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This paper proposes a three-tier Stackelberg game-based hierarchical optimization framework for integrated electric vehicle (EV) battery swapping stations (BSS) and charging point operator (CPO) systems. The framework models the strategic interactions among three decision-making layers comprising grid operators, integrated CPO-BSS operators, and EV users within a multi-stakeholder energy management environment. A bi-level mixed-integer linear programming (MILP) formulation combined with backward-induction-based Subgame Perfect Nash Equilibrium (SPNE) analysis is developed to optimize dynamic electricity pricing, battery charging and swapping schedules, grid power utilization, and user service decisions under operational and grid constraints. The upper layer determines time-varying tariffs, demand-response incentives, and capacity charges to improve grid stability and social welfare, while the middle layer optimizes integrated charging-swapping operations and battery inventory management in response to grid signals and user behavior. The lower layer models EV users as rational followers responding to dynamic pricing through charging or swapping decisions. The proposed framework is validated using EV charging sessions from the publicly available ACN-Data corpus from which BSS swapping demand inputs were synthetically derived via a principled data-mapping procedure and Italian GME day-ahead electricity market price data. The results show that the proposed hierarchical framework reduces the operational cost of the system by 14.2-26.5% when compared with the unoptimized baseline system over the five-year simulation period (2020-2024), while reducing the peak grid demand by 26-28% (192-204 kW) compared with the unoptimized system and maintaining 96.8% service reliability. The coordinated strategy further enables effective load shifting toward low-price periods, enhances battery utilization efficiency, and improves demand elasticity through dynamic pricing mechanisms. Comparative analysis shows that the proposed framework captures 15-22% additional value over decentralized Nash equilibrium strategies while achieving near-optimal centralized social welfare performance under realistic institutional and operational constraints. Sensitivity and benchmarking studies confirm the robustness, computational tractability, and scalability of the proposed approach across varying tariff structures, battery inventories, and demand scenarios. The framework provides practical insights for EV infrastructure planning, grid-aware energy management, and regulatory policy design for future integrated charging and battery swapping ecosystems.

Externally triggered prodrug activation can improve therapeutic index by decoupling systemic distribution from pharmacological activity. This review examines prodrug uncaging strategies enabled by electromagnetic radiation through the lens of activation physics and clinical constraints, spanning UV-visible-near-infrared photochemistry and ionizing radiation. Differences in clinical applicability arise from fundamentally contrasting activation physics and irradiation geometry. While recent reviews have cataloged externally triggered prodrug systems across multiple modalities, we instead organize the field by activation regime and evaluate these systems under clinically realistic constraints. In photochemical activation, direct light-chromophore coupling enables predictable bond cleavage through defined excited-state pathways, but effective application is constrained by tissue optics and beam-sample geometry. X- and γ-rays penetrate deeply into tissues, but their uncaging pathway is indirect via diffusible water radiolysis products, resulting in stochastic rather than deterministic cleavage chemistry. We evaluate these platforms using deliverability, irradiation geometry, dose efficiency under clinically realistic conditions, microenvironment dependence, and functional group compatibility. We conclude with practical design rules and a decision framework that aligns targeted pathology and treatment objectives with the appropriate trigger chemistry, payload selection, and delivery strategy under the governing activation physics.

Prolonged economic sanctions-often framed as non-military tools-have increasingly harmed health systems, especially where institutional and policy capacities are fragmented. Despite the growing debate on sanctions, documented analyses of health system responses remain scarce. This study examines Iran to assess how prolonged sanctions shaped its health policy architecture and resilience capacity, with attention to compounded crises such as COVID-19. Phase one synthesized seven empirical studies conducted by the authors-including document reviews, interviews, Delphi, and policy analyses-to assess how resilience principles were embedded across the four stages of the health policy cycle: agenda-setting, policy formulation, implementation, and evaluation. These findings formed the foundation for Phase two, which applied an expanded Theory of Change (ToC) framework to reconstruct policy logics, surface implicit assumptions, and identify institutional breakpoints. After modelling the ToC, a panel of experts reviewed and validated the findings, ensuring methodological rigour and contextual accuracy in mapping resilience under sanctions. The findings indicate that while Iranian health authorities implemented adaptive measures, responses were shaped by fragmented coordination, untested assumptions, and limited structured learning systems. Resilience limitations emerged during implementation and were embedded in early design phases, not fully anticipated or addressed, given the complex and uncertain policy environment. This study offers an analytical framework for mapping resilience in policy systems under long-term constraints. Building on identified governance and design weaknesses, we recommend strengthening international legal safeguards; establishing protected humanitarian corridors; institutionalizing risk-informed planning; routine scenario-based resilience testing; and feedback-driven learning mechanisms within national policy systems. These capacities are essential to absorb shocks and enable adaptive, inclusive, sustainable responses. By clarifying structural domains where resilience can be embedded in advance, the analysis offers guidance for countries under comparable pressures to target strategies in governance, planning, and resource protection. This provides a transferable blueprint for strengthening justice-oriented health systems under long-term constraints.

This paper introduces a deep learning-based framework for phase-only synthesis of cosecant-squared (csc²) radiation patterns in planar antenna arrays with high efficiency and accuracy. The proposed method employs a physics-informed deep neural network (PIDNN), where the training process is guided by a loss function that enforces consistency between the desired and generated radiation patterns. By embedding physical constraints into the learning procedure, the model effectively achieves two critical objectives: suppression of sidelobe level (SLL) and minimization of ripples within the shaped beam region. To meet the target csc² profile under sidelobe constraints, only the phase excitations of the array elements are optimized, reducing the complexity of the problem. The performance of the proposed approach is evaluated against established optimization methods, including genetic algorithms (GA) and particle swarm optimization (PSO). Numerical and statistical analyses demonstrate that the PIDNN provides superior results in terms of pattern fidelity, loss function convergence, and computation time, particularly for large-scale planar arrays.

Cervical cancer remains a major cause of morbidity and mortality among women in Uganda. Although the Human Papillomavirus (HPV) vaccine is highly effective in preventing cervical cancer, completion of the recommended two-dose schedule remains low, particularly in rural settings. Rukiga District was selected for this study due to persistently low HPV second-dose (HPV2) completion rates compared with national targets. This study assessed health facility-level barriers influencing HPV vaccine completion among adolescent girls aged 9-14 years in rural Uganda. A mixed-methods cross-sectional study was conducted between June and September 2022 in selected Health Centre II (HC II), Health Centre III (HC III), and Health Centre IV (HC IV) facilities in Rukiga District. A household survey involving 292 caregivers of eligible adolescent girls was conducted using systematic random sampling. The primary outcome was completion of the two-dose HPV vaccination schedule (HPV2). Quantitative data were analysed using logistic regression to identify factors associated with vaccine completion. In addition, 21 key informant interviews involving 11 healthcare workers and 10 Village Health Team (VHT) members were conducted and analysed thematically to explore contextual barriers affecting HPV vaccine uptake and completion. The HPV vaccine completion rate was 23.49%, indicating low coverage. In multivariable analysis, vaccine stock-outs and cold-chain challenges (adjusted odds ratio [AOR] = 1.75, 95% confidence interval [CI]: 1.04-2.93; p = 0.004) and understaffing of healthcare workers (AOR = 1.97, 95% CI: 1.05-3.68; p = 0.006) were the only statistically significant predictors of HPV vaccine completion. Although limited healthcare worker knowledge (AOR = 0.94, 95% CI: 0.70-1.24) and absence of government programmes targeting out-of-school girls (AOR = 0.97, 95% CI: 0.73-1.29) were not statistically significant in the adjusted model, qualitative findings highlighted them as important contextual barriers. Additional challenges identified included weak outreach systems, transportation constraints, misconceptions about HPV vaccination, and limited community awareness. HPV vaccine completion in rural Uganda remains low and is strongly influenced by health system constraints, particularly vaccine supply-chain disruptions and human resource shortages. Strengthening vaccine logistics, improving staffing levels, enhancing healthcare worker capacity, and expanding outreach strategies targeting underserved populations are essential for improving vaccine completion and achieving national immunisation targets.

Oral diseases remain a major public health challenge in Mexico. Despite the proven effectiveness of preventive measures, dentists do not consistently implement them in clinical practice. Understanding the factors underlying this gap is essential to strengthening prevention-oriented care. To identify perceived barriers to implementing preventive actions in dental practice. A qualitative study based on grounded theory was conducted using focus groups. Twenty-seven dental professionals from urban, semi-urban, and rural settings in Mexico participated in five virtual sessions. Data were recorded, transcribed, and analyzed using thematic coding. Barriers to implementing preventive actions in dental practice were grouped into three categories: patient-related (lack of preventive habits, misinformation, financial constraints, fear of dental visits); dentist-related (biomedical training, professional prestige linked to complex procedures, financial incentives, and time constraints); and structural (limited institutional support, unequal service distribution, and poor integration of oral health into health policies). A subgroup of dentists reported consistent implementation of preventive actions, characterized by a biopsychosocial approach, stronger communication skills, and a health promotion-oriented professional identity. Cultural, professional, and structural factors interact to shape the implementation of prevention in dental practice in Mexico. Strengthening communication skills, reorienting dental education toward prevention, and enhancing institutional support may facilitate the integration of preventive actions into routine care. Understanding multilevel barriers to prevention may help dentists integrate preventive actions into routine care by strengthening communication strategies, addressing patient expectations, and adapting clinical decision-making to social and structural contexts.

Initiation of resuscitation (IOR) in intensive care units (ICUs) is a high stakes and ethically complex decision, in which nurses are often the first professionals to recognise cardiac arrest and act. Although resuscitation guidelines provide standardised algorithms, nurses' real-world IOR decisions are shaped by intertwined clinical, ethical, organisational and sociocultural factors. This study aimed to explore and explain the IOR decision-making process among Iranian ICU nurses. This qualitative study used Strauss and Corbin's grounded theory methodology. Fourteen participants, including ICU nurses, nurse managers and physicians, were recruited through purposive and theoretical sampling from university hospitals in Iran. Data were generated through 16 in-depth semi-structured interviews and field notes. Analysis followed constant comparative methods with iterative open, axial and selective coding supported by memo writing. Methodological rigour was ensured through adherence to Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines and Lincoln and Guba's criteria for trustworthiness. Nurses' main concern was living in a 'limbo between duty and outcome', reflecting tension between legal-professional mandates for IOR and realistic expectations regarding prognosis and post-resuscitation quality of life. The core category, patient-centred self-protection, explained how nurses balanced ethical intent, anticipated patient benefit and self-preservation within contexts characterised by legal ambiguity, cultural-religious values, hierarchical dynamics and structural constraints. Three interrelated strategies-rule-driven, ethics-driven and defensive-led to consequences ranging from moral distress and disengagement to professional moral-existential fulfilment. IOR decision-making in ICUs is dynamic, context-dependent and morally complex, requiring nurses to continuously negotiate competing expectations under uncertainty. Context-sensitive CPR guidelines, legal clarity, ethical support, effective leadership and simulation-based education may strengthen nurses' moral agency, reduce defensive practice and promote humane, patient-centred resuscitation in critical care settings.

We aimed to explore the ethical challenges encountered by nurses when providing family-centered care. Thematic analysis was conducted using data from 82 semi-structured interviews with practicing nurses across four states in the United States. Theme 1: Navigating professional and ethical boundaries in nursing care that complement or complicate family advocacy; Theme 2: Experiencing family conflicts and disagreements; Theme 3: Feeling helpless at times from ethical, legal, and familial expectations and moral constraints. Participants showed commitment to involving families in patient care. Yet, we also observed tension in balancing respect for families' wishes with patient autonomy, which led to moral distress and ethical uncertainty in their professional boundaries. While family-centered care recognizes families' contributions to patient care and health care decisions, ongoing efforts are needed to address the ethical challenges that arise in its implementation in practice.

Protein function annotation traditionally follows a reductionist approach, assigning functions to individual proteins acting in isolation. This treats each annotation as an independent fact, disconnected from the broader biological system. However, proteins operate within integrated networks where their functions depend on genomic context and interacting partners. This needs to be reflected in function annotation and evaluation frameworks. We assess whether annotated protein functions could plausibly coexist within a living organism. To achieve this goal, we formalize three criteria grounded in systems biology principles: completeness (presence of essential functions), coherence (satisfaction of functional dependencies), and consistency (absence of mutually exclusive functions). We applied this framework to manually curated function annotations from six model organisms and computational function predictions from seven methods. While model organism annotations largely satisfied our constraints, computational function prediction methods systematically failed to produce biologically plausible genome-scale annotations. Our review reveals a measurable gap between the per-protein objectives of current annotation methods and the system-level criteria that an annotation set must satisfy to describe a viable organism. Our evaluation framework grounded in systems biology principles provides quantitative metrics for evaluating biological plausibility and establishes a foundation for developing system-aware annotation approaches. Augmenting protein-level annotation with system-level criteria offers a tractable path to improving annotation of the rapidly growing collection of sequenced genomes and metagenomes.

Electrochemical nitrogen reduction reaction (NRR) provides a promising pathway for sustainable ammonia synthesis under ambient conditions and direct integration with renewable electricity. However, practical implementation remains limited by sluggish N2 activation, severe competition from the hydrogen evolution reaction (HER), low ammonia partial current densities, and insufficient long-term stability. Existing studies frequently address these challenges separately, focusing on catalyst classes or mechanistic pathways, leaving a gap between atomic-scale materials design and system-level requirements for scalable operation. In this Review, we present an integrative perspective on electrocatalytic NRR that links reaction kinetics, descriptor-guided materials design, and reactor-level considerations. Emerging catalyst architectures, including single-atom, dual-atom, vacancy-engineered, and metal-free systems, are critically evaluated, highlighting how cooperative active sites, electronic-structure modulation, and defect chemistry regulate N2 adsorption, stabilization of key intermediates (particularly NNH), and suppression of HER. Mechanistic descriptors, scaling relations, and design principles are discussed alongside experimental performance trends to clarify thermodynamic and kinetic limits governing selectivity. Beyond catalyst discovery, we examine electrolyte and interphase engineering, gas-liquid-solid transport, pressure and flow management, durability, and ammonia handling. By connecting catalyst design, mechanistic descriptors, reactor constraints, and techno-economic targets, this Review outlines credible pathways toward scalable electrochemical ammonia production.

Neural architecture search (NAS) can improve medical image segmentation, but its practical use is limited by computational cost and instability of the discovered architectures, particularly when applied to pre-trained models and limited data. We present Shapley-guided pruning as a practical validation-guided extension of retrofit NAS for pre-trained U-Nets. Rather than defining a new NAS family, the method keeps the IAC search space and PC-DARTS-style supernet optimization, while adding iterative pruning driven by Shapley value estimates computed on held-out validation data. By progressively removing low-impact components while preserving learned architecture parameters, the approach reduces search space complexity and improves the reliability of the final discrete architecture. We evaluate the method on four public benchmarks (ACDC, BraTS, KiTS, and AMOS) in a controlled 2D slice-based, [Formula: see text], single-GPU setting. Within this protocol, the proposed approach improves or matches strong baselines in most comparisons, accelerates search by up to four times, and yields more stable operation choices across runs. The findings support Shapley-guided pruning as a practical retrofit-NAS mechanism under resource constraints, without implying direct clinical competitiveness with high-resolution or fully 3D segmentation pipelines.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) represents a formidable challenge in the Intensive Care Unit (ICU), characterized by high transmissibility and mortality. Distinguishing between imported cases and nosocomial transmission is essential for effective infection control but remains challenging using conventional methods. This study aimed to characterize CRKP introduction and transmission dynamics using Whole-Genome Sequencing (WGS) and evaluate the discordance with standard epidemiological investigation. We conducted a retrospective observational study in a tertiary ICU from January 2019 to December 2021. Clinical and microbiological data were collected from all patients screened for CRKP. Standard infection prevention and control (IPC) investigation classified cases based on spatiotemporal overlap. WGS was performed on available isolates to analyze Core Genome Multi-Locus Sequence Typing and virulence profiles. Integrated genomic and epidemiological data were used to reconstruct precise transmission chains. Among 826 screened patients, 91 (11.02%) were CRKP-positive, classified by standard criteria as 56 imported and 35 ICU-acquired cases. WGS analysis of 61 isolates identified ST11-KL64 and ST15-KL19 as the predominant clonal lineages. Notably, ICU-acquired isolates exhibited significantly higher virulence scores compared to imported strains (P<0.05). Genomic surveillance resolved four distinct nosocomial transmission clusters involving nine patients. These transmission events were significantly associated with high-risk department history, prolonged ICU stay, readmission, and staffing constraints during the COVID-19 pandemic. WGS serves as a powerful complementary tool to resolve transmission chains and identify hypervirulent lineages. The findings highlight the critical need for maintaining adequate staffing resources and implementing precision IPC strategies to contain spread, particularly during healthcare crises.

Hematopoietic stem cell transplantation (HSCT) offers curative potential for hematologic malignancies and immune disorders, yet pulmonary complications remain major contributors to non-relapse morbidity and mortality. Traditionally attributed to immune suppression and graft-versus-host disease (GvHD), these complications are increasingly recognized to involve disruption of pulmonary microbial communities. A growing body of clinical and experimental evidence indicates that HSCT-associated perturbations in the lung microbiome, driven by conditioning, antimicrobials, immune injury, and infection, are associated with distinct post-transplant pulmonary phenotypes and, in some cohorts, with mortality risk. Whether these microbial shifts represent causal contributors to lung injury or contextual biomarkers of immune vulnerability remains unresolved, and this distinction carries direct implications for microbiome-targeted intervention. Dysbiotic shifts in the lung have been associated with both infectious and non-infectious complications, including idiopathic pneumonia syndrome, bronchiolitis obliterans syndrome, and fibrotic lung disease. Gut-lung microbial crosstalk may amplify or reflect systemic immune dysfunction, though the directionality of this relationship remains incompletely characterized. Multi-omics approaches, integrating metagenomics, metatranscriptomics, and metabolomics, are beginning to define the host-microbiome interaction signatures that distinguish injury subtypes and predict outcomes. This review synthesizes mechanistic insights into lung microbiome-immune interactions after HSCT, critically appraises the methodological constraints on the current evidence base, and evaluates microbiome-based interventions, including fecal microbiota transplantation, inhaled postbiotics, and precision antimicrobials, as candidate strategies for respiratory protection in transplant recipients, while acknowledging that prospective interventional evidence in this population remains limited.

Hospital-based treatment refusal among oral cancer (OC) patients remains poorly understood. No qualitative studies have examined this phenomenon specifically in OC. This study aimed to explore the beliefs and attitudes underlying treatment refusal among OC patients in Pakistan. We conducted a theory-informed qualitative case study guided by the Health Belief Model (HBM) using purposive criterion sampling. Semistructured interviews were conducted with eight OC patients who refused hospital-based treatment, eight family members involved in decision-making, and three oral surgeons. Interviews were audio-recorded, transcribed verbatim, translated into English, and analysed using inductive thematic analysis. Emergent themes were subsequently interpreted in relation to HBM constructs using abductive, theory-informed pattern matching. Methodological rigour was enhanced through data source triangulation and member checking. From the 19 in-depth interviews, we identified seven interconnected themes underlying treatment refusal: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, cues to action, and religious beliefs. Treatment refusal was shaped by fatalistic perceptions of cancer incurability, intense fears of treatment-related harm, and substantial financial, access, and role-related constraints. Strong family influence and trust in traditional and complementary medicine further reinforced these decisions. Religious interpretations functioned bidirectionally, reinforcing acceptance of divine destiny and legitimizing nonbiomedical healing pathways, while in some cases also supporting treatment-seeking through religious doctrine. Refusal of hospital-based OC treatment reflects a culturally embedded decision-making process shaped by interacting sociocultural, religious, and structural factors. The findings support the applicability of the HBM in this context and inform the development of culturally responsive, communication-focused interventions in oral oncology. Recognition of belief-driven treatment refusal can help clinicians tailor communication, address fears of treatment harm, and engage family and religious influences to improve acceptance of hospital-based OC care.

Targeted protein degraders (TPDs), including proteolysis-targeting chimeras (PROTAC) and molecular glue degraders (MGD), are among the most promising small-molecule-based drug treatments in oncology. The May 2026 FDA approval of vepdegestrant provides a regulatory milestone for heterobifunctional protein degradation and for PROTAC therapeutics. First generation TPDs were developed for oral delivery, however, the intrinsic physicochemical properties of TPDs impose constraints on their oral bioavailability, systemic exposure, target site accumulation and therapeutic efficacy. As the field transitions toward a second wave of TPD development, nanoparticle-based targeted protein degraders (nano-TPD) are gaining momentum for broadening the therapeutic landscape of protein degradation. In this context, drug delivery systems offer opportunities to overcome key translational barriers by improving pharmacokinetics, tissue distribution, target site localization, cellular uptake, and therapeutic index. We here provide an overview of TPD discovery, from early laboratory to (pre) clinical progress, discuss translational challenges, and suggest advanced drug delivery solutions to help realize the full potential of TPD therapies.

This work aims to investigate the benefits of incorporating fluid dynamic models into ultrasound vector flow imaging through a novel data assimilation framework using tensor product B-splines for model-based regularization. A variational data assimilation method was developed using tensor product B-splines with the goal of solving high-dimensional regularization problems governed by the Navier-Stokes equations. The method was implemented in an open-source library and validated across three experimental setups: in silico using a computational fluid dynamics phantom, in vitro using a pulsatile flow phantom with particle imaging velocimetry and in vivo using 4-D ultrasound compared with magnetic resonance imaging. The proposed method outperformed conventional smoothing techniques and matched the performance of state-of-the-art regularization approaches. In silico tests showed improved noise suppression and lower root mean squared error. In vitro experiments demonstrated accurate reconstruction of flow features and gradient-based metrics. In vivo comparisons revealed good agreement with magnetic resonance imaging in high-velocity regions and successful reconstruction in dropout zones. The data assimilation approach using B-splines and fluid dynamic constraints enables efficient and accurate reconstruction of 4-D flow fields in ultrasound vector flow imaging. It offers a promising solution for bedside clinical applications, balancing noise suppression and resolution while leveraging physical models for robust flow estimation.

Climate change is increasingly recognized as a public health issue that exacerbates existing social and structural inequities. While growing attention has been paid to gendered impacts of climate-related hazards, transgender populations remain largely absent from climate research, policy, and practice, especially in low- and middle-income country contexts. This article introduces Invisible in the Storm: Climate Change and the Lived Realities of Transmasculine People in India, a community-led and produced health promotion resource developed by Transmen Collective, India's first national organization dedicated to transmasculine rights and well-being. Based on a mixed-method, survey-based study conducted with transmasculine participants across multiple regions of India, the report documents how climate events such as heatwaves, floods, and water scarcity intersect with gender identity, health care and resource access, and mental and emotional well-being. Quantitative findings highlighted exposure to climate stressors and disruptions to essential resources, while qualitative narratives illuminated how climate stress is embodied through constraints on gender expression and experiences of discrimination. Together, these findings reveal how climate change amplifies existing inequities and how the needs of the transgender community are rendered invisible in climate planning and responses. Positioned as a community-led health promotion resource, Invisible in the Storm offers insights for practitioners, organizations, and policymakers across climate, disaster, and public health sectors, highlighting the urgent need to integrate gender-diverse perspectives into climate responses. The report also underscores the importance of integrating lived experience into climate responses and demonstrates the value of gender-inclusive approaches to advance climate justice and health equity.

Developing an accessible and scalable strategy for three-dimensional (3D) protein imaging with hard X-ray nanotomography at the single-cell level remains a major analytical challenge due to the lack of intrinsic molecular specificity. Here, we report a target-switchable nanoprobe system based on biotinylated metal nanoparticles (BioMNs) that enables dual-modal fluorescence and hard X-ray imaging of specific proteins in intact single cells. In this modular architecture, the metal nanoprobes serve as universal X-ray signal modules, while molecular specificity is introduced through streptavidin-mediated coupling to biotinylated antibodies, thereby decoupling molecular recognition from signal generation. Combined with synchrotron radiation hard X-ray nanotomography (SR-HXT), this platform allows 3D visualization of diverse protein targets by simply replacing the primary antibody, eliminating the need for customized probe synthesis for each new target. As a validation of its versatility, membrane-associated HER2 and nuclear Ki67 were imaged as representative targets, revealing distinct spatial distributions and nanoscale heterogeneity in intact cells. This target-switchable approach overcomes the constraints of conventional single-target probes and establishes a correlative fluorescence-X-ray workflow for scalable 3D molecular imaging at the single-cell level.