Abstract: Laser polishing (LP) is widely used to improve the surface quality of additively manufactured (AM) metals; however, its behaviour within deep or narrow internal geometries remains insufficiently understood. Many high-performance AM components including biomedical implants, turbine cooling channels, and metal microfluidic devices contain confined internal features where heat-transfer conditions differ substantially from those at open surfaces. In this study, LPBF-fabricated 316L stainless steel specimens containing ~10 mm deep slots with widths ranging from 1 to 5 mm were laser polished to examine how internal geometry influences microstructural evolution and mechanical response. A clear depth-dependent microhardness gradient was observed along the slot wall, with hardness decreasing from approximately 270 HV in the lower region to about 210 HV toward the slot opening. The gradient was more pronounced in narrower slots. Microstructural characterization revealed finer grains near the slot base and progressively coarser grains toward the upper regions. These variations are consistent with differences in conductive coupling to the surrounding bulk substrate along the slot depth, which influence local cooling conditions during solidification. The results provide quantitative evidence that internal geometric boundary conditions can affect microstructure and hardness development during laser polishing, even when nominal processing parameters are held constant. This work highlights the importance of considering feature geometry in the post-processing of AM components containing confined internal structures and offers guidance for achieving more predictable local mechanical performance.

Abstract: The full-scale invasion of Ukraine in February 2022 has subjected the national health system to unprecedented structural, financial, and operational stress. This article examines how Ukraine’s health system has functioned under sustained armed conflict, focusing on service delivery, workforce dynamics, health financing, governance, mental health integration, and long-term recovery planning. Drawing on reports from the World Health Organization (WHO), the World Bank, United Nations monitoring mechanisms, and emerging peer-reviewed literature, the analysis explores both system vulnerabilities and adaptive responses. Despite widespread destruction of infrastructure, repeated attacks on health facilities, population displacement, and economic contraction, primary health care services have remained operational in most regions. Pre-war financing reforms—particularly the centralized purchasing model implemented through the National Health Service of Ukraine—contributed to continuity of provider payments and financial protection for patients. At the same time, the conflict has intensified workforce shortages, disrupted supply chains, and generated a substantial burden of mental health conditions and unmanaged noncommunicable diseases. The Ukrainian case illustrates that health system resilience is rooted in pre-existing institutional capacity, protected pooled financing, digital health integration, and coordinated governance under national leadership. Beyond immediate survival, the conflict presents opportunities for transformative reconstruction aligned with equity, community engagement, and universal health coverage goals. The findings offer critical policy lessons for health systems operating in conflict and protracted crisis settings, emphasizing that preparedness, primary health care strengthening, and governance integrity are central determinants of systemic endurance during war.

Abstract: Glucose is a principal energy substrate for the brain. Hypo- and hyperglycemic episodes are frequent in senescent people thereby contributing to functional and structural impairment of brain neurons, yielding cognitive deficits in this population. In this report we investigate whether long-term changes in extracellular concentration of glucose may affect viability and transmitter functions of septum-originated SN56 cholinergic neuronal cells through alterations in their acetyl-CoA availability. Cell phenotypes with low (NC) and cAMP/retinoic acid in-duced, high expression cholinergic phenotype (DC) were investigated. Hypoglycemia brought about similar about 20-30% decreases of in pyruvate dehydrogenase complex (PDHC) and ATP-citrate lyase (ACLY) and 65% decline of lactate dehydrogenase (LDH) activities in NC and DC, respectively. Choline acetyltransferase (ChAT) and LDH activities in DC were about 3 to 8 and 1.7 to 2.4 times higher than in NC, over the entire range of glucose concen-trations, respectively. In effect DC appeared to be more resistant than NC to hypoglycemia, as evidenced by lower values of [IC50] of glucose against cell count and intracellular LDH, re-spectively. On the other hand, some of functional properties of DC such as cholinergic phe-notype (ChAT) and plasma membrane function (trypan blue exclusion, TB+) were found to be more prone to hypoglycemia than in NC, as demonstrated by higher respective [IC50] for glu-cose in DC. Acetyl-CoA levels in DC were 40% lower than those in NC, and both decreased parallelly with deepening hypoglycemia by about 25%. Cytotoxic effects of amyloid-β25-35 (Aβ) and sodium nitroprusside (NO generator-SNP) in those conditions were tested. In 25 mM glucose medium these toxic compounds in DC exerted greater detrimental effects than in NC. On the contrary, in 1 mM glucose more evident cytotoxicity of SNP and Aβ was ob-served in NC. These data may indicate that higher rate of glycolysis in differentiated cholin-ergic septal neurons may establish a protective mechanism against hypoglycemia.

Abstract: Business development plays a critical strategic role in organizational growth, yet it lacks a structured performance measurement framework capable of capturing its multi-phase and multidimensional characteristics. This study develops a hierarchical performance evaluation framework that links strategic intent with measurable execution across six interdependent phases of the business development process. The framework integrates eighteen key performance indicators and applies the Analytic Hierarchy Process to derive relative phase weights and maintain internal coherence within the evaluation architecture. Scenario-based simulations examine the robustness of the framework under alternative strategic orientations, including growth-focused, efficiency-driven, and balanced configurations. Results indicate that the feasibility evaluation and risk validation phases exert the strongest influence on aggregate performance outcomes, while sensitivity analysis confirms the stability of the weighting structure across strategic contexts. An illustrative organizational application demonstrates how the framework translates strategic priorities into structured performance assessment. By extending hierarchical multi-criteria evaluation into the domain of business development, this study advances performance measurement research through the development of an architecture-sensitive evaluation logic that supports transparency, comparability, and strategic alignment in complex cross-functional processes.

Abstract: Due to the highly anxiety-inducing nature of music aptitude tests or exams, there is a need for a specialized tool to measure adolescents' beliefs about their musical poten-tial. This study aims to develop and validate the Music Aptitude Self-Efficacy Scale (MASES) for adolescent music teacher candidates preparing for aptitude tests. The study group consisted of 383 students (ages 16–18) preparing for the Music Education Department entrance exams at the Education Faculties in Türkiye. Split-sample meth-od was used for Exploratory Factor Analysis (n=199) and Confirmatory Factor Analy-sis (n=184). The EFA results have revealed a three-factor structure: Cognitive-Auditory Competence, Psychomotor- Performance Competence, and Affective Regulation Competence. CFA results, this three-factor model has excellent fit indices (χ2/df = 1.24, RMSEA =. 036, CFI =. has been validated with 976). The scale has high internal con-sistency and composite reliability (Cronbach α =. 87, McDonald's ω =. 92). The criteri-on-related validity analysis revealed significant positive correlations (r = .586) between the MASES and the Questionnaire for Measuring Self-Efficacy in Youths. The findings indicate that MASES is a valid and reliable tool that can be used by music educators to identify student’s self-efficacy levels and to design guidance programs for aptitude tests or exams.

Abstract: The reuse of treated wastewater for agricultural irrigation is increasingly recognized as a strategic response to the growing challenges posed by climate change and freshwater scarcity. The paper outlines the development of EU regulations on using treated wastewater for irrigation, focusing on Italy. It highlights Regulation (EU) 2020/741, which sets minimum standards and water quality classes for agricultural reuse, and discusses its integration into national law. The aim of the paper is to present a case study of the wastewater treatment plant operated by GIDA S.p.A. in the Municipality of Prato, Tuscany. A quali-quantitative survey was conducted with a sample of local agri-food producers located in proximity to the plant, aimed at assessing their irrigation needs, current water sources, and attitudes toward the use of reclaimed water. Results indicate a general willingness to adopt treated wastewater for irrigation. The case of Prato is further contextualized within two ongoing municipal frameworks: the development of a local food policy strategy and the “Prato Circular City” program, which positions cir-cular economy principles at the core of urban planning. Through the integration of policy analysis and empirical data, this study provides valuable insights into peri-urban agricultural environments in Central Italy.

Abstract: The global seafood industry faces persistent challenges related to product quality, safety, and authenticity, driven by complex supply chains, increasing demand, and the perishable nature of aquatic products. Traditional analytical methods often fall short in providing rapid, comprehensive, and non-destructive insights into the intricate biochemical changes occurring in seafood. 1H Nuclear Magnetic Resonance (1H NMR) spectroscopy has emerged as a powerful and versatile tool for metabolomics, offering a holistic view of the low-molecular-mass compounds (metabolites) present in biological samples. The present study applied 1H NMR for chemical fingerprint identification in mullets (Mugil liza) from Brazil. Dorsal muscle samples were taken from samples during summer, autumn, and winter. The procedure involved freeze-drying the muscle tissue, thereafter extracting polar metabolites using designated solvents (methanol, water, and chloroform), and analyzing them using a 600 MHz spectrometer. As results, 23 metabolites related to degradation biomarkers, essential metabolites, energy expenditure, and muscle structure were identified. The statistical analysis demonstrated a distinct separation between the geographical origins (RJ vs. SC), mostly influenced by variations in the concentrations of lactate, histidine, threonine, phenylalanine, and ornithine. Factors like fish size and seasonal variations did not markedly affect the overall metabolic profile, so underscoring the reliability of these chemicals as stable origin indicators. The Principal Components Analysis identified two distinct groups of metabolites, establishing a profile for each geographical origin. The developed protocol can be applied in the processes for geographical identification. Thus, the 1H NMR tool was efficient in determining metabolites that can be considered biomarkers in analyses for seafood traceability.

Abstract: Background Cataract remains the leading cause of blindness worldwide, particularly in low- and middle-income countries. Cataract surgery is an effective intervention, but poor or borderline outcomes reduce its impact in eliminating avoidable blindness. Assessing cataract surgical outcomes is essential for improving surgical quality. This study aimed to identify reasons for poor or borderline cataract surgical outcomes at NkhomaHospital, Malawi, focusing on ocular comorbidities, surgical complications, refractive errors, and demographic factors, including gender.Methods This was a retrospective analysis of 828 patients with a post-operative visual acuity of 6/18 or worse in at least one eye following cataract surgery. Data were extracted from theatre records, including demographics, referral source, preoperative examination, intraoperative findings, and post-operative assessment. Reasons for poor or borderline outcomes were classified based on ocular comorbidities, refractive error, surgical complications, and screening errors.Results Among patients with poor or borderline surgical outcomes, 52.2% had ocular comorbidities (e.g., glaucoma, diabetic retinopathy, corneal scarring), which adversely affected vision post-surgery. Uncorrected refractive error accounted for 25.8% of cases, indicating gaps in post-operative optical correction services. In 13.5% of cases, records lacked sufficient data to determine a cause.A significant gender disparity was observed (p = 0.027), with women more likely than men to present as blind at the time of surgery. These findings highlight potential barriers to timely cataract surgery for women, including financial constraints, caregiving responsibilities, and delayed health-seeking behavior. Conclusion The causes of poor cataract surgery outcomes at Nkhoma Hospital are multifactorial, mainly driven by pre-existing ocular conditions, inadequate screening, and post-operative refractive care. Strengthening pre-operative screening, post-operative optical correction services, and gender-targeted outreach programs can improve surgical success. Targeted capacity building for cataract surgeons, outreach teams, and post-op follow-up staff is essential to ensuring equitable access to cataract surgery and reducing avoidable blindness in Malawi and other countries.

Abstract: Background: many wildfire models simulate fire spread by resolving the front-normal velocity (spread rate) as a function of the local environmental conditions and the orientation of the fire front. We study one such model, here called the wind-projected Rothermel model, which extends the Rothermel model to all front orientations by using the front-normal component of the midflame wind speed \( \vec{U} \): \( R(\vec{n}) = R_0 (1 + A \max(0, \vec{n} \cdot \vec{U})^B) \) Methods: we apply the mathematical methods recently developed by the authors to solve front propagation in spatially constant conditions. Results: as soon as wind is moderately high, the model undergoes heading fire collapse, i.e. the downwind-facing front collapses to a pointed shape, which then advances at a lower speed than predicted by the Rothermel model. Formulas are derived to compute the characteristic length and time scales of the collapse. The effect is more pronounced for finer fuels that have a higher wind exponent. Conclusions: the formulas provided here can facilitate model validation. Heading fire collapse is frequent in this model; this is a salient point for model validation, as it makes the model behavior drastically different from other Rothermel extensions, like those based on elliptical Huygens wavelets. This also threatens the validity of some numerical implementations.

Abstract: Machine Learning (ML) is fundamentally reshaping education, offering tools to personalize instruction, automate assessment, and predict student outcomes. This paper provides a comprehensive overview of ML's role in education, tracing its evolution from early computer-assisted instruction to today's generative artificial intelligence (AI). We explore key applications, including intelligent tutoring systems, early warning systems for at-risk students, and automated essay scoring, highlighting their potential to address the long-standing challenge of individualized learning at scale. However, this technological integration is fraught with significant challenges. Ethical concerns regarding algorithmic bias, data privacy, and the "black box" nature of complex models threaten to exacerbate existing educational inequities. The recent proliferation of generative AI, exemplified by tools like ChatGPT, has further disrupted traditional paradigms of assessment and academic integrity, prompting urgent questions about the nature of learning itself. By synthesizing current research, this paper argues that while ML holds immense transformative promise, its successful and equitable implementation depends not on technological prowess alone, but on a concerted, ethically-grounded effort involving educators, researchers, and policymakers to ensure these tools augment human expertise and serve all learners.

Abstract: The optimization of membrane permeability is a decisive strategy for mitigating late-stage failures in peptide drug development. By leveraging linker chemical diver-sity, stapled peptides utilize linker engineering to precisely modulate key physico-chemical parameters—such as lipophilicity and conformational constraints—to over-come the desolvation energy penalty. This review systematically evaluates link-er-based strategies for enhancing the permeability of stapled peptides, categorized into two primary dimensions: (1) High-throughput screening (HTS) compatibility, focusing on the integration of functionalized linkers into mRNA display, phage display, and DNA-encoded libraries (DELs) to identify lead scaffolds with inherent permeability potential during early discovery ; and (2) Post-screening structural refinement, cover-ing rational design strategies including intramolecular hydrogen bond (IMHB) shield-ing, "chameleonic" adaptations, and stimuli-responsive reversible stapling . Further-more, we analyze the paradigm shift in assessment methodologies from qualitative imaging to quantitative cytosolic delivery assays, which have deepened our under-standing of mechanisms such as the charge/lipophilicity threshold balance and meta-bolic-driven trapping. Overall, linker engineering provides a robust technical roadmap for developing the next generation of cell-permeable stapled peptide therapeutics.

Abstract:

We introduce the NeuroCore framework, a formal mathematical treatment of modular neural architectures in which a minimal executive Core—possessing no higher cognitive capabilities—autonomously orchestrates a heterogeneous collection of specialist modules through learned continuous-representation interfaces. The Core’s behavior is governed by two neuromodulation-inspired subsystems: a Dopamine System implementing distributional reinforcement learning with prediction-error intrinsic motivation and a stagnation penalty, and a Serotonin System formulated as a meta-reinforcement-learning controller that learns to optimize long-horizon constraint satisfaction. We make four theoretical contributions. First, we formalize the stagnation-modification tradeoff—proving that without explicit anti-stagnation pressure, optimal policies in self-modifying systems converge to modification-avoidance, and deriving the conditions under which the stagnation penalty restores non-trivial self-modification behavior (Theorem 1). Second, we prove a general non-convergence result for coupled self-modifying multi-objective systems, showing that the joint optimization does not admit guaranteed convergence to fixed points or bounded attractors in the parameter space (Theorem 2). Third, we establish partial stability guarantees: bounded representational drift via homeostatic Lyapunov functions (Theorem 3), local convergence under frozen modules via two-timescale stochastic approximation (Proposition 1), and modification frequency bounds (Proposition 2). Fourth, we derive information-theoretic costs for module manipulation operations that serve as principled proxies for true disruption. We propose seven falsifiable empirical predictions and discuss implications for the design of autonomous self-organizing AI systems.

Abstract: Under high C-rate and wide-temperature conditions, independently estimated SOC and SOH often diverge due to decoupled model dynamics, resulting in inaccurate power boundary calculations. This affects power limiting, thermal safety, and fast-charging strategies. To solve this, a unified online estimation framework is proposed for SOC, SOH, and power capacity under voltage and thermal constraints. It integrates a state-space model based on an equivalent circuit, combining SOC, polarization voltage, internal resistance, and capacity degradation, with temperature-dependent parameter evolution to capture coupling with aging. A dual extended Kalman filter enables collaborative SOC–SOH estimation, while lightweight machine learning modules correct internal resistance and polarization dynamics to reduce mismatch under extreme conditions. Physical constraint projections embed voltage, temperature, and power limits into the estimation loop, mitigating noise amplification and drift. Based on consistent estimates, the SOP boundary is computed online to support control decisions. Validation across six temperatures (−20 °C to 55 °C) and five C-rates (0.2C to 6C), using bench, HIL, and pack-level tests over 120+ hours, shows SOC RMSE <1.6%, SOH error <2.5%, and SOP hit rate >95% within 10 seconds. Under noise and parameter disturbances, error growth is reduced by ~25% versus baselines. These results confirm improved SOC–SOH consistency and boundary tracking, with computational cost suitable for embedded deployment.

Abstract: This paper reports on an online survey of 2,000 English adults, designed to inform the debate about the potential for wider adoption of e-micromobility modes, such as e-bikes, e-cargo bikes and e-scooters. It shows that, by 2023, take-up was already greater than for electric cars, with 11% of households owning at least one of those vehicles and 9% of adults using one at least once a month. On average, users were more likely to be male, young, well-educated urban dwellers, but findings also suggested relatively high take-up by people with children, greater appeal to women than conventional cycling, and the potential to appeal to a wider range of age groups over time. Use of e-micromobility was associated with more varied mobility strategies, and lower levels of frequent car use. Over 50% of adults were interested in trying out vehicles, and evidence from other UK trials and existing users suggests that being able to trial vehicles may be key for purchase decisions. On balance, non-users were broadly positive (or neutral) towards these modes, though with particular concerns arising around the safety of e-scooters and their relationship with pedestrians. Cost, fear of theft, difficulties with storage and parking, unsafe road environments and lack of confidence cycling all emerged as key barriers. Users of e-micromobility were less likely to be sedentary and more likely to be meeting physical activity targets than non-users, highlighting important synergies with other active travel modes (i.e. walking and cycling), but any measures to increase uptake need to find ways to ensure that different active travel modes can safely coexist.

Abstract: This paper proposes an axiomatization of the absolute infinite within a non-recursively enumerable class theory, called MK^meta, that maximally and consistently extends the formal MK: Morse-Kelley with global choice (GC). Class ordinals and class cardinals avoid the Burali-Forti paradox and GC is assumed to warrant comparability of class cardinals. A Hamkinsian multiverse M_h is defined as the collection of all the formal models v of any syntactically consistent, formal extension of MK. MK^meta is then rigorously defined by ranging over M_h and has V^meta as its unique model. At last, the absolute infinite Ω^meta = Ord^meta is derived from V^meta. Informal, formal, and formal-based theories, having increasingly many axioms, are strictly weaker than the meta-formal theory MK^meta, which has absolutely infinitely many axioms. Moreover, truth relativism is countered by MK^meta, which accepts those axioms that maximize V^meta. Consequently, the definition of M_h can be used as a rebuttal of both height and width potentialism, when combined with the argument that only the meta-formal level can capture the entire mathematical reality in a single rigid theory.

Abstract: Background: Metabolic dysfunction associated steatotic liver disease (MASLD) is a slow evolutionary condition from inflammation to cirrhosis. Manual therapy applied to the liver could optimize its visceral function and relieve inflammation. Given that MASLD prevalence increases with aging and reduced mechanical and metabolic stimulation, understanding non-pharmacological interventions becomes increasingly relevant in older populations. The main objective was to assess the usefulness of visceral manipulation therapy (VMT) on liver steatosis and insulin resistance measured by hepatic steatosis index (HSI) and the homeostasis model assessment (HOMA). Materials and Methos: An open label, randomized clinical trial of patients with MASLD. Patients with steatosis determined by HSI (> 36 indicate steatosis) were randomly assigned in a 1:1 ratio to receive either manual therapy or nothing. Participants were recruited between April and September 2024. VMT was performed by the same osteopathic therapist following a precise protocol for four weeks. The primary endpoint was changes from basal score to after proceeding in the HSI and HOMA. The secondary endpoints were changes in other non-invasive scores to evaluate steatosis, steatohepatitis and fibrosis. All patients received standard care according to their condition. Results: Forty participants, 20 each group, were finally included. Patients undergoing manual therapy experienced a significant mean reduction in the HOMA (7.22 vs. 5.5 p=0.018) and HSI (47.40 vs. 45.55 p=0.036) value after intervention. These findings did not appear in the control group: HOMA (4.17 vs. 4.7 p=NS), and HSI (42.6 vs. 41.9 p=NS). The secondary endpoints there were not changes of the scores to assess steatohepatitis or fibrosis neither experimental nor control group. Conclusions: VMT could be an adjuvant treatment in early stages of hepatic steatosis due to metabolic conditions improving insulin resistance and inflammation.

Abstract: By decoupling services and enabling elastic deployment, microservice architecture improves system scalability and evolutionary capability. At the same time, it substantially increases operational complexity. Failures often exhibit cross service propagation and a mismatch between observed symptoms and underlying root causes. To address the heterogeneity and fragmentation of multi source observability data such as logs, metrics, and distributed traces, this study proposes a unified modeling and intelligent root cause localization method for microservice systems. The approach treats each service as a basic modeling unit and maps heterogeneous observations into a shared representation space. Service dependency structure is explicitly incorporated to characterize system state at a global level. Through structure aware modeling on the dependency graph, anomaly information is propagated and constrained along real invocation relations. This design enables more accurate separation of local disturbances from structural anomalies. In addition, a consistency based measure derived from state deviation is constructed to score service anomalies. Dependency relations are then used for attribution and ranking, which unifies root cause localization and impact analysis within a single framework. Comparative results show that the proposed method achieves more stable and consistent advantages across multiple evaluation metrics. It captures anomaly propagation patterns in microservice systems more effectively and provides a unified and structure aware solution for intelligent diagnosis of complex distributed systems.

Abstract: Conservation of architectural heritage structures (AHS) requires compatible built her-itage materials with aesthetic, physical, chemical, and mechanical properties similar to those of the original materials. In recent years, however, urbanization, land reclamation, depletion of stone quarries, anti-mining and anti-quarrying legislation have limited access to original heritage materials. In the absence of the original heritage materials, ce-ment-based alternatives have been developed and widely applied for conservation. Major drawbacks of concrete- and cement-based materials include their large carbon footprint and long-term damage to the original rock or substrate, due to inadvertent promotion of salt efflorescence. This study systematically reviewed geopolymer-based materials as a sustainable, greener alternative to concrete- and cement-based materials for tuff- and coral rock-built heritage structures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were implemented for the literature review, using Scopus, Web of Science (WoS), and Google Scholar (supplementary) as databases, between 2013 and 2024. Inaccessible items, non-English, reviews, conference proceedings, book chapters, errata, and papers unrelated to geopolymers, tuff, and coral rock were excluded, resulting in a total of 103 articles. These works were classified into geopolymers (34 arti-cles), tuff-built heritage structures (60 articles), and coral rock-built heritage structures (9 articles). This review included 103 items in the qualitative analysis; however, only 34 arti-cles contained meaningful data for content analysis. These 34 articles were categorized in terms of the (i) main precursors; that is, metakaolin, fly ash, slag, and pyroclastic materi-als (i.e., pumice, volcanic ash, and volcanic soil), ceramic, others (i.e., tuff waste, silica fume, and mine wastes), (ii) formulations (i.e., precursors, activators, admixtures, and ag-gregates), and (iii) compressive strength. Furthermore, critical factors for compatibility were reviewed and classified into aesthetics (e.g., color, presence of efflorescence, and tex-ture) and physical, chemical, and mechanical properties. This review also explored recent applications of geopolymers in heritage structures, indicating that geopolymers are typi-cally used as repair mortar and consolidants. Finally, a bibliometric analysis was con-ducted to evaluate research trends on geopolymers, including a critical assessment of their aesthetic compatibility with heritage structures in the Philippines built with volcanic tuff and coral rock.

Abstract: This study explores the effects of sputtering pressure and power on FeCoNi high‑entropy alloy films prepared by DC magnetron sputtering, focusing on microstructure, surface morphology, and static/high‑frequency magnetic properties. In situ Lorentz TEM (LZ‑TEM) was used to directly observe magnetic domain evolution. Results show that low sputtering pressure (1 mTorr) promotes strong FCC (111) crystallization, smooth and dense surfaces. Increasing pressure leads to amorphization, higher roughness, and degraded magnetic performance. Under optimized pressure, 100 W sputtering power yields the best crystallinity, smoothest surface, and optimal soft magnetic properties, including high remanence ratio, low coercivity, and clear ferromagnetic resonance in the 2–7.5 GHz range. The optimal parameters are confirmed as 1 mTorr and 100 W, producing uniform nanocrystalline FeCoNi films. In situ LZ‑TEM reveals river‑like domain walls, vortex–antivortex structures, and uniform magnetic moment precession, indicating weak domain pinning and excellent high‑frequency magnetization consistency. This study provides experimental and theoretical support for the controllable fabrication of high‑performance FeCoNi soft magnetic films for high‑frequency devices.

Abstract: With the continuous increase in the number of retired lithium-ion batteries, accurately and quickly estimating their MRC has become a key challenge for the rapid sorting and secondary utilization of retired lithium-ion batteries. Conventional detection methods often suffer from low efficiency, prolonged detection cycles, and limited scalability for large-scale applications. To address these issues, this paper presents a fast MRC estimation method for retired lithium-ion batteries using a hybrid Convolutional Neural Network (CNN)-Conv Block Attention Module (CBAM)-Long Short-Term Memory (LSTM) architecture (CNN-CBAM-LSTM). The proposed approach integrates both factory-scale test data and laboratory experimental data to extract key voltage and capacity features from the initial 30-minute charging phase. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies in the time-series data, and the CBAM enhances feature representation by emphasizing critical characteristics. Experimental results demonstrate that the proposed method achieves MRC estimation within 30 minutes, significantly outperforming traditional approaches in terms of accuracy. The R² value increased to 99.42%, while the MAPE decreased to 1.55%. These results highlight the superior performance of the proposed method, which not only holds strong potential for rapid battery sorting and cascaded utilization but also exhibits broad applicability in large-scale battery health monitoring systems.