission in these settings is complex, thereby occurring through both direct rodent-to-rodent contacts and indirect exposures to environmental contamination. In this paper, we formulate and analyze a deterministic model that integrates rodent population dynamics with an environmental pathogen compartment. To identify resource-efficient mitigation policies, we develop an optimal control framework that incorporates three time-dependent interventions: contact prevention, environmental sanitation, and treatment. By applying Pontryagin's Maximum Principle, we derive the Hamiltonian, the adjoint system, and the characterization of the optimal controls, and subsequently solve the optimality system using a forward-backward sweep algorithm. We evaluate the epidemiological impact and economic viability of seven distinct intervention strategies using the Average Cost-Effectiveness Ratio (ACER) and the Incremental Cost-Effectiveness Ratio (ICER). Numerical simulations demonstrate that while the full combination of controls yields the maximum reduction in infection, the prevention-only strategy emerges as the most economically attractive option under restricted budgets. These findings suggest that prioritizing contact-reduction measures provides the most cost-effective basis for disease management, while integrated sanitation and treatments should be scaled based on resource availability. This study provides a quantitative framework that may assist agricultural stakeholders in developing resource-efficient control policies.

Climate change poses an escalating threat to human health, yet graduate medical education has been slow to incorporate environmental health content into residency training. Internal medicine residents report low confidence in their ability to discuss environmental health topics, creating gaps in patient counseling and health advocacy. Despite position statements from major medical societies calling for environmental justice education in graduate medical education, few published curricula exist to guide internal medicine residency programs in this effort. To describe the design, implementation, and preliminary evaluation of an interdisciplinary environmental health curriculum for internal medicine residents at a large university-affiliated residency program in the southern United States, informed by a formal needs assessment identifying specific knowledge gaps. A seven-session environmental health curriculum was developed using a needs assessment-driven approach grounded in Bandura's self-efficacy theory and Kern's six-step framework for curriculum development in medical education. Twelve environmental health topics were assessed via a baseline confidence survey of 110 internal medicine residents. Curriculum content was prioritized based on the lowest-confidence domains and delivered through interactive didactic sessions led by content-area experts across disciplines including pulmonology, infectious disease, toxicology, and public health. Sessions covered climate change and human health, environmental justice, food security, endocrine disruptors, air pollution, emerging infectious disease, extreme weather, climate and cancer, and occupational lung disease. Post-curricular confidence surveys were administered to evaluate impact. The baseline needs assessment (n = 62, 56% response rate) revealed a mean confidence score of 2.22 out of 5 across all environmental health topics. Hazardous waste, endocrine disruptors, water quality, toxicology, and environmental justice represented the lowest confidence domains. Seventy-one percent of residents reported no or slight confidence discussing environmental justice with peers. The curriculum was successfully integrated into existing didactic time across seven sessions from April through December 2024, with positive resident engagement and satisfaction. Post-curricular assessment data analysis is ongoing. This article presents a replicable, needs assessment-driven model for embedding environmental health education into internal medicine residency training. By grounding curricular design in self-efficacy theory and leveraging interdisciplinary expertise, programs can systematically address the gap between the growing health impacts of climate change and physician preparedness to counsel patients and advocate for communities.

This paper proposes a radio frequency (RF)/free space optics (FSO) communications hybrid system that will improve security and reliability of future sixth generation (6G) wireless communication network links with practical channel conditions. The system uses the composite Weibull-Lognormal (WLN) turbulence model for modeling the free-space-optics (FSO) link and incorporates the effects of both local fade events and global weather phenomena; it also uses Nakagami-m/Rayleigh fading to model the RF link. A hybrid link selection algorithm (HLA) is used to select the best available transmission link as a function of real-time channel characteristics. The performance of the proposed hybrid system is analyzed from three perspectives: secrecy capacity, bit-error-rate (BER), and outage probability under different fading/turbulence conditions through an exhaustive Monte-Carlo simulation process and supported by analytical results. These analyses show that this hybrid system has significant advantages over single-link systems employing either RF or FSO alone; these advantages include reduced outage probability, improved BER performance, and higher secrecy capacity especially when operating under high-turbulence conditions. These results show that a composite-fading architecture provides a reliable and secure framework for the development of fifth-generation (5G)-like communication systems which can be used in future sixth-generation (6G) wireless communication networks.

Locust phase polyphenism, a classic example of phenotypic plasticity, has been studied for over a century, generating an extensive body of literature. However, a quantitative synthesis of the field's structure, evolution, and disparities has been lacking. Here, we present the first comprehensive bibliometric analysis of locust phase polyphenism research, quantitatively mapping its intellectual structure, collaborative networks, and thematic evolution. Analyzing 400 peer-reviewed primary studies published from 1921 through February 2025, we reveal a field at a critical inflection point. Publication trends demonstrate robust growth since the 1990s, reflecting the refinement of classical methods and the emergence of molecular and neurobiological approaches. However, this expansion rests upon a narrow foundation: research concentrates almost exclusively on two model species-Schistocerca gregaria and Locusta migratoria-while ecologically important non-model taxa remain critically understudied. Co-authorship network analysis exposes significant geographical disparities: research leadership concentrates in a small number of countries (UK, China, Japan, Belgium, Israel, Kenya, Germany, and USA), while most other locust-affected regions across the Sahel, the Horn of Africa, and the Middle East remain peripherally integrated. Keyword co-occurrence mapping identifies critical thematic blind spots within the phase polyphenism literature, including a complete absence of explicit climate change framing and limited representation of socioeconomic dimensions and non-model species. The field thus exhibits a notable disconnect: deep mechanistic knowledge of how phase change occurs exists alongside limited understanding of when and why outbreaks occur under changing environmental conditions. Transformative advances require strategic reorientation: from model-organism focus to comparative ecology, from episodic collaboration to equitable partnership with range-state scientists, from descriptive mechanism to functional validation, and from neglected frontiers (microbiome, epigenetics, and climate) to systematic investigation. This bibliometric mapping provides an evidence-based framework to guide future research toward greater impact on global food security. El polifenismo de fases en langostas, un ejemplo clásico de plasticidad fenotípica, ha sido estudiado durante más de un siglo, lo que ha generado una extensa literatura científica. Sin embargo, aún se carece de una síntesis cuantitativa sobre la estructura, evolución y disparidades de estecampo. Aquí presentamos el primer análisis bibliométrico exhaustivo sobre la investigación del polifenismo de fases en langostas, mapeando cuantitativamente su estructura intelectual, redes de colaboración y evolución temática. Analizando 400 estudios primarios revisados por pares publicados desde 1921 hasta febrero de 2025, revelamos el campo en un punto de inflexión crítico. Las tendencias de publicación demuestran un crecimiento robusto desde la década de 1990, reflejando el refinamiento de métodos clásicos y el surgimiento de estudios con enfoques moleculares y neurobiológicos. Sin embargo, esta expansión se asienta sobre un fundamento estrecho: la investigación se concentra casi exclusivamente en dos especies modelo—Schistocerca gregaria y Locusta migratoria—mientras que otros taxones no-modelo ecológicamente importantes permanecen críticamente subestudiados. El análisis de redes de coautoría expone disparidades geográficas significativas: el liderazgo en investigación se concentra en un pequeño número de países (Reino Unido, China, Japón, Bélgica, Israel, Kenia, Alemania, EE. UU.), mientras que la mayoría de otras regiones afectadas por langostas como lo son el Sahel, el Cuerno de África y el Medio Oriente permanecen integradas periféricamente. El mapeo de co-ocurrencia de palabras clave identifica diversos vacíos temáticos críticos en la literatura sobre el polifenismo de fases, incluyendo la ausencia total de un marco explícito sobre cambio climático y una representación limitada de las dimensiones socioeconómicas y de especies no-modelo. Éste campo presenta, por tanto, una desconexión notable: existe un conocimiento mecanístico profundo sobrecómo ocurre el cambio de fase, pero sigue siendo limitado el entendimiento de cuándo y por qué se producenlos brotes bajo condiciones ambientales cambiantes. Avances transformativos requieren de una reorientación estratégica: desde el enfoque centrado en organismos modelo hacia la ecología comparativa; desde la colaboración episódica hacia una asociación equitativa con científicos de los países del área de distribución de las plagas; desde mecanismos descriptivos hacia la validación funcional; y desde fronteras poco exploradas (microbioma, epigenética, clima) hacia una investigación sistemática. Este mapeo bibliométrico proporciona entonces, un marco basado en evidencia para guiar investigaciones futuras hacia un mayor impacto en la seguridad alimentaria global.

The black soil region in Northeast China is an important commercial grain production base in China. However, unsustainable land-use practices and climate change have accelerated black soil degradation, exacerbating the vulnerability of agricultural systems. To address this, this study establishes a comprehensive index system to assess the vulnerability of agricultural systems in the black soil region based on the widely-adopted IPCC vulnerability framework including exposure, sensitivity, and adaptability. The index weights were determined using the entropy method. The results revealed distinct spatiotemporal patterns across vulnerability components. Exposure increased annually by 0.73%, with high-value areas in the Songnen Plain and low-value areas around Mudanjiang City. Sensitivity grew faster at 1.25% per year, exhibiting low-value areas in the north and high-value areas in the south. Adaptability declined annually by 2.59%, with high-value areas mainly located in the developed southwest. Consequently, overall vulnerability increased by 4.83% annually. The southwestern plains were identified as the primary high-value areas. Combining the dominant element method and qualitative analysis, we identify five distinct vulnerability types. Based on these classifications, this study proposes tailored vulnerability coping strategies for each type. The framework provides a transferable model for enhancing food security and ecological resilience in vulnerable agricultural regions globally.

Centrally located breast cancers (CLBC) involving the nipple-areolar complex (NAC) pose a significant challenge for breast-conserving surgery, often necessitating mastectomy. The Grisotti flap, while a valuable oncoplastic technique, has limitations in vascular reliability and applicability, especially post-neoadjuvant chemotherapy. We introduce a novel "Pedicled Skin Island Therapeutic Mammoplasty" (PSI-TM) as an alternative. This single-center retrospective study analyzed 23 consecutive patients with CLBC infiltrating or fixed to the NAC who underwent PSI-TM between April 2018 and June 2023. All patients had medium-to-large breast volumes (Cup C/D). Data on demographics, tumor characteristics, surgical outcomes, complications, aesthetic results, and oncological safety were collected and analyzed. The cohort was predominantly premenopausal (82.6%) with invasive ductal carcinoma (87.0%). Most patients (73.9%) were treated post-neoadjuvant chemotherapy. The overall complication rate was 21.7% (5/23), all Clavien-Dindo Grade I-II, with no returns to the operating room. The combined ratings from the surgeon and independent observer were 'Good' or 'Very Good' in 73.9% of cases (17/23). Over a median follow-up of 50 months, there were no instances of positive margins, local recurrence, or distant metastasis. PSI-TM is a feasible and safe oncoplastic technique for CLBC with NAC involvement. It may offer improved vascular security compared to the traditional Grisotti flap based on theoretical anatomical advantages, leading to low complication rates, excellent aesthetic results, and encouraging early oncological outcomes, even in a post-neoadjuvant setting.

Scientific literature highlights that rising mean temperatures, combined with increasing frequency and duration of extreme heat events, pose risks to food security, particularly in climate sensitive regions of South Asia. These impacts are especially critical in West Bengal, India, where diverse agroecological condition and climate-sensitive farming systems create uneven socio-climatic stress. Despite growing concern, district-level heat wave risk assessment for agricultural communities in West Bengal remains limited, particularly in frameworks that integrate climatic, agricultural, and socio-economic dimensions with predictive modelling. In this milieu, the present research makes a significant contribution to climate resilience building by advancing a composite, spatially explicit heat wave risk assessment grounded in the IPCC Sixth Assessment Report (AR6) risk framework. By integrating climatic hazards with exposure and socio-economic vulnerability and using Principal Component Analysis (PCA) Composite heat wave risk index (CHWRI) were derived. Further to capture nonlinear spatio-temporal dynamics, Long Short-Term Memory (LSTM) model optimized using the Hippopotamus Optimization Algorithm (HOA) was employed to predict district-level heat wave risk classes. The results revealed pronounced spatial heterogeneity, with the red and lateritic, coastal saline, and old alluvial zones exhibiting compounded risk. The observed and predicted district-level CHWRI classes showed strong agreement, with 16 of 22 districts correctly classified (72.73%). Spatial statistics confirmed significant clustering (Global Moran's I = 0.47), while Purulia emerged as a major hotspot in Getis-Ord Gi∗ analysis. The proposed PCA-LSTM-HOA framework offers a novel, interpretable, and policy relevant tool for hotspot prioritization, early warning, and climate resilient agricultural planning -an urgent priority in the face of accelerating global warming.

Plant diseases are a serious danger to the world's food security since they drastically lower crop output. Traditional manual plant leaf inspection is time-consuming, labor-intensive, and frequently subjective. Recent developments in deep learning provide effective and scalable methods for image-based analysis-based automated plant disease identification. Three deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification. The Augmented iBean dataset, which has three classes-angular leaf spot, bean rust, and healthy leaves-was used to train and assess the models. Every model was trained using the same preprocessing and training settings to provide fair benchmarking. Receiver Operating Characteristic (ROC) curves, accuracy, precision, and confusion matrices were used to assess the model's performance. ResNet18 fared better than CNN and Vision Transformer models, according to a comparative analysis. ResNet18 maintained a high level of computing efficiency while achieving 99% accuracy and 99.01% precision. Its better categorisation capacity across all disease categories was validated using confusion matrix and ROC analysis. The study shows that ResNet18 offers the optimal trade-off between accuracy and efficiency and creates a standard benchmarking framework for bean leaf disease identification. The results demonstrate its applicability for real-time deployment in precision agricultural systems for better crop management and early disease identification.

Signed into law on July 4, 2025, the One Big Beautiful Bill Act (OBBBA) fundamentally changes the Supplemental Nutrition Assistance Program (SNAP), the largest food assistance program in the United States and a key support for maternal and child food and nutrition security. The OBBBA reduces SNAP eligibility, expands work requirements, eliminates nutrition education, prevents future benefit increases, and shifts significant costs to states. Combined, these changes may have a direct negative effect on women and children who rely on SNAP for adequate nutrition and may also reduce their access to other programs with automatic SNAP eligibility, namely the Special Supplemental Nutrition Program for Women, Infants, and Children and other child nutrition programs. The loss of this foundational public health nutrition infrastructure may negatively affect infant birth outcomes, child growth and development, and maternal morbidity and mortality. Although some state governments have implemented actions to reduce the effect of federal SNAP changes on maternal and child health, federal legislation to permanently reverse OBBBA SNAP provisions is critical to prevent short- and long-term harms. Adequately funded and accessible federal food and nutrition education programs have the potential to support the goals of the Make America Healthy Again initiative and the 2025-2030 Dietary Guidelines for Americans to support access to and consumption of whole, nutrient-dense foods and improve maternal and child health. Policy actions and funding levels for federal nutrition programs therefore should align with the stated goals of these initiatives. The purpose of this article is to describe OBBBA SNAP provisions and other related policy actions or inactions and their implications for maternal and child health in the United States. The purpose of this series is to critically examine emerging federal and state health policy changes and their implications for the health and well-being of women, childbearing families, and young children and to provide evidence-based analyses that inform the nurses who care for these populations. The author solicitation and preparation of each article are overseen by series coordinators Mindy B. Tinkle, PhD, RN, WHNP-BC, CNE, FAAN, associate professor, and Nick Edwardson, PhD, MS, associate professor, College of Nursing, University of New Mexico, Albuquerque, NM.

Older adults with type 2 diabetes experience high risk of hypoglycemia, yet clinicians often lack actionable glucose data to guide individualized treatment decisions. This study examined how standardized continuous glucose monitoring (CGM) reports inform clinicians' decision-making using Endsley's situation awareness (SA) framework. We conducted semi-structured interviews. Thirty clinicians reviewed three simulated older adult type 2 diabetes cases before and after reviewing CGM reports. Data saturation was achieved, consistent with qualitative research standards. Using an SA informed framework, we analyzed the proportion of clinicians referencing specific data elements and describing changes in diagnostic reasoning and treatment decisions. After CGM data review, clinicians frequently revised assessments and plans. Most clinicians (90%) incorporated time-in-range metrics, while 93% identified previously unrecognized hypoglycemia or nocturnal patterns. Continuous glucose monitoring data led 86% of clinicians to modify their original treatment plan, including deprescribing high-risk agents (eg, sulfonylureas), adjusting insulin timing or dosing, or initiating safer alternatives (eg, SGLT-2 inhibitors, GLP-1 receptor agonists). Clinicians also identified multiple barriers to treatment implementation, including cost, medication access, housing instability, and limited food security. Continuous glucose monitoring data-compared with A1C alone-provided meaningful, actionable information that improved individualized treatment decisions for older adults with type 2 diabetes. Continuous glucose monitoring data enhanced clinicians' perception, comprehension, and projection across the SA continuum, fostering more confident diagnostic reasoning and safer treatment strategies. These findings inform the design of SA-based clinical decision-support tools to better integrate CGM data, address contextual barriers, and optimize management of older adults with type 2 diabetes.

There is an increase in use of cosmetic procedures (CP) worldwide. Research suggests that people might develop addictive cosmetic procedures use (ACPU). We aimed to assess the prevalence of ACPU and explore associated risk factors. We collected cross-sectional data from a large sample of Jewish women in Israel aged 25-71 (N = 1614). We measured self-reported ACPU, personality measures (body- and self-esteem, attachment security), attitudes (feminism, anti-aging) and problematic social media use (PSMU). Among women who had CP (n = 710), 20% reported moderate-severe risk for ACPU during their lifetime and 15.4% in the past year. Multiple regression analyses indicated that higher ACPU was associated with lower body esteem and feminist attitudes and with higher PSMU. The association between body esteem and ACPU was primarily among women with higher PMSU. Findings indicate that ACPU is prevalent and emphasizes the importance of various risk factors, especially low body esteem and PSMU.