搜索结果：verified

To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson-Cook (ML-JC) frameworks based on artificial neural network (ANN) surrogate models. Two predictive frameworks-the parallel-decoupled PD-ANN-JC and the multi-objective integrated MOI-ANN-JC-were constructed. Quantitatively, both developed ML-JC frameworks achieve significantly higher stress prediction accuracy and superior generalization capability compared with the conventional JC model. Specifically, on the testing set, the MOI-ANN-JC framework yields an average absolute relative error (AARE) of 1.424% and an R2 of 0.997, outperforming the PD-ANN-JC framework (AARE of 3.246%, R2 of 0.988). On the validation set, the MOI-ANN-JC framework also demonstrates exceptional generalization, with an AARE of 3.302% and an R2 of 0.987. Scientifically, the superior performance of the MOI-ANN-JC framework stems from its ANN-mnδ surrogate model, which simultaneously predicts the strain hardening exponent n, thermal softening exponent m, and relative error δ directly from deformation parameters. This mutual coupling establishes an intrinsic correlation between m and n, successfully aligning with the physical reality wherein strain hardening and thermal softening are inherently linked during deformation. Qualitatively and practically, by integrating the MOI-ANN-JC framework into finite element (FE) simulation software, dynamic tracking and visualization of the thermal softening exponent m during warm deformation were achieved. Combined with FE simulations, Vickers hardness testing and EBSD observations, this study successfully establishes a direct qualitative spatial correspondence between low-m regions and macroscopic defects, which was further verified through the warm forging of a thin-walled dual-cavity component. Crucially, this approach for evaluating deformation stability bridges the gap caused by the inapplicability of conventional processing maps within this temperature regime, offering a robust and broadly applicable workflow for complex forming optimization.

Background: Peri-procedural management of mechanically ventilated intensive care unit (ICU) patients undergoing hyperbaric oxygen therapy (HBOT) requires coordinated preparation, chamber-compatible equipment management, transfer readiness, and restoration of standard ICU support. Evidence on HBOT-specific workflow vulnerabilities remains limited. This single-centre pilot survey described staff-reported observations of omitted, delayed, or incompletely performed peri-HBOT tasks and used them to inform a preliminary checklist prototype. Methods: Nineteen healthcare professionals involved in peri-HBOT care at a specialist hyperbaric centre in Poland completed an anonymous questionnaire based on one-month retrospective recall. Respondents indicated whether they had observed each listed task being omitted or incompletely performed at least once. Analyses used respondent-level counts, percentages, and Wilson 95% confidence intervals. Results: Before HBOT, the most frequently reported observations concerned preparation of the intubation set for transfer (14/19, 73.7%; 95% CI: 51.2-88.2), disconnection of the anti-decubitus mattress pump (8/19, 42.1%; 95% CI: 23.1-63.7), removal or replacement of hazardous bed materials (7/19, 36.8%; 95% CI: 19.1-59.0), and preparation of the self-inflating bag (6/19, 31.6%; 95% CI: 15.4-54.0). After HBOT, observations most often involved replacing fluid with air in the endotracheal tube cuff (11/19, 57.9%; 95% CI: 36.3-76.9), reconnecting the anti-decubitus mattress pump (10/19, 52.6%; 95% CI: 31.7-72.7), and reconnecting interrupted intravenous infusions (5/19, 26.3%; 95% CI: 11.8-48.8). Conclusions: Findings identified perceived peri-HBOT workflow vulnerabilities and informed a locally derived checklist prototype. They should not be interpreted as verified omission prevalence, event rates, patient harm, or checklist effectiveness; usability testing, refinement, and multicentre validation are required.

Rotating machinery has been widely used in industries, but it often faces a high incidence of sudden failures under harsh operating conditions. Therefore, ensuring its safety and reliability is of utmost importance. However, fault diagnosis frequently encounters challenges such as limited training samples and the susceptibility of individual vibration sensors to external interference and noise. To enhance the recognition accuracy of rotating machinery under noisy and small-sample conditions, a fault diagnosis method for small samples based on multi-sensor fusion and CWT-CNN-BiLSTM is proposed in the paper. Firstly, the data from the multi-sensor is concatenated and fused, and then converted into a two-dimensional feature image through CWT. This study introduces a CNN-BiLSTM model designed to extract pivotal features from images. The feasibility of this method has been verified using the rotating machinery fault diagnosis database made available by the Korean Institute of Science and Technology. The average diagnostic accuracy achieved is 99.90%. The experimental results show that the proposed method results in more accurate and robust fault classification under small-sample conditions.

Lanthanide doped upconversion nanoparticles have attracted widespread attention due to their unique and efficient anti-Stokes emission. However, the large specific surface area and high surface quenching rates pose significant challenges in achieving small upconversion nanoparticles with strong emission intensity. Herein, we identify the surface defects that disrupt the crystal lattice periodicity as lanthanide cation vacancies and propose an effective localized lattice reconstruction strategy to block undesired energy transfer from excited states to surface quenching sites in LiYF4:Yb,Tm upconversion nanosystems. The improvement in upconversion performance is verified at the single nanoparticle level, eliminating the macroscopic statistical averaging inherent in ensemble measurements using solution- or powder-based systems. Notably, the emission intensity enhancement becomes more pronounced as nanoparticle size decreases. An ∼60-fold emission enhancement of the 1G4 → 3H6 transition is achieved on 13.5 nm nanoparticles without increasing the particle size, which demonstrates the significance of suppressing surface quenching for small nanoparticles. This lanthanide ion-assisted post-annealing strategy for surface lattice reconstruction could promote the development of small but bright upconversion nanoparticles for advanced applications.

Phyllanthus emblica is valued for its nutritional and medicinal properties, yet the genomic divergence between localized and widespread cultivars remains poorly understood. We investigated the genomes of two individuals from the endemic cultivar 'Hongguang' (HG), propagated via regional grafting, and the commercially widespread 'Dongkeng' (DK), known for its superior protein content. Using whole-genome sequencing, we reconstructed phylogenies from two nuclear markers, profiled genome-wide variations, assembled chloroplast genomes, and verified relative plastid copy numbers via real-time quantitative PCRs (qPCRs). Nuclear internal transcribed spacer (ITS) and phytochrome C (PHYC) phylogenies confirmed both samples belonged to the P. emblica lineage, while revealing a distinct genetic identity for the HG individual. Genome-wide variant profiling of the two individuals identified KEGG enrichment in plant hormone signaling pathways; DK variants mapped to the canonical auxin axis, while HG variants were annotated to reversible protein phosphorylation. Comparative chloroplast genomics demonstrated shared maternal inheritance and shared mutations in key photosynthetic genes (psaB, petA, and the ndh cluster) between the two genomes, though qPCR validation revealed a higher relative chloroplast DNA copy number in the DK sample. Despite the two-individual limitation, these findings revealed preliminary genomic variations, offering candidate molecular markers for future population studies and marker-assisted breeding.

High-precision absolute radiometric calibration is the cornerstone of space-based quantitative remote sensing. Monochromator-based radiance calibration is a key approach for space-borne radiometric benchmark transfer; however, the influence of the monochromator bandwidth on calibration accuracy urgently requires validation via an independent, SI-traceable benchmark. Taking a spectrometer as a case study, this paper establishes an equivalent theoretical calibration model. The model reveals that discrepancies in the equivalent spectral distributions of the calibration sources primarily contribute to their calibration deviations. Within the 775-855 nm range, cross-comparison experiments were conducted between the monochromator facility and a reference standard lamp-plaque. Experimental results demonstrate that under bandwidth configurations of 2 nm, 4 nm, and 6 nm, relative deviations in calibrated radiance responsivity stabilize at approximately 2.3%, exhibiting no significant dependence on bandwidth variations. Crucially, the consistency between the two methodologies is rigorously verified, as the radiance responsivity ratios uniformly fall within the combined expanded measurement uncertainty bounds. By demonstrating that bandwidth expansion within the specified 2-6 nm range does not degrade calibration accuracy, this study provides a critical empirical basis for safely increasing solar monochromator bandwidths to improve the signal-to-noise ratio (SNR) in next-generation benchmark missions like the Space-based Radiometric Measurement Benchmark Mission (LIBRA) program.

The wheat curl mite (WCM), Aceria tosichella Keifer, is an important pest of winter wheat (Triticum aestivum L.) in the North American Great Plains region because of its ability to transmit three important viruses to wheat. Wheat streak mosaic virus (WSMV; Tritimovirus tritici) has been the most studied of these three viruses, but the rapid spread of Triticum mosaic virus (TriMV; Poacevirus tritici) throughout the region, along with its synergistic relationship with WSMV, has increased the need for a more complete understanding of its transmission capabilities and epidemiology. Studies were conducted to determine the acquisition and retention periods of TriMV by the WCM. TriMV acquisition by the WCM began after just 1 h of feeding, but transmission occurred at a low efficiency (2.9%). Transmission efficiency increased to a maximum level (32%) by 24 h of feeding. The virus retention period was determined by holding viruliferous adult mites on a good mite host, but one that is a nonhost for TriMV (barnyardgrass). After a base transmission rate of 40% when the retention study was initiated, transmission rates declined to 2% on day 6 and 9% on day 8. This study is the first to document the acquisition and retention characteristics of TriMV by the WCM and demonstrated that these characteristics are similar to those previously determined for WSMV transmission by the mite. The study also verified that WCM adults are incapable of acquiring and subsequently transmitting TriMV, and thus, the virus must be acquired by immature mites. Establishing transmission characteristics for the viruses in the wheat mite-virus complex is critical for a complete understanding of plant-vector-virus interactions and their influence on the epidemiology of this complex.

This study comprehensively examines the whole-genome sequence and probiotic potential of Lacticaseibacillus paracasei RM081, a strain originally isolated from raw bovine milk. Whole-genome sequencing and in silico analyses provided a robust molecular basis for its functional traits. The L. paracasei RM081 genome harbors an extensive repertoire of carbohydrate-active enzymes, suggesting strong prebiotic utilization capabilities. Crucially, genomic mining identified key genetic determinants for postbiotic synthesis, including the potential to synthesize the anti-inflammatory metabolite 5-methoxytryptophan (5-MTP). Moreover, comprehensive safety evaluations confirmed the absence of transferable antimicrobial resistance genes, virulence factors, biogenic amine-producing genes, and plasmids, indicating a secure genomic architecture without horizontal gene transfer risks. These genomic predictions were further substantiated by valid in vitro phenotypic models. The strain exhibited strong tolerance to gastric acid, maintaining high viability at pH 3.5 and 2.5 after 4 h, and survived well at 0.1% bile salt concentration. Furthermore, L. paracasei RM081 demonstrated robust cell surface properties, with a high auto-aggregation rate (85.0 ± 0.7%), hydrophobicity (71.5 ± 2.4%), and 78.0 ± 4.8% adhesion to Caco-2 intestinal epithelial cells, supporting its potential for colonization. Regarding antioxidant capacity, the cell-free supernatant displayed the highest DPPH scavenging activity (37%), indicating the active secretion of antioxidative metabolites. Collectively, these findings establish L. paracasei RM081 as a highly promising, safe probiotic and postbiotic candidate with verified colonization potential and functional capabilities.

Vacuum induction melting (VIM) of recycled powder with bulk master alloy represents an industrialized approach for recycling metallic waste. However, the intrinsic mechanisms governing the co-melting behavior of materials with distinct melting characteristics, such as powder bed and bulk alloy, remain insufficiently understood. To address this, a coupled multiphysics model was developed to simulate the evolution of induction melting involving homogeneous alloys with different morphologies. This model integrates magnetic, electric, and phase-field dynamics while incorporating melt convective heat transfer, thereby establishing a fully coupled electromagnetic-thermo-hydrodynamic framework. Through this modeling approach, the entire VIM process of melting homogeneous alloy with different morphologies can be comprehensively analyzed. The validity of the model was verified via small-scale VIM experiments using FGH96 powder/bulk composite, supported by infrared temperature measurements. This simulation methodology is not only applicable to small-scale recycling but can also be extended to large-scale industrial production, providing a reliable theoretical foundation for the recycling of powder materials.

Small ruminant lentivirus (SRLV) infects goats and sheep of all breeds and ages worldwide. There are no current records regarding the three-dimensional structure or antigenic capacity of the nucleocapsid protein p14 of FESC-752 Mexican strain. The antigenic structure of p14 protein of a B1 genotype was predicted. cDNA from FESC-752 was used to overexpress the recombinant SRLV-rp14 protein. Then, its antigenicity was verified in vitro by evaluating plasma samples from goats and sheep naturally infected with SRLV. Antigenicity prediction showed a "horseshoe"-type structure shared by different lentiviruses and five epitopes distributed throughout the p14 surface regions where they coincide suggesting conserved epitopes in the zinc-finger structures of the nucleoproteins of the SRLV, Human Immunodeficiency Virus (HIV-1), and Feline Immunodeficiency virus (FIV) retroviruses. Multi-species molecular docking showed a notable structural convergence where caprine, bovine, murine, and human immunoglobulins target a predictive 23 amino acid epitope (residues 41-64) within the core zinc-finger region. Furthermore, CABS docking simulations predicted that p14-derived peptides preferentially bind within the antigen-presenting cleft of both caprine and bovine major histocompatibility complex class I (MHC-I) molecules. The stability of these immunological complexes is mediated by dense networks of hydrophobic interactions and highly conserved aromatic anchoring residues. Antigenicity analysis revealed that 78.7% of samples from naturally infected goats showed immunoreactivity toward SRLV-rp14 and the predictive evidence that p14 can simultaneously stimulate both humoral and cellular pathways makes it a strategic candidate for the design of next-generation vaccines aimed at controlling lentiviruses in small ruminants.

Unmanned aerial vehicle semantic communications are increasingly required in low-altitude sensing, intelligent inspection, and emergency response, where raw image transmission is difficult to sustain under limited onboard resources and time-varying air-to-ground links. Meanwhile, the simultaneous transmission of visual semantic features and object-centre location metadata under third-party eavesdropping creates a dual-privacy vulnerability: an attacker can exploit both to reconstruct sensitive content. In this paper, we propose a differential privacy-based collaborative protection framework that inserts dedicated perturbations into visual semantic and location descriptors before transmission. For visual data, we design a region-aware differential privacy mechanism that applies stronger noise to sensitive semantic regions while preserving utility for non-critical areas. For location data, a scenario-adaptive strategy is developed, comprising randomized differential privacy for discrete grid-based location information (coarse spatial awareness) and Laplace-based differential privacy for continuous coordinates (fine-grained protection). To balance privacy and utility, we formulate a joint optimization problem. It maximizes legitimate-side semantic task performance by coordinating the visual privacy budget, location privacy budget, and transmit power. A BCD-based algorithm is developed to solve this non-convex problem. Attacker-side recoverability is verified empirically at the optimized operating point. Simulation results demonstrate stable convergence within a small number of iterations. Compared with uniform differential privacy, the proposed framework achieves a superior task-level privacy-utility trade-off and provides selective sensitive-region protection, with the two mechanisms yielding comparable whole-image attack suppression.

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic tool used to predict how a drug moves through the body by incorporating real human physiology, including organ sizes, blood flows, tissue compositions, and enzyme activities. It has been widely employed to estimate drug exposure in different populations with organ impairment, genotype variabilities, and physiological variations. Metoclopramide is an antiemetic and prokinetic agent that is subject to CYP2D6 polymorphism. The study aims to develop PBPK models for several CYP2D6 variants to predict changes in the pharmacokinetic (PK) behavior of metoclopramide. Methods: To conduct this study, a literature review was conducted, and the retrieved physicochemical, biochemical, and PK data were integrated into PK-Sim to develop a PBPK model. Initially, a non-genotype-specific model was developed and extrapolated to genotype-based models. The models were verified using a Visual Predicted Check (VPC), mean predicted-to-observed ratio (Rpre/obs) values, and mean relative deviation (MRD). Results: The simulated profiles were aligned with the reported data, and all the predicted and observed PK parameters were comparable, as the Rpre/obs values were within the 0.5-2 range and MRD values were <2. Moreover, an increasing trend in AUC0-&#x221e; was observed across CYP2D6*wt/*wt, CYP2D6*wt/*10, CYP2D6*10/*10, and CYP2D6*5/*10, with approximately 1.63-, 2.64-, and 2.88-fold increases compared with the CYP2D6*wt/*wt genotype. Conclusions: The models have adequately estimated the PK behavior of metoclopramide across different CYP2D6 variants. These models might be helpful for populations with diverse CYP2D6 genotypes in dose optimization.

Aiming at the issue of poor robustness in continuous control set model predictive current control (CCS-MPCC) algorithm, a class of model predictive controllers based on linear-nonlinear switching extended state observers has been designed. Theoretical derivations demonstrate the structural equivalence between the extended state observer-based deadbeat model predictive current controller and the active disturbance rejection control (ADRC) and the errors introduced into the MPC algorithm's predictive output by parameters such as motor resistance, flux linkage, and inductance have been calculated. Subsequently, a linear-nonlinear state observer was employed to detect disturbances in the system caused by parameter deviations and the observed disturbance values were fed back into the current loop controller in real time, thereby improving the prediction accuracy of the CCS-MPCC algorithm. Finally, the control algorithm was verified on a 5.5 kW experimental platform of permanent magnet synchronous motor (PMSM). The experimental results show that when the model parameters and actual parameters have deviations, the model predictive current control algorithm based on the linear-nonlinear switching extended state observer has a better speed response effect, and can reduce the harmonic content of the original method by up to 1.03%, 1.25%, and 1.12% under three operating conditions.

Accurate monitoring of internal winding temperature is essential for assessing the thermal state and operational reliability of oil-immersed transformers. However, direct deployment of distributed temperature sensors inside transformer windings is difficult because of insulation constraints, structural complexity, and potential reliability risks. To address this problem, this paper proposes a non-invasive internal winding temperature estimation method based on surface temperature sensing and a hybrid deep learning model. In the proposed framework, external surface temperature measurements are used as sensor inputs to infer the internal transient thermal state of the transformer. First, an extreme gradient boosting (XGBoost) model is employed to evaluate the contribution of different surface temperature measurement points and select the sensing locations that are most strongly correlated with internal winding temperature variations. Then, the selected surface temperature time-series data are used to train a Long Short-Term Memory (LSTM) network, which captures the temporal evolution of the transformer temperature field under different operating conditions. The proposed method is verified through both numerical simulation and experimental testing on a scaled single-phase oil-immersed converter transformer model (D-800/35) developed in this study. The results show that the proposed XGBoost-LSTM model can estimate internal winding temperature with an error of less than 1.5 K. Compared with direct internal sensing, the proposed method provides a non-invasive and sensor-efficient solution for internal temperature monitoring. The results demonstrate its potential for real-time thermal state estimation, condition monitoring, and fault diagnosis of oil-immersed converter transformers.

African swine fever in both domestic and wild pig populations is caused by the extremely infectious African swine fever virus (ASFV). It seriously endangers biodiversity and results in large financial losses for the worldwide pork sector. The major capsid protein p72 is molecularly chaperoned by the ASFV pB602L protein, which is essential to viral assembly. Furthermore, as a nonstructural protein expressed at late stages of infection, pB602L induces a distinct antibody response that may complement existing serological assays based on structural proteins. Given its strong immunogenicity, pB602L represents a promising antigen for developing supplementary diagnostic tools for African swine fever (ASF). In this study, we successfully generated and separated the ASFV pB602L protein, and we verified its responsiveness using serum from pigs infected with ASFV. Additionally, we produced four monoclonal antibody-specific hybridoma cell lines that targeted the pB602L protein exclusively. These cell lines demonstrated high immunoreactivity and responsiveness toward ASFV pB602L. These results highlight the potential enhancement of diagnostic skills. We have detected two previously unknown linear B-cell epitopes (138TIDSFL143 and 164TNVDTC169) using overlapping peptide and truncated protein fragment analysis. Due to their high degree of conservation across various ASFV strains, these epitopes offer trustworthy candidates for the creation of particular diagnostic instruments. This study expands the known ASFV antigenic repertoire by systematically mapping immunodominant epitopes of pB602L. The identified epitopes provide potential molecular targets for the rational design of multi-epitope subunit vaccines.

Background/Objectives: The aim of current study was the significant improvement of both the flowability and the compressibility of mesoporous silica microparticles (MSMs), to enable the formulation a potential drug delivery system. MSMs are of emerging interest in the pharmaceutical industry, due to their numerous advantages and versatile applicability, such as improvement in aqueous solubility and epithelial permeability, thus enhancing the oral bioavailability of drugs. However, the formulation of these types of materials has been a major challenge. This problem originates from poor powder flow characteristics due to particle properties. Methods: A binder-free high-shear wet granulation (HSWG) process was performed to improve the flowability and compressibility of the model material, meanwhile preserving its porosity. The prepared granules were characterized by particle size, size distribution, yield percentage, particle morphology, porosity, powder flowability, crushing strength, and stability. Micro-CT measurements were performed to examine the structure of the granules and to see the internal segmentation resulted by the two-step granulation process. The granules were compressed into tablets to evaluate the compressibility behavior based on the models of Kawakita and Walker. The physical parameters of the compressed tablets, such as breaking hardness, tensile strength, and thickness, were tested. Results: The prepared granules were evaluated successfully according to the mentioned properties and found to be satisfactory compared to the raw materials. The binder-free method appeared to be effective, thus the use of binders may be avoided if the process is designed well and critical process parameters (CPPs) selected carefully. The granules showed good stability over a one-year testing period. The micro-CT test also verified the success of the initial concept of preparing core-shell structured granules, and enabled the determination of macropores. Nevertheless, the results were completed with BET measurements to determine specific surface area of the granules. Conclusions: The effect of the critical process parameters of the granulation process on all the mentioned attributes was investigated and since major differences were observed between the batches, the effect of the selected CPPs were also verified.

Rusty root rot of ginseng (Panax ginseng) caused by Ilyonectria spp. infection is a devastating soil-borne disease restricting the sustainable production of garden-cultivated ginseng (GCG) in Northeast China and causes severe yield and economic losses; GCG is far more susceptible to this pathogen than forest-cultivated ginseng (Lin-Xia-Shan-Shen, LXSS). Ginsenosides, the signature triterpenoid saponin defensive metabolites of ginseng, are characteristic dammarane-type triterpenoid defensive saponins represented by Re, Rg2, Rb1, Rd, and Rg1. These compounds are continuously secreted into the rhizosphere and widely participate in plant-microbe interactions, yet their functional roles in mediating Ilyonectria infection remain poorly clarified. This study aimed to clarify how rhizospheric ginsenosides regulate the infection process of pathogenic Ilyonectria strains. Two pathogenic strains, Ilyonectria sp. SYM-1 and Ilyonectria sp. SYM-2, were found isolated from diseased GCG roots and verified as causal agents via morphological observation, molecular ITS identification and artificial inoculation infection experiments. Interestingly, the concentrations of five ginsenosides, Re, Rg2, Rb1, Rd, and Rg1, in the rhizospheric soil of GCG with rusty root rot were significantly higher than those in the rhizospheric soil of healthy LXSS plants. In addition, the concentrations of ginsenosides in the LXSS rhizospheric soils decreased with increasing age of plants. Non-nutritive suspension co-culture assays showed that high concentrations of the ginsenosides Rg1 and Rd significantly promoted spore germination of the strains SYM-1 and SYM-2. However, Rb1 had a certain inhibitory effect on the growth of Ilyonectria sp. SYM-2. Host inoculation experiments further indicated that infection with either fungus significantly reduced the concentrations of ginsenosides produced in ginseng roots. These results demonstrate that the pathogenic fungi SYM-1 and SYM-2 of Ilyonectria can adapt to and utilize ginsenosides. Collectively, these findings prove that the two pathogenic Ilyonectria strains have evolved the capacity to adapt to and exploit rhizospheric ginsenosides to facilitate their infectivity. From an application perspective, reducing rhizospheric ginsenoside release may represent a promising theoretical strategy for ginseng cultivation and germplasm improvement, which warrants further verification by field or greenhouse experiments for validation.

The Social Determinants of Health (SDH) encompass the systemic conditions shaping daily life and health outcomes. In the Islamic Republic of Iran, health equity and SDH have been institutionalized as core components of sustainable national policy to address the "causes of the causes" behind health disparities. This study aims to evaluate the strategic evolution of Iran's health system, focusing on upstream SDH policies, intersectoral institutional frameworks, national research productivity, and the longitudinal development of monitoring mechanisms established to identify and mitigate inequities. This is a policy review article with qualitative key-informant input, covering the initiation of SDH efforts through the end of 2023. The study analyzed 25 national upstream policies, the functionality of 32 effective intersectoral councils, and the outputs of 38 specialized SDH research centers. Primary data were gathered through 26 semi-structured interviews with key authorities conducted between 2020 and 2023 and verified through member checking. Since the 1980s, Iran has established significant infrastructure, primarily through the Primary Health Care (PHC) network. Key reforms include the Universal Health Insurance Act (1994) and the Comprehensive Welfare and Social Security System (2004). Governance is driven by the Supreme Council of Health and Food Safety. While research output is robust-exceeding 4,200 indexed articles-operational challenges persist, including a 39% out-of-pocket expenditure rate, overlapping council mandates, and the deteriorating impact of international sanctions on equitable access. Iran possesses an advanced architecture for tracking 69 health equity indicators. However, reducing disparities requires moving beyond indicator collection to sustained political commitment, legally institutionalized intersectoral alignment, and translating data into targeted socioeconomic interventions.

Chronic Pain (CP) affects approximately one in five adults and is associated with emotional distress, disability, reduced productivity and substantial healthcare and societal costs. Acceptance and Commitment Therapy (ACT) has demonstrated effectiveness in improving emotional, behavioral and functional outcomes in individuals with CP, but access to ACT is frequently limited by geographic, organizational and workforce barriers. Internet-based interventions may help overcome these limitations by increasing accessibility, reducing treatment costs and improving scalability. This study evaluates the effectiveness and cost-effectiveness of MobACT, a guided internet-based ACT programme for Italian adults with CP. The primary effectiveness endpoint is the post-intervention assessment (T1). This two-arm randomized controlled trial will allocate adults with medically verified CP to either immediate MobACT or waitlist control (1:1). Participants in the waitlist control condition will receive access to MobACT only after completion of the T1 assessment, following the primary between-group comparison. A 6-month follow-up (T2) will be conducted in the intervention group only to examine maintenance of treatment effects. The primary outcome will be pain acceptance, assessed with the Chronic Pain Acceptance Questionnaire. Pain intensity and pain interference will be key clinical secondary outcomes. Additional secondary outcomes include quality of life, sleep quality, central sensitization, pain catastrophizing, psychological flexibility, self-efficacy, coping, anxiety, and depressive symptoms. Weekly assessments of sleep quality and changes in pharmacological treatment will also be collected during the active intervention period. The economic evaluation will compare the two conditions from healthcare and societal perspectives using service utilization data, productivity indicators, and EQ-5D-3L-derived quality-adjusted life years. Ethical approval was obtained from the Ethics Committee of Universit&#xe0; Cattolica del Sacro Cuore, Milan (Approval No.: 146/24). Results will be disseminated through peer-reviewed publications, international conferences and stakeholder reports for patient organizations. ClinicalTrials.gov Identifier: NCT07270588.