Abstract: The study examined whether cow's milk and beef differ from plant-based alternatives based on emotional profiling. It also sought to determine whether two panels of trained observers with different levels of preparation and experience would reach consistent results. Cow’s milk was compared with almond drink and oat drink, and beef with tofu and seitan. All products were organic. Two panels of trained observers (n=8/10, n=9/11) evaluated the products. The EmpathicFoodTest® (EFT) was used for emotional profiling. Cow’s milk, almond drink, and oat drink could be clearly distinguished from one another. Significant effects were observed in all 12 items of the EFT. Beef also differed significantly from the plant-based alternatives tofu and seitan, specifically in 8 and 9 individual characteristics, respectively. However, the differences between tofu and seitan were not significant. Milk and meat differed primarily in the items “warm/cold” and those related to emotional well-being compared to plant-based alternatives. The two independent panels, one with many years of experience, the other with brief, intensive preparation, achieved largely consistent results. Very good consistency was found for the warm/cold item, the duration item, and the items of emotional well-being. Emotional profiling can describe meat, milk, and plant-based alternatives. Intensive training over a short period enables a panel to achieve results comparable to those of a panel with several years of experience.

Abstract: This paper presents the complete design and development of a dried whole blood cartridge designed for point-of-care (POC) clinical diagnostics. The system integrates a near-infrared (NIR) spectroscopy sensor with a disposable multilayer paper cartridge capable of collecting and analyzing small, controlled volumes of capillary blood (20 μL). The work emphasizes a technical and iterative design approach that combines product design with both additive and subtractive prototyping, supported by experimental validation. The development process involved multiple design iterations focusing on fluid transport, capillary dynamics, usability, and optical integration. Several materials and manufacturing processes, such as CNC (Computer Numerical Control) machining and Material Jetting (MJ), were explored to optimize channel geometry and flow behavior. Experimental results guided successive refinements, leading to a cartridge configuration that ensures efficient capillary action, minimal coagulation, and consistent optical alignment with the sensor’s analysis zone. The study underscores the importance of an integrated engineering approach that unites design methodology, material selection, and manufacturing processes to achieve a reliable and reproducible cartridge for point-of-care blood diagnostics. It demonstrates how iterative design, supported by experimenal testing, can effectively bridge the gap between experimental prototyping and practical implementation in medical device development.

Abstract: This study analyzes the relationship between trade openness, FDI, and economic growth in Ho Chi Minh City within the framework of an extended Solow model, using annual time series data from 2000 to 2024, and provides empirical evidence at the municipal level for Vietnam's leading economic and integration center. The trade open-ness is separated into the ratio of exports and imports to GRDP to reflect the different impacts of economic integra-tion. The ARDL and ECM model are applied in order to instantaneously analyze both short-term and long-term ef-fects. The results show that the variables have mixed integration orders. Capital is the factor with the most positive and stable impact in the long term. Meanwhile, relative exports and FDI have a positive impact in the short term but a negative one in the long term, implying that the benefits of integration depend on the quality of international trade, the ability to absorb technology, and domestic linkages. The negative and statistically significant error correc-tion coefficient indicates the existence of an adjustment mechanism toward equilibrium. The robustness check with the COVID-19 dummy variable approves the stability of the main results. The study points out that the necessity of changing from extensive quantitative integration to enhance the quality of growth and the efficiency of resource allocation.

Abstract: This paper introduces bending anisotropy of gun drill rods, caused by asymmetric cross-section, into chatter stability analysis. A dynamic model considering different stiffnesses along the two principal inertia axes is established. The Galerkin method and semi-discretization method are used to solve the governing equations and generate stability lobe diagrams. Parameter sensitivity analysis shows that drill rod length and material damping coefficient are high-sensitivity parameters, while coolant hole size and eccentric position are low-sensitivity ones. The results reveal the mechanism of bending anisotropy on stability and provide theoretical guidance for chatter suppression in deep-hole machining.

Abstract: This paper focuses on the optimization of engineering decision-making under uncertain environments. Engineering decision-making requires optimizing the input of production materials and the selection of equipment and processes under the constraints of cost and expected return to minimize costs and maximize production benefits. As an efficient formal verification technique, model checking provides a new approach to solve this problem. Traditional model checking mainly focuses on qualitative verification, while quantitative model checking techniques (such as probabilistic and possibilistic model checking) have been developed gradually, among which possibilistic model checking is more suitable for systems with fuzzy uncertainty. However, existing possibilistic model checking techniques have obvious defects: first, they only target closed systems and do not consider the interaction between the system and the external environment; second, the simple information aggregation method leads to information desynchronization and information loss; third, they cannot model and verify systems with incomplete information. Model checking technology based on possibilistic decision processes considers uncertain action selection and initially solves the problem of modeling and verification of open systems. The author has introduced the idea of quality constraints into possibilistic temporal logic to solve the problems of information desynchronization and information loss in possibilistic model checking; moreover, the author has established the theories of Intuitionistic Fuzzy Kripke Structure (IFKS) and Intuitionistic Fuzzy Computation Tree Logic (IFCTL), which can model and verify systems with incomplete information. To improve the usability and accuracy of engineering decisions, this paper will draw on the ideas and methods of uncertain selection of decision behaviors, quality constraints, and incompleteness modeling, extend IFKS to Weighted Intuitionistic Fuzzy Kripke Structure (WIFKS), induce IFCTL to Intuitionistic Fuzzy Decision Tree Logic (IFDTL), propose an algorithm for solving IFDTL model checking problems, and present a solution algorithm for multi-attribute engineering decision-making based on IFDTL model checking, along with its correctness proof and complexity analysis. Finally, a case study of Qinling health-preserving tourism planning is given to verify the rationality and efficiency of the proposed method, providing a new formal solution for uncertain engineering decision-making.

Abstract: Precision agriculture increasingly relies on high-resolution, long-term remote sensing to delineate sub-field management zones. However, traditional spatial zonation assumes temporal stationarity, utilizing seasonal aggregates that obscure transient, intra-annual stress signals. This study develops a data-driven framework to characterize both persistent and non-stationary crop water use dynamics by integrating monthly, 30-meter evapotranspiration (ET) data from OpenET (2000–2025) with zero-shot temporal anomaly detection. A pre-trained time-series foundation model (Chronos-T5-Small) generated counterfactual expectations for sub-field ET, quantifying deviations using a mean absolute error-based anomaly score. Unsupervised clustering of these anomaly scores with longitudinal ET metrics partitioned the landscape into dynamic biophysical regimes. Cross-registered against legacy persistence mapping based on seasonal totals, the foundation model showed strong directional agreement (86.1%, Cohen’s Kappa = 0.716) in identifying chronically constrained zones across 869 shared active pixels. Crucially, the framework identified 966 historically persistent pixels undergoing stability decay, of which 95.3% were statistically verified via paired t-tests to have collapsed into the field's baseline variance pool. Furthermore, counterfactual anomaly detection isolated zones of recent acute divergence, differentiating enduring edaphic constraints from sudden system disruptions. This approach demonstrates how foundation models can transition from purely predictive engines to diagnostic instruments, advancing operational precision agriculture.

Abstract: Real-time system monitoring without human intervention is an important issue nowadays. The challenge is to find the learning model that best suits each system. In hydropower systems, critical situations occur when the water reaches the spill level or the minimum exploitation level. The actual learning models use past data to detect such instances. Our approach is to build models on future data, which is more appropriate for learning from real data. Given that the current forecasting methods are well developed and have proven their performance (the RBF Regressor achieved an RMSE of 0.291 in the current work), we propose forecasting data stored within the next month and using it to build clustering and classification models. The results show that our proposed approach achieves higher classification accuracy (99.51%) and higher or comparable Precision, Recall, F-Measure, and MCC than those of other models trained on similar datasets.

Abstract: Australia is a country endowed with natural resources such as coal, lithium, rare earths amongst other high-level commodities, which attract global trade opportunities viable for boosting its economy. Amidst its natural resources, Australia has been viewed as a prosperous nation in view of its standing in the Commonwealth of Nations. Nonetheless, the country still faces numerous challenges ranging from floods, heatwaves, bushfires, cyclones, and drought amongst other forms of hazards. While such hazards reverse hardly-won strides of development, other inter-related aspects of vulnerability which limit attributes of social capital poses tremendous challenges which impact on pre, during and post disaster interventions. By using causal loop diagrams, this study takes a systemic approach towards exploring emerging system structures; interdependence and interconnectivity; and institutional pressures, to unravel factors influencing Australia’s vulnerability to hazards with the aim of facilitating concerted interventions for reducing vulnerability and ultimately, the risks impeding sustainable development.

Abstract: Pediatric acute-onset neuropsychiatric presentations occurring in the context of prior streptococcal exposure remain clinically important but diagnostically inconclusive, particularly at the interface between PANS and PANDAS. This observational cohort study examined whether serological, psychometric, and electroencephalographic findings converged within a clinically selected pediatric psychiatric sample. Children and adolescents presenting with acute-onset or abruptly worsened neuropsychiatric symptoms and a history suggestive of prior streptococcal exposure were recruited over a 12-month period through inpatient and outpatient child psychiatric services. Of 154 screened cases, 96 with analyzable baseline data were retained and stratified by ASO status. Symptom burden was quantified using the Pediatric Acute-onset Neuropsychiatric Syndrome 31-Item Symptom Rating Scale (PANS-31) and examined in relation to ASO titers, time since the last reported streptococcal infection, EEG findings, and selected developmental and clinical-history variables. Higher ASO values were strongly associated with greater PANS-31 symptom burden, whereas a shorter interval since the last reported streptococcal infection was associated with both higher ASO titers and higher symptom scores. PANS-31 showed good total-scale internal consistency and meaningful domain-level convergence with age-appropriate CSI-4 and ASI-4 domains. These findings do not support a disease-specific biomarker model, but suggest that higher antistreptococcal serology, more recent streptococcal exposure, and greater neuropsychiatric burden may cluster within a more clearly expressed clinical phenotype in a real-world psychiatric environment.

Abstract: ABS is widely used as engineering plastic, but extensive use generates a significant amount of waste which is difficult to recycle due to material's complex composition. Physical recycling of ABS using TNO Möbius dissolution technique has been used here to separate pure SAN polymer, from PBR, and other substances. Relationships between properties and composition of the original materials were investigated as a starting point for evaluation of the effects of recycling on the quality of recycled materials. Three ABS materials were used in the recycling process to produce pure SAN polymers. The recycled SANs were then melt-blended with fresh masterbatch. The final ABS ma-terials had the same composition which allowed to investigate whether SAN recycled from different sources causes differences in properties of the final ABS materials. All properties of ABS materials made with recycled SAN are similar regardless of the source of SAN. Substances were quantified in the original ABS materials and in SAN polymers obtained by the recycling process. The substances were largely removed from all materials except one. The main conclusions from this study are that SAN polymer obtained by physical recycling from different sources does not affect properties of the final ABS material and the TNO process successfully separates SAN from other substances.

Abstract: Background/Objectives: Aluminum-adjuvanted vaccines are widely used in pediatric immunization, yet particle size and distribution are critical but under-standardized quality attributes. Methods: This study aimed to establish and validate a laser diffraction particle size analysis for evaluating particle size consistency and stability of inactivated enterovirus 71 (EV71) vaccine and adsorbed acellular diphtheria–tetanus–pertussis (DTaP) vaccine. Results: EV71 vaccine stock solution (40–350 nm) and aluminum adjuvant (75–600 nm) exhibited distinct distributions, with the final product showing bimodal distribution (50–14,000 nm): main peak at 0.1–0.2 μm (~65%) and secondary peak at 0.3–3 μm (~14%). DTaP final product (2000–20,000 nm) showed significant aggregation with 79.6% at 3–8 μm and 15.7% at >8 μm. Four EV71 batches (A1–A4) showed uneven inter-batch consistency (D50: 100, 132, 103, 103 nm; CV 13.8%), while intra-batch CVs were acceptable (3.9% for EV71, 8.0% for DTaP). Long-term stability at 4°C revealed gradual aggregation: EV71 D50 increased from 100 nm to 134 nm over 60 days, with >0.2 μm aggregates increasing from 0.03% to 1.50%; DTaP showed severe tailing at day 60 (>50 μm particles: 2.8%). Accelerated studies showed 37°C caused slight enlargement, whereas −20°C induced marked aggregation (EV71 D50: ~37 μm, CV 16.1%; DTaP D50: ~45 μm, CV 13.8%). Conclusions: Laser particle size analysis is a robust, reliable tool for assessing particle size consistency and stability of aluminum-adjuvanted vaccines. It supports process control, batch release, and stability monitoring to improve vaccine quality and safety.

Abstract: This study investigates the structural and sorption characteristics of nanostructured polysaccharide biopolymers isolated from the tubers of dahlias (Dahlia spp.) and Jerusalem artichokes (Helianthus tuberosus). The plant raw materials were subjected to preparation and extraction to isolate pectin biopolymers, after which the resulting pectins were purified and dried to a stable state, ensuring their suitability for further physicochemical and sorption studies. The obtained pectin matrices were characterized using scanning electron microscopy (SEM) to analyze morphology and nanostructure, infrared (FTIR) and Raman spectroscopy to identify functional groups, as well as atomic absorption spectrometry to study sorption properties. The use of Raman spectroscopy further confirmed the presence of characteristic structural fragments of pectin and revealed changes in the vibrational spectra of functional groups upon interaction with metal ions. The ability of biopolymers to adsorb the heavy metal ions Cu²⁺ and Zn²⁺ from aqueous solutions was investigated. It was shown that as the concentration change (ΔC) increases, the sorption capacity increases; in most cases, the sorbent derived from dahlia tubers (DT) exhibits higher activity compared to Jerusalem artichoke (HT), which is associated with structural features and the availability of functional groups. Analysis of sorption isotherms showed that the adsorption of Cu²⁺ is well described by the Langmuir and Freundlich models, indicating a mixed sorption mechanism, whereas the Freundlich model is more appropriate for Zn²⁺, reflecting the heterogeneity of the surface and the presence of active sites with different interaction energies. The obtained data confirm the potential of nanostructured pectin biopolymers as environmentally safe sorbents for the removal of heavy metals from aqueous media and serve as a basis for the development of new sorption materials.

Abstract: Background/Objectives: The Child-Pugh system is widely used to grade cirrhosis severity but includes clinical components that may be variably documented. This study evaluated the association and diagnostic performance of the aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 (FIB-4) index, and albumin-bilirubin (ALBI) score for discriminating Child-Pugh classes in cirrhosis. Methods: We conducted a retrospective cross-sectional study using medical records from 302 adults with cirrhosis treated at Thai Binh General Hospital, Vietnam, from January to June 2025. Child-Pugh class was reconstructed from bilirubin, albumin, PT%, ascites, and hepatic encephalopathy. APRI, FIB-4, and ALBI were calculated from routine laboratory data. Group comparisons, correlation analysis, multivariable regression, receiver operating characteristic analysis with bootstrap 95% confidence intervals, optimal cut-offs, and reclassification metrics were assessed. Results: Among 302 patients, 48 (15.9%) were Child-Pugh A, 120 (39.7%) Child-Pugh B, and 134 (44.4%) Child-Pugh C. ALBI values differed consistently across Child-Pugh classes (-2.23 ± 0.37, -1.65 ± 0.45, and -0.80 ± 0.46; p < 0.001), whereas APRI and FIB-4 showed less distinct separation between classes. ALBI showed a strong correlation with the Child-Pugh score (r = 0.853, p < 0.001) and remained associated with Child-Pugh severity in multivariable linear and logistic regression models. Among the three indices, ALBI showed the highest discrimination for Child-Pugh B/C versus A in this cohort (AUC, 0.919; 95% CI, 0.884-0.950), with an estimated optimal cut-off of -1.753. Conclusions: In this retrospective cohort, ALBI showed closer agreement with Child-Pugh severity and higher discrimination for Child-Pugh B/C versus A than APRI and FIB-4. ALBI may be considered as a simple laboratory-based adjunct to support Child-Pugh stratification in routine cirrhosis assessment, but further prospective validation is required before broader clinical application.

Abstract: This paper presents a method for continuously optimizing the turn-on and turn-off angles of a switched reluctance generator (SRG) operating in single-pulse mode and connected to an asymmetric bridge converter. The optimal angles are defined as those that minimize total SRG loss while ensuring accurate tracking of the terminal voltage reference. The Pearson correlation coefficient between SRG loss and selected SRG variables was calculated, with the highest correlation found for the average value of all phase currents. Therefore, the average phase current was selected as the variable to be minimized in a perturb-and-observe (P&O) method used to determine the optimal turn-on angle at a given operating point. The turn-off angle was calculated to maintain the terminal voltage at its reference value. The method was validated using both a conventional SRG simulation model and an advanced model that accounts for mutual coupling, iron losses, and remanent magnetism, and was further verified experimentally on an 8/6 SRG rated at 1.1 kW under various load conditions, terminal voltages, and rotor speeds.

Abstract: Bispecific T-cell engager (TCE) development continues to attract substantial industrial investment alongside a translation record that remains uneven across target antigens and disease settings. Multiple independent reports across the field have observed that target antigen density on tumor cells does not predict cytotoxic potency or clinical response, while other reports describe within-target density-potency correlations of widely varying strength. These findings, when read in parallel, appear contradictory and have not been organized by any unifying analytical framework that has gained adoption in the field's standard practice. A mechanistically motivated joint binding-effector framework suggested that this apparent contradiction may reflect a single biological structure being read through analytical conventions that examine target antigen density and effector-side biology in isolation. To investigate this systematically, we assembled a verified dataset of bispecific TCE clinical-stage programs spanning eleven target antigens (CD19, CD20, BCMA, GPRC5D, CD33, CD123, CLL-1, FLT3, EpCAM, PSMA, and DLL3) and read it against the published primary-source record of within-target density-outcome reports. The systematic empirical pattern that emerges is consistent with a joint binding-effector structure in which neither variable alone is sufficient. In the limiting case, target cells lacking the antigen produce no cytotoxicity at any effector-to-target ratio. The published record reflects the two variables asymmetrically by design: target antigen density is reported across cell-line panels, primary samples, and clinical correlative cohorts, while effector-side variation is structurally absent from cell-line panels (which fix effector-to-target ratios at non-biological values with uniform donor T cells) and is observable only in primary-sample and clinical correlative analyses. Across approved drug programs at clinical exposure, target antigen density does not predict outcome (verified in multiple peer-reviewed primary samples and clinical correlative analyses including a registration-trial cohort of n=165); in those same settings, measures of effector-side biology -- effector-to-target ratio, T-cell counts, regulatory T-cell frequency, and exhaustion markers -- are associated with outcome, consistent with the elementary requirement that both antigen-bearing targets and adequate effector cells are needed for TCE activity. Within-program triangulation in a discontinued clinical-development program (CD33/AMG 330) demonstrates the same structural pattern in the failure direction. We propose the joint binding-effector account as a testable explanation that reconciles the systematic empirical record assembled here: it is logically coherent, internally consistent, and consistent with the field's documented findings. The systematic dataset and the verified primary-source documentation are deposited as supplementary material to support independent evaluation.

Abstract: Complex adaptive systems (CAS) have two defining characteristics. First, they are complex, i.e., composed of several interacting parts. Second, they are adaptive, i.e., their behavior can be changed in response to external stimuli and changes in the external environment. Due to this, managing such systems is quite challenging. Traditional approaches have involved defining policies that determine the behavior of any CAS under particular circumstances. However, such approaches are rigid and inflexible, since they are dependent on pre-specified policies. To that end, in this position paper, we describe an intent-driven approach to modeling and managing CAS. This would be a more flexible approach, not dependent on any specific policies, but which can be customized based on the context in which the CAS is functioning. We describe the various components of our approach, which include compositional reasoning to decompose the intent into sub-intents as per the context; mapping the sub-intents onto the execution model which will satisfy the intent; and feeding back the results of the execution to facilitate continual learning and continuous improvement in managing the CAS. In particular, one aspect that we highlight is the application of neurochaos learning, which uses chaos theory to facilitate rapid continual learning that would help improve the overall efficiency of our approach. For each component of our approach, we also present several research questions that need to be addressed before intent-driven management of CAS can become a reality.

Abstract: Background: Stress may have an impact on the course of inflammatory bowel disease (IBD), but evidence is still lacking regarding a potential association between perceived (subjective) stress and objectively measured stress, and whether patients’ levels of stress in relapse differ from those in remission. The aim of this study was to investigate patients’ level of stress in relapse and in remission. Methods: Twenty-three patients with active IBD participated in the study. Cortisol was assessed in saliva and in blood to obtain objective measurements. For subjective meas-urements, the patients completed the Short Health Scale (SHS) and Perceived Stress Scale (PSS) questionnaires. Physiological measurements were taken and questionnaires com-pleted at the beginning of relapse and when the patient was classified as being in re-mission. Relapses and remissions were determined by endoscopic examination and faecal calprotectin. Results: Cortisol levels did not differ between measurements in active disease and in remission. PSS showed no differences between the two measurements, but on both oc-casions medium-high levels of stress were shown. Inflammatory cytokines IL-6 and IL-8 showed significantly lowered values at remission. Conclusion: This study demonstrated moderate levels of perceived stress in patients with IBD, both during active disease and remission. However, no evidence of elevated ob-jective stress was found when levels of cortisol in saliva were measured. Further research is needed to establish the possible association between stress and IBD and how it affects patients.

Abstract: This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes.

Abstract: Self-check-in via digital technology is becoming increasingly prevalent to streamline workflows and improve primary care efficiency, including kiosks, eCheck-in via portals, mobile check-in apps, and pre-appointment questionnaires. This scoping review examines the value-creation potential of digital self-check-in tools by assessing the quality of intake data generated with these tools and their reuse. Following the Joanna Briggs Institute guidelines for conducting scoping reviews and the PRISMA-ScR reporting criteria, searches were conducted across the CINAHL, PubMed, and Google Scholar databases to identify English-language peer-reviewed studies published between 2021 and 2026. In total, 488 studies were identified; 361 were assessed based on titles and abstracts after duplicate removal, 65 were reviewed in full text, and 15 studies were included in the final review and graded using the Johns Hopkins Nursing Evidence-Based Practice (JHEBP) levels and quality ratings. Most of the evidence was level III with a B quality rating. Findings showed that the portal and pre-visit questionnaire approaches provided the most reliable support for data structuring, visit preparation, and communication between the patient and the clinician. In turn, improvements in workflow efficiency, reduced patient congestion, increased throughput, and minimized front-desk burden could be achieved primarily through studies focused on kiosks and registration processes. Across the study, the strongest evidence supports operational and informational value rather than return on investment (ROI). The main barriers to the effective implementation of the interventions included access inequity, workflow integration, staff training, and bad data quality. Overall, digital self-check-in tools create value in primary care when patient-generated intake data are timely, complete, structured, and reusable across downstream clinical and administrative workflows. However, stronger evidence is still needed regarding measurable economic return.

Abstract: Quantumgateestimationandtomographypipelinesroutinelycombineintrinsicallydefined likelihoods with priors or regularization terms specified in local Euclidean coordinates. This practice implicitly replaces the Haar reference measure on SU(2) with Lebesgue measure, specifying a different statistical model rather than a reparametrization of the intended one. Weshowthat omitting the associated chart-volume factor alters the optimization objective itself, modifying its gradient field and stationary-point structure. The mismatch persists arbitrarily close to the identity, so that flat-coordinate surrogate objectives can converge to points that are non-stationary for the corresponding Haar-consistent objective even in regimes where local Gaussian approximations are assumed valid. We prove a formal non-equivalence proposition and validate a leading-order Fisher-information correction analytically and numerically. Large-scale multi-start optimization experiments (N = 11,900 runs) demonstrate that the discrepancy is regime-dependent and most pronounced under moderate-to-strong regularization or limited data. The fix requires a single-line modification to any gradient-based optimizer. These results identify reference-measure selection as an explicit modeling decision with direct consequences for optimization and inference in gate-set tomography, randomized benchmarking, and Bayesian gate estimation on curved parameter manifolds.