Abstract:

The Possibilistic Cramér–Rao Bound (PCRB) of the Theory of Epistemic Abductive Geometry (TEAG) floors the rate at which evidence may delete admissible microstates: \(H_π^{+} \geq H_π^{-} + \tfrac{n}{2}\log(1-I_k)\), with \(n\) the state dimension and \(I_k\) the Choquet information content of the observation. This paper corrects and strengthens the bound by exploiting a structural fact the earlier formulation ignored: possibilistic entropy lives on state space, but evidence deforms the impossibility field through the state-to-measurement map, which is not injective — many states produce the same evidence. For a measurement of rank m ≤ n, the surprisal field is the pullback of an \(m\)-dimensional field and is constant on the (n-m)-dimensional fibers of the measurement map. Three results follow. (1) Fiber conservation: a single observation leaves the fiber-direction geometry of every admissible \(\alpha\)-cut exactly invariant; the entire entropy change is the admissibility-integral of the log base-mass retained, an identity we prove by exact Fubini factorization. Ignorance is conserved along every direction the evidence cannot compare — the Principle of Comparative Information as a conservation law. (2) The rank-aware PCRB: under an explicit base-marginal coupling condition that formalizes the innovation–state isotropy assumption in its correct (\(m\)-dimensional) home, \(H_π^{+} \geq H_π^{-} + \tfrac{m}{2}\log(1-I_k)\) — a strictly stronger floor than the state-dimension version whenever \(m < n\), recovering it at full rank. (3) Falsifiability–observability: over \(\ell\) observations with dynamics, a direction of state space is falsifiable if and only if it lies in the accumulated pulled-back row space; the state is totally falsifiable if and only if the system is observable; and evidence-versus-evidence falsification — the detection of inconsistency, bias, and model stress through joint total falsification — is possible if and only if the stacked system is overdetermined, \(\ell m > n\) after accounting for rank. Consequences for the PCRB-admissible basin, for the ESPF reference implementation, and a falsifiable prediction about previously reported over-pruning in rank-deficient tracking are derived. Tightness of the corrected floor remains open and is inherited, not resolved, by the rank refinement.

Abstract: (1) Introduction: Stroke remains a leading cause of long-term disability, with 80–90% of survivors experiencing gait disturbances. Functional electrical stimulation (FES) is widely used to improve motor recovery and gait; however, its effectiveness remains uncertain because outcomes vary across studies. (2) Objectives: To evaluate the effectiveness of FES, alone or combined with rehabilitation strategies, for gait recovery in individuals with chronic stroke. (3) Review Summary: A systematic search of the PubMed/MEDLINE, ClinicalTrials.gov, Cochrane Central Register of Controlled Trials (Cochrane CENTRAL), and PEDro (Physiotherapy Evidence Database) identified randomized controlled trials published between 2016 and 2026. Adults with chronic stroke receiving lower-limb FES, alone or combined with conventional rehabilitation, were included. Outcomes included gait performance, balance, motor function, and activities of daily living. Methodological quality was assessed using the Cochrane Risk of Bias 2 tool. Five studies involving 109 participants met the inclusion criteria. FES improved gait speed, balance, lower-limb motor function, and functional independence, particularly when combined with task-specific rehabilitation. Improvements in coordination, neuromuscular activation, and corticospinal excitability also supported its role in motor recovery. (4) Conclusions: FES is a relevant adjunctive intervention for post-stroke rehabilitation. Although methodological heterogeneity remains, current evidence supports its potential to improve gait and functional outcomes.

Abstract: Corn stunt is one of the most important diseases affecting maize (Zea mays L.) production in tropical regions of the Americas. The disease is caused by a complex of pathogens transmitted by the corn leafhopper (Dalbulus maidis), and its predominantly quantitative inheritance complicates the identification of tolerant genotypes under field conditions. In this context, we aimed to perform a comprehensive phenotypic stratification of corn stunt tolerance in a tropical public maize diversity panel and to identify contrasting inbred lines for breeding and genetic studies. A total of 360 inbred lines were evaluated under natural infection using three complementary disease-response traits: survivor plant health score (SPHS), proportion of survivor plants (PSP), and whole-plant health score (WPHS). Multi-trait mixed-model analyses revealed significant genotypic variation, moderate to high broad-sense heritability, and significant genotype × environment interactions for all evaluated traits. A multi-trait index (MSI), calculated from standardized best linear unbiased predictions (BLUPs), successfully integrated the three phenotypic components and enabled robust stratification of the diversity panel, identifying 60 highly tolerant and 60 highly susceptible inbred lines. Further, a genomic principal component analysis demonstrated that these phenotypic extremes were distributed across both tropical and subtropical germplasm, indicating that tolerance is not restricted to a single genetic background. The proposed phenotypic framework provides a robust and reproducible strategy for characterizing quantitative disease tolerance, identifying valuable parental germplasm, and establishing well-defined phenotypic extremes for future investigations of the genetic architecture of corn stunt tolerance.

Abstract: The COVID-19 pandemic and subsequent economic crises uncovered deep vulnerabilities in social, economic, and institutional systems across the world. In this context, the corporate social responsibility (CSR) and sustainability strategies emerged as strategic tools for resilience and collaboration overcoming the crisis. This study examines the role of CSR and sustainability-oriented approaches in strengthening partnerships between for-profit and non-profit organizations in Slovenia during periods of crisis. Focusing on the various cross-sector collaborations formed or adapted during crises in the context of COVID and other events, the research explores how organizations mobilized resources, shared knowledge, and co-created social value to respond to urgent community needs. Using a qualitative method based on interviews the study identifies the various CSR and Sustainability strategies that companies and NPO/NGOs in Slovenia have adopted to combat the impact of pandemic and subsequent crises in society. Particular attention is given to how sustainability-driven innovative strategies enable longer-term, trust-based partnerships for achieving better organizational outcomes and provide recommendations for companies and NPO/NGOs moving forward. Findings indicate that organizations with embedded CSR and sustainability practices were better positioned to pivot from transactional relationships to strategic alliances with non-profits. These partnerships enhanced crisis response capacity and contributed to economic and social resilience to various crisis situations. Moreover, the crises acted as a catalyst for redefining value creation, shifting the focus from compliance management toward shared impact and collaboration.

Abstract: The Heritage-Aligned Reconstruction Framework (HARF) was developed through the Western Buddha of Bamiyan to structure AI-mediated heritage reconstruction through evidentially constrained prompt design, prompt-sufficiency assessment and expert-in-the-loop evaluation. Its transferability beyond a figural-sculpture case has not been empirically examined. This article tests that question through a bounded cross-site evaluation of the Temple of Bel at Palmyra, substantially destroyed in 2015. The case is not treated as a second full reconstruction study, but as a test of whether HARF’s schema and evaluative logic can be instantiated for a monumental architectural site governed by different material, proportional and iconographic conventions. The Dynamic Prompt Blueprint was adapted by replacing Bamiyan-specific descriptors with Palmyra-specific architectural evidence, including limestone construction, fluted Corinthian columns, inscriptions, cella dimensions, pseudoperipteral colonnade, thalamoi and zodiac ceiling reliefs. A fourteen-item Palmyra block within the Phase I expert questionnaire was completed by 32 respondents. The findings show that HARF’s schema transfers without redesign, while dominant failure points shift towards architectural and decorative details, especially relief, surface, inscription and roofline form. HARF is therefore best understood as a diagnostic evaluation framework, not as a reconstruction engine.

Abstract: We examine energetic electron distributions in the region of the diffuse aurora using a two-dimensional bi-Maxwellian model, applied to electron flux data at a single geomagnetic equator (L = 6.5). Electron flux is analyzed as a function of energy and pitch angle and transformed into velocity space to reconstruct the distribution function. An unweighted log-space least-squares fit of the bi-Maxwellian model to the reconstructed distribution yields a reduced residual measure of χ2 v = 1.000053, a mean absolute residual of |∆log10 f| = 0.0469 dex, an anisotropy factor, AT = 0.9886±0.0016, and no statistically significant bulk drift. These results show that, at this location, the bi-Maxwellian model reproduces the observed velocity-space structure with good quantitative accuracy and reveals a quasi-isotropic, near-equilibrium electron population.

Abstract: There have been considerable amounts of waste fibres produced as a result of the rapid expansion of the textile industry, which has led to major concerns regarding the environment and brought to light the necessity of developing recycling systems that are sustainable. To provide lightweight, thermally efficient, and environmentally friendly construction materials, this work valorizes Linters textile waste fibres (LTWF) as reinforcement in cement-based mortars. Thermal conductivity, diffusivity, effusivity, and volumetric heat capacity were determined using the flash method coupled with an inverse identification approach based on a genetic algorithm. The addition of LTWF greatly improved the thermal insulating properties of the mortars. At 7 wt.% LTWF, thermal diffusivity decreased by about 60% and thermal conductivity declined from 0.592 to 0.17 W·m⁻¹·K⁻¹, representing a 71% reduction. The composites also showed increased porosity and reduced density, demonstrating their lightweight insulating properties and ability to minimize building energy use. Mechanical characterization identified 3 wt.% LTWF as the optimal fibre content, producing gains of around 97% in flexural strength and 53% in compressive strength when compared with the reference mortar. From an environmental perspective, The application of LTWF significantly reduced the carbon footprint of the composites, resulting in a maximum 31.7% reduction in CO₂ emissions through partial substitution of cementitious materials and lower clinker consumption. Overall, the developed LTWF-reinforced mortars showed an excellent mechanical performance, outstanding thermal insulation, lightweight behavior, and enhanced environmental sustainability, confirming the strong potential of recycled LTWF as a cost-effective reinforcement for next-generation sustainable composites and circular economy applications.

Abstract: Polyendocrine metabolic ovarian syndrome (PMOS), previously referred to as polycystic ovary syndrome, is conventionally diagnosed through reproductive manifestations, although its metabolic evolution before diagnosis remains poorly defined. Here we conducted an observational study of de-identified electronic health records from a large federated US network to characterize the prediagnostic evolution of PMOS through four complementary analyses. First, across a reference landscape of 1,929 clinical conditions spanning 15,087,821 people, PMOS combined high body weight at diagnosis of 94.3 kg with marked five-year prediagnostic weight gain of 10.0 kg, corresponding to 15.6%, positioning it alongside insulin resistance, metabolic syndrome and obesity rather than reproductive disorders. This condition-wide pattern placed PMOS within the metabolic disease landscape and raised the question of when the anthropometric divergence begins. Second, among 104,003 women receiving their first recorded PMOS diagnosis at a mean age of 30.9 years (s.d. 9.5) and matched controls, BMI was already higher at the earliest 15-year lookback during adolescence, averaging 23 kg m⁻² versus 20 kg m⁻², respectively (P < 0.001). During the final five years before diagnosis, mean weight increased from 85.9 kg to 94.0 kg, an 8.1 kg gain corresponding to 9.4%, and was 17.9 kg higher than in matched controls at diagnosis (94.0 kg vs 76.1 kg, P < 0.001). This established that the PMOS-associated weight gap was present during adolescence rather than emerging only near reproductive-age diagnosis. Third, among 809,719 women with BMI recorded between 5 and 20 years of age, 19,681 subsequently received a PMOS diagnosis. Ten-year cumulative PMOS incidence increased progressively from 3.3% with healthy BMI to 17.9% with obesity class III, corresponding to an RR of 5.4 (P < 0.001). Black women constituted 11.1% of the healthy-BMI group and 26.5% of the obesity class III group, a 2.4-fold difference across increasing adiposity bands (each band versus healthy BMI, P < 0.001). Thus, early-life adiposity not only preceded PMOS diagnosis but stratified subsequent incidence across a pronounced dose gradient. Fourth, among 1,416 matched case–control pairs with AI-extracted serial body-composition measurements, women with PMOS had 9.8 kg greater fat mass at diagnosis than controls (55.4 kg vs 45.6 kg, P < 0.001). Despite greater absolute lean mass, their lean-mass proportion was 3.8 percentage points lower (52.0% vs 55.8%, P < 0.001). Approximately 72% of the 14.1 kg excess total weight near diagnosis was non-lean mass, revealing that the prediagnostic weight excess was predominantly adipose. Together, these analyses trace an adiposity-linked prediagnostic continuum that begins during pediatric and adolescent years, stratifies more than fivefold variation in subsequent PMOS incidence and culminates in predominantly adipose weight excess before reproductive-age diagnosis.

Abstract: This report focuses on cognitive gaps for pressure dependence of the melting temperature, presenting: (i) the coherent discussion on T_m (P) behaviour both for ‘Hard’ Matter (‘standard’ solid state) and Soft Matter systems, to reveal the importance of intermolecular interactions strength, (ii) the model picture linking systems with (dT_m)⁄(dP>0), (dT_m)⁄(dP< 0) and T_m (P) curve maximum; which also shows the relevance of the negative pressures domain, (iii) the ultimate validation test of the recent ‘universal’ scaling of T_m (P) pattern derived by Trachenko [Phys. Rev. E 2024, 109, 034122] against empirical data. The evidence for ‘Hard Matter’ focuses on systems relevant for the semiconductor industry, but yet under-discussed: Silicon, Germanium, and Gallium Nitride. For Soft Matter, these include unique polymeric systems and liquid-crystalline pentylcyanobiphenyl (5CB). For the latter, symmetry-selected melting/freezing takes place. Finally, the specific case of graphite and diamond a discussed. The report includes an innovative solution for melting temperature under-pressure detection.

Abstract: Oil-based mud filtrate (OBMF) invasion significantly alters the petrophysical response of nuclear magnetic resonance (NMR) logging, severely compromising the accuracy of reservoir fluid identification and petrophysical evaluation. However, the NMR relaxation behavior of OBMF under extreme high-temperature (>100°C) and low-frequency (< 2MHz) conditions remains poorly understood. In this study, temperature-dependent NMR experiments were conducted from 30°C to 100°C at a fixed frequency of 21MHz, while frequency-dependent experiments were performed from 1 MHz to 21MHz at 30°C. Using combined analysis of T₂ spectra and T₂-T₁ two-dimensional spectra, the effects of temperature and magnetic field frequency on the relaxation characteristics of OBMF were systematically investigated. The results show that increasing temperature shifts the T₂ distribution toward longer relaxation times, increases T₁ values, and decreases the T₁/T₂ ratio. In contrast, decreasing frequency leads to prolonged T₂ relaxation times, shortened T₁ relaxation times, and an increased T₁/T₂ ratio. Based on these experimental findings, a dual-parameter model incorporating both temperature and frequency was established for OBMF. The proposed model provides a theoretical basis for the analysis and correction of NMR logging data under oil-based mud invasion conditions.

Abstract: Linde Type A (LTA) zeolite was synthesized from purified Angren kaolin (Uzbekistan) by a metakaolin route. Raw kaolin was beneficiated by dispersion–decantation to reduce iron, calcined at 700 °C, and crystallized in NaOH solution at 100 °C. Products were characterized by powder X-ray diffraction (PXRD), X-ray fluorescence (XRF), Raman spectroscopy, N₂ physisorption and thermal analysis (TGA/DTA). PXRD confirmed crystalline LTA as the major phase (cubic lattice parameter a = 24.68 ± 0.02 Å), with residual quartz from the precursor. XRF gave a near-ideal bulk composition (Si/Al = 1.07, Na/Al = 0.98), confirming sodium incorporation, and Raman corroborated minor quartz and anatase impurities. The low N₂ BET area (~2.5 m² g⁻¹) reflects restricted access of the 4 Å LTA windows to N₂ at −196 °C rather than absence of microporosity. Thermogravimetric analysis showed a ~13 wt% loss of zeolitic water below 300 °C, confirming a hydrated microporous framework. Locally sourced Angren kaolin can thus be converted into crystalline LTA, a candidate material for gas-separation and ion-exchange applications.

Abstract: Entanglement swapping is the cornerstone of quantum networks,while quantum information masking is a method to ensure data privacy. The combination of the two can open new perspectives for ensuring information security in quantum network environments. In this paper, we study entanglement swapping with information masking. We consider the entanglement swapping that combines several quantum information masking methods, including the entanglement swapping of two-particle entangled states and multi-particle ones. Based on our previous work [2025, arXiv, 2009.02555v11], we derive the entanglement swapping formulas. Besides, we put forward some hypotheses about observers, which, although not the main focus of this paper, can inspire people to rethink quantum mechanics phenomena and the relationship between humans and nature.

Abstract: Artificial-intelligence-driven screening on wearable and IoT-connected physiological sensors is limited by the scarcity of holistic, ethically shareable datasets that jointly capture sensor readings, symptoms, demographics, and clinically grounded condition labels. Randomly generated synthetic data can fill this gap in volume but typically fails to preserve the clinically meaningful correlations that a screening model must learn. This paper presents an evidence-to-data framework for constructing a synthetic multi-condition health-screening dataset from measurable IoT-derived physiological indicators (heart rate, blood pressure, peripheral oxygen saturation, body temperature, and electrocardiogram morphology) combined with structured symptom, demographic, and contextual profiles. Twenty-eight conditions spanning oxygenation, cardiovascular/hemodynamic, thermoregulatory, respiratory-infectious, gastrointestinal, urinary, neurological, psychophysiological, dermatological, and cardiac-rhythm categories are characterized from peer-reviewed literature and clinical guidelines, each with defined sensor thresholds, symptom profiles, demographic modifiers, confounders, and clinical exceptions. A cross-condition analysis quantifies feature overlap and identifies the minimal discriminative feature sets that separate clinically similar presentations. This evidence base is then formalized into a structured knowledge model and dataset schema in which every variable, conditional dependency, and label is traceable to a specific clinical finding, providing a reproducible foundation for generating, evaluating, and benchmarking screening-oriented machine-learning models. The framework was implemented end-to-end: a 200,000-record synthetic generator was built from this schema, and a multi-output Random Forest baseline trained on the resulting dataset achieves macro-F1 of 0.936 (Disease_Label), 0.933 (Screening_Category), and 0.993 (Screening_Outcome), with under 0.5% false-negative escalation on the highest-severity class, confirming that the encoded clinical structure is learnable. The resulting framework, generator, and baseline model are intended to support the development and preliminary validation of IoT-based multi-condition screening systems and are explicitly not intended for definitive clinical diagnosis.

Abstract: The JSSP is recognized as one of the most difficult combinatorial optimization problems because it as well and indeed can be classified as an NP-hard problem. This paper introduces two metaheuristic frameworks which both make use of Iterated Local Search, but differ fundamentally from each other in the way they represent solutions. The initial metaheuristic, called the Priority-based metaheuristic, creates schedules using priority dispatching rules guided by ILS. In contrast, the second metaheuristic, referred to as the Permutational coding-based metaheuristic, employs a permutation coding approach for every operation and directly implements neighborhood moves on this sequence. Both metaheuristics aim to reduce makespan. To evaluate their performance, computational experiments were performed in MATLAB on recognized benchmark datasets to analyze the quality of solutions. The main goal is to identify which representation provides a better equilibrium between exploration and exploitation abilities when combined with the same ILS methods. The JSSP is known to be one of the complex problems of combinatorial nature due to the fact that it belongs to the class of NP-hard problems. In this study, two novel methods of solving the JSSP are presented and evaluated. The two methods are that both make use of the Iterated Local Search (ILS) algorithm. The first method, termed Priority-based metaheuristic, is the one which constructs schedules according to priority scheduling rules based on ILS. On the other hand, the second method, which is termed Permutational coding-based metaheuristics, is built on the idea of coding each operation in terms of a permutation of the operation and putting the neighborhood operations directly on the short string. The two metaheuristic methods are aimed at minimizing the makespan. In order to test the performance of the two methods, computational tests are conducted in MATLAB using the standard benchmark tests to investigate the performance of the problems solved.

Abstract: Construction supply chains are pivotal to circular economy (CE) transitions but remain structurally fragmented, limiting the scalability of resource-efficient solutions. At the same time, digital technologies and life cycle assessment are often deployed in isolation, constraining their ability to enable system-level circularity. Using a theory-building literature synthesis of 141 publications across circular economy, sustainable supply chain management, digitalization, and life cycle sustainability assessment, this study develops an integrated conceptual framework that explains how circular performance can be achieved through the interaction of artificial intelligence (AI), life cycle sustainability assessment (PESI-LCA), and system-level alignment (DCAM). Drawing on an integrative synthesis of sustainable supply chain management, CE, and digitalization research, AI is conceptualized as a dynamic capability for prediction and optimization, while PESI-LCA is positioned as an operationalized LCSA-based con-straint system that embeds environmental, social, and economic criteria into decision architectures. DCAM defines the alignment conditions required across digital infra-structure, circular strategies, business models, and institutional enablers. The framework advances a non-additive logic: circular outcomes emerge only when sustainability constraints shape AI-driven decision-making and when alignment enables coordinated implementation across supply chains. A key contribution is the identification of structural distortion as a failure mode in which digital optimization reinforces linear resource flows. The study advances sustainable supply chain theory and offers testable propositions and governance implications for scaling circular construction systems.

Abstract: Agroforestry is widely promoted as a socioecological strategy that can integrate biodiversity conservation, climate change mitigation, sustainable production, and rural development, yet the relative position of rural livelihoods within the scientific structure of the field remains insufficiently understood. This study evaluated the temporal evolution, conceptual organization, and geographical and economic distribution of global agroforestry research, with particular attention to the autonomy of livelihood-related research. A bibliometric analysis was conducted using 5,713 Scopus-indexed journal articles published between 1976 and 2025. Records were classified into four non-exclusive analytical domains: biodiversity/conservation, climate-carbon-soil, production/silvopastoral-agronomic systems, and livelihoods/socioeconomic dimensions. The analysis combined temporal bibliometric indicators, semantic normalization of Author Keywords, Multiple Correspondence Analysis, Sankey mapping, domain proportion tests, chord diagrams, and chi-square tests. Agroforestry research expanded substantially and became thematically more diversified. Climate-carbon-soil and production/silvopastoral-agronomic systems showed the highest proportional contributions, whereas rural livelihoods were visible but did not form an autonomous conceptual domain. Instead, livelihood-related terms were embedded within a broader socioecological/conservation cluster. Livelihoods/socioeconomic research was more strongly represented in Africa, Asia, and lower-middle- and low-income contexts. These findings indicate that rural livelihoods are present in agroforestry research but remain less conceptually autonomous than environmental, climate-related, and production-oriented domains.

Abstract: Large language model (LLM) accelerators are commonly optimized for fixed model structures and isolated inference workloads, whereas emerging artificial-intelligence operating systems (AI OSs) primarily virtualize agents, memory, and tools in software. This work investigates whether LLM inference can instead be exposed as a secure and reusable hardware service. We introduce HCDP-LLM, an AI-OS-managed hierarchical common datapath that composes reusable tensor, reduction, nonlinear, quantization, memory, and key–value (KV)-cache modules through model capsules and a Moore finite-state controller. The architecture is formalized as a finite-precision multi-tape computational transducer, enabling deterministic checkpointing, migration, isolation, and Turing-machine-based bit-time, space, and I/O analysis. The derived bounds distinguish quadratic prompt-attention work, context-linear per-token attention, and context-linear KV-cache storage, while exposing the different compute- and bandwidth-dominated regimes of prefill and decoding. The formal model further establishes that any fixed physical realization is necessarily finite-state, whereas a scalable implementation family with extensible read/write memory can support universal computation. These findings indicate that Hardware LLMs are feasible not as permanently hardwired models, but as runtime-composable, weight-programmable inference fabrics. HCDP-LLM therefore provides a pragmatic foundation for secure multi-model and multi-agent acceleration without full hardware resynthesis.

Abstract: Rapid detection of organophosphorus pesticides (OPs) is urgently needed. A recombinant organophosphorus hydrolase (OPH) was expressed in E. coli and immobilized onto polyaniline nanofibers (PANF) and magnetic particles (PAMP) via adsorption–crosslinking (EAC) or adsorption–precipitation–crosslinking (EAPC). The immobilized OPH was characterized and integrated into an electrochemical biosensor for methyl-paraoxon detection. The EAPC method on PAMP gave the highest activity recovery (80.4%). Immobilized OPH showed optimal activity at pH 11 and 55 °C, with improved thermal stability (50% activity retained after 2 h at 60 °C) and storage stability (>80% after 30 days). The biosensor exhibited a linear response to methyl-paraoxon from 1 to 5000 μmol·L⁻¹ (R² > 0.99), with optimal detection at pH 12 and a scan rate of 150 mV·s⁻¹. The EAPC-PAMP immobilization strategy effectively stabilizes OPH, and the resulting biosensor offers a fast, sensitive, and stable platform for OP residue analysis.

Abstract: Cold hypersensitivity in hands and feet (CHHF) affects 20–52% of East Asian populations, is more prevalent in women, and is managed with vasodilators in Western medicine or herbal therapies in traditional East Asian medicine. This study was conducted to compare the pharmacological characteristics of two heat-clearing (HCHs, Scutellariae Radix and Coptidis Rhizoma) and two blood-tonifying herbs (BTHs, Angelicae Sinensis Radix and Paeoniae Radix Alba) used for CHHF. Candidate herbs were selected based on herbology classifications, PubMed searches, and the Korean Medicine Clinical Practice Guideline for CHHF. A total of 61 active compounds and 323 corresponding protein targets with CHHF were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), standardized via UniProt, and intersected with CHHF-related genes from GeneCards. Protein–protein interaction networks were con-structed, core targets identified, and functional enrichment analyses performed using Gene Ontology and Reactome databases. A total of 166 herb–CHHF common targets were identified, including 24 shared across all four herbs. HCHs were primarily enriched in hemostasis-, immune-, and signaling-related pathways, whereas BTHs were associated with immune regulation, metabolism, and neuronal modulation. Notably, Scutellariae Radix and Paeoniae Radix Alba showed substantial overlap in enriched pathways. These findings suggest that HCHs and BTHs may act through distinct yet complementary mechanisms in CHHF, providing a mechanistic basis for combined traditional use.

Abstract: Background/Objectives: Implant impressions can present inaccuracies, affected by many factors, such as impression technique, impression material, parallelism or not among the implants. The use of a verification index can assess the accuracy of the mastercast, and can be used in order to adjust the inaccuracy of the mastercast. This article presents a novel technique for fabricating a verification index, followed by implant analog repositioning, which can be used in case of an impression inaccuracy. Case Presentation: A patient with moderate gag reflex received an implant. The impression of an implant and a prepared tooth was made. At the metal try-in an inaccuracy of the mastercast was found, attributed to the final impression. A technique was illustrated, describing a poly-methel methacrylate (PMMA) transfer index fabrication, as well as the implant analog repositioning in the removable die of the master cast. This method can skip the remaking of a new impression. The novelty in this technique is the repositioning of the implant analog in the removable die. Conclusions: This is a viable technique, which can replace the need for a new impression, and therefore skip one appointment. It is important for patients with moderate to severe gag reflex, since it can minimize the discomfort and stress for the patient and the dentist.