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Background: Therapeutic recombinant biological products are essential in modern medicine, but their immunogenicity remains a major concern for both clinical safety and therapeutic efficacy. Protein aggregation may occur during manufacturing, storage, transportation, and administration, potentially increasing immune recognition. Here, we have reviewed the mechanisms underlying aggregation-associated immunogenicity and summarized strategies for mitigating the associated risks. Methods: We integrated published evidence on therapeutic recombinant proteins, monoclonal antibodies, antibody–drug conjugates, peptide and glycoprotein hormones, and recombinant blood products. We focused on aggregation-induced structural changes, immune activation pathways, differences among product types and aggregation states, and approaches for controlling aggregation and mitigating immunogenicity. Results: Protein aggregation creates or reveals neoepitopes by disrupting the native conformational state, exposing previously buried hydrophobic regions, and generating nonnative interfaces. Aggregates may further promote innate and adaptive immune responses through repetitive epitope presentation, pattern-recognition receptor signaling, complement activation, cytokine release, dendritic-cell maturation, enhanced antigen presentation, and B-cell receptor cross-linking, ultimately increasing the likelihood of anti-drug antibody formation. Aggregate size and morphology, protein characteristics, formulation, storage conditions, administration route, and other biological factors influence the immunogenic potential of aggregates. Thus, effective risk reduction requires integrated control strategies, including molecular and structural optimization, formulation design, manufacturing-process control, appropriate storage and transportation, and sensitive analytical monitoring. Emerging artificial-intelligence approaches may further facilitate the prediction and design of low-immunogenicity biologics. Conclusions: Protein aggregation is a multifactorial determinant of immunogenicity. Controlling aggregation throughout the product lifecycle is essential to reduce immune-mediated risks and maintain safety, stability, and efficacy of therapeutic recombinant biological products. Full article

Existing heritage digital twins document geometry and monitor condition with high fidelity, but rarely model the behavioral process through which owners, authorities, and peer-exchange networks jointly determine the long-run conservation profile of a historic district, and therefore cannot answer the subsidy-design questions heritage authorities face. This paper supplies that missing behavioral layer. We formalize heritage conservation credit systems (indivisible credits, capped holdings, balanced aggregate stock) as a matching model and establish four analytical guarantees for twin operation: predictions exist, are unique, are globally stable, and vary continuously differentiably with policy parameters. Global stability acts as a resilience certificate, guaranteeing recovery from any shock within roughly twenty annual cycles, and smoothness bounds prediction uncertainty in closed form. These certificates are properties of the behavioral engine, established analytically; they are not claims about field-validated operation. A three-layer architecture embeds the engine behind a state-synchronization loop that re-estimates behavioral parameters from registry observables at a cadence set by the data source rather than by the model; in a controlled in silico sensing experiment the synchronized twin reduces post-disruption tracking error by 71% relative to a static twin, and a window-length sweep shows the loop dominates the static twin by 62–71% at every cadence tested. Calibrated to a traditional village in the Mount Tai Piedmont and verified against an agent-based Monte Carlo counterpart, the twin yields three model-dependent policy results: a subsidy trap (higher subsidy rates activate conservation exchange yet reduce steady-state top-tier counts), the dominance of within-tier cooperation over cross-tier mentorship, and an inverted-U sustainability frontier locating Mount Tai villages in the under-funded regime. All outputs consolidate into a single HBIM-compatible information-management deliverable. We report the framework as a specified and in-silico-verified behavioral twin whose sensing interfaces await field deployment, and we state explicitly which components remain conceptual. Full article

Tourism seasonality affects destination sustainability by concentrating demand into a short period and leaving accommodation capacity underused off-season. This study examines whether Romanian tourism reverted to its pre-pandemic seasonal pattern once volumes recovered, comparing 2019 with 2025 on monthly INS TEMPO-Online series for arrivals, overnight stays and net occupancy, from which operating capacity is derived. Concentration is measured with four complementary indices, nationally, for exhaustive partitions by visitor origin and accommodation category, and for four destination case studies. Arrivals exceeded the 2019 level by 4.89% while overnight stays returned to it, the average length of stay fell by 5.25%, operating capacity rose by 14.03%, and net occupancy fell in every month from 34.15% to 29.76%. The national Gini index of arrivals rose by only 1.20%, less than the change recorded between the pre-pandemic calendar years 2018 and 2019; counterfactual reweighting attributes this near-stability to offsetting component movements. Evidence for a change in national structure therefore rests on the seasonal-group and dispersion measures rather than on month-level inequality. Six of twelve series exceed the pre-pandemic year-on-year change on all three tested measures, among them operating capacity, non-resident arrivals and the seaside cases. The analysis is descriptive; national aggregates understate the redistribution it documents. Full article

Soil degradation in the agro-pastoral ecotone of North China severely constrains the productivity of vital forage crops like oat (Avena sativa L.). While biochar and humic acid are known soil amendments, their combined synergistic effects and optimal application ratios remain poorly understood. This study aimed to investigate the integrated effects of biochar and humic acid on soil properties and oat productivity to identify an optimal co-application strategy for this fragile ecosystem. A three-year field experiment (2023–2025) was conducted using a randomized complete block design. Treatments included a control (CK), sole biochar (B1H0), sole humic acid (B0H1), and three combined biochar-humic acid ratios (7:3, 5:5, and 3:7 by mass; B7H3, B5H5, B3H7). We measured key soil physicochemical and biological properties, as well as oat growth, forage yield, and quality parameters. Partial least squares structural equation modeling (PLS-SEM) was employed to elucidate the causal pathways linking amendments, soil quality, and crop performance. The combined application of biochar and humic acid demonstrated significant synergistic effects, outperforming sole applications in improving soil health and oat productivity. The B5H5 (1:1 ratio) treatment was the most effective, significantly reducing soil bulk density by up to 22.4% and increasing the mean weight diameter of water-stable aggregates by 40.7% compared to the CK. This treatment also led to the greatest increases in soil organic matter (68.9%), microbial biomass carbon (142.2%), and the activities of sucrase (139.8%) and urease. Consequently, the B5H5 treatment enhanced oat forage yield by 56.1% and improved its nutritional quality by reducing acid detergent fiber and neutral detergent fiber content by 22.9% and 12.7%, respectively. The PLS-SEM analysis confirmed that the positive effects of the amendments on crop performance were primarily mediated through the enhancement of soil quality (path coefficient β = 0.75, p < 0.001). The integrated application of biochar and humic acid, particularly at a 1:1 mass ratio, is a highly effective strategy for restoring degraded soils in the agro-pastoral ecotone. These findings provide a strong scientific basis for promoting this co-amendment approach as a sustainable solution to enhance agricultural resilience and productivity in fragile environments. Full article

Semiconductor nanocrystals with a perovskite crystal structure, both fully inorganic and hybrid, have found broad application as scintillation materials over the past decade, owing to their high absorption capability, fast response time, tunable optical properties, and radiation hardness achievable through compositional and matrix engineering, as demonstrated in a large number of recent works. At the same time, scintillating materials based on perovskite nanocrystals (PNCs) are not devoid of drawbacks. These fall into two groups: intrinsic limitations—such as self-reabsorption of the emitted light caused by a small Stokes shift, and a soft ionic lattice with labile surface ligands that renders PNCs sensitive to humidity, heat, and ionizing radiation—and limitations introduced during composite fabrication, such as aggregation at the PNC loadings needed for sufficient X-ray absorption, poor compatibility between the ligand shell and the host matrix, light scattering, and matrix-induced degradation. These limitations constrain scintillator performance—particularly the light yield achievable without additional material engineering—and necessitate the development of new synthesis strategies and material architectures. The characteristics of PNC-based scintillators are determined both by the PNC synthesis route—in situ growth directly within the host material, or the use of pre-synthesized PNCs subsequently incorporated into a supporting matrix—and by compositional and functional modification strategies such as PNC doping, surface ligand engineering, or coupling of PNCs with an energy donor, acceptor, or plasmonic component. In this review, we examine how PNC chemical composition, surface ligands, synthesis route, and other fabrication strategies jointly determine the performance of the final functional scintillator, in terms of light yield, radiation detection limit, radio- and photoluminescence decay time, stability under ionizing radiation and environmental factors, optical transparency, and spatial resolution. We discuss the most problematic issues associated with these composite nanomaterials and highlight the most promising strategies for developing highly efficient, long-lasting scintillators for applications in medical imaging, radiotherapy, forensics, and quality control. Full article

Language ability reflects the developmental level and social communication in children with autism spectrum disorder (ASD), making scalable and explainable assessment essential. Existing large language model (LLM)-based methods mainly rely on single-agent reasoning or aggregate independent outputs, offering limited insight into disagreement identification, evidence exchange, and consensus formation. To address this limitation, this paper proposes a Multi-Agent Consensus framework that formulates language assessment as a structured consensus process among LLM-based agents. Using parent–child free-play transcripts, three Expert Agents independently assess language comprehension, vocabulary competence, logical expression, and communication skills under shared criteria. A Judge Agent identifies dimension-level disagreements, marks dimensions with consistent judgments as resolved, coordinates evidence-based negotiation, and adjudicates remaining conflicts. On a real-world dataset of 79 children, the framework achieves a Pearson correlation of 0.843 with an external standardized language development measure, outperforming Single Agent (0.802) and Initial Expert Aggregation (0.814), while maintaining strong cross-run stability. Ablation and process analyses show that consensus negotiation integrates initial judgments, resolves disagreements, and improves assessment stability. Case analysis and human expert evaluations support the plausibility, evidential grounding, and transparency of the generated reports. These findings suggest that multi-agent consensus can enhance automated language assessment for children with ASD while providing structured, traceable explanations. The correlations support criterion validity, but clinical validity and effectiveness in practice require further evaluation given the limited sample size and data sources. Full article

Reproducible packet-level wireless experimentation requires more than access to a testbed or a traffic generator: the configuration, packet evidence, metric semantics, software state, and provenance of an execution must remain connected. Existing physical testbeds, emulators, traffic generators, and general reproducibility frameworks typically address these concerns at different layers, leaving a practical gap for a lightweight workflow that couples packet-level evidence with explicit observation-level semantics. This paper presents a software-first open-source workflow in which a declarative profile is converted into a canonical packet table, analyzed separately at per-node and merged-stream levels, and accompanied by manifests and integrity hashes. The workflow is evaluated through fixed-input determinism, configuration-to-record consistency, metric checks, 30-seed stability, controlled-omission and offered-load sensitivity, a local reference scalability benchmark, and automated fresh-environment continuous-integration executions. For the tagged artifact used by this study, the CI workflow successfully installs the software, runs the test suite, executes a synthetic demonstration, and verifies deterministic reference traces on Ubuntu with Python 3.9, 3.11, and 3.12. With omissions disabled, generated record counts match the configured event model and the packet traces contain no missing or duplicate sequence identifiers. The revised span-based event-rate estimator uses the $N-1$ observed inter-event intervals rather than N records, preventing the systematic finite-span inflation of the previous formulation. Across 30 seeds per profile, span-rate coefficients of variation remain below 0.007%; controlled omission tests expose the expected boundary limitation of sequence-span completeness; and a local reference benchmark scales from 6000 to 120,000 records with approximately linear generation, analysis, and CSV-storage growth. The aggregation-ratio calculation is treated as an internal consistency invariant rather than as independent validation. The resulting workflow is intended to help researchers inspect the configuration-to-evidence chain and to help educators teach provenance and aggregation-aware interpretation without conflating synthetic traces with PHY/MAC or radio measurements. The present evaluation remains synthetic and does not establish physical-link fidelity, capture accuracy, deployment performance, or learning gains. Full article

Concrete production consumes vast quantities of natural aggregate and potable water, while coastal dredging produces hundreds of millions of cubic meters of underutilized marine sediments and sea sand annually. This study aims to provide a review on recycling dredged marine sediments, sea sand, and seawater into sustainable mortars and concrete. Systematically synthesizing the investigations on cement hydration kinetics, phase transformations, fresh-state workability, mechanical strength evolution, and long-term durability mechanisms under aggressive marine exposure. The studies revealed that chlorides and sulphates in seawater and unwashed marine sands accelerate early hydration kinetics, advancing exothermic heat flow peaks by 1.2 to 1.7 h and boosting early compressive strength by up to 53%. However, this rapid early reaction leads to long-term trade-offs, including nearly doubled drying shrinkage, increased scaling during salt-frost cycling, and elevated corrosion vulnerability for steel reinforcement. Furthermore, dredged sediments cause substantial workability loss and strength deterioration beyond a threshold value as clinker or fine aggregate replacement, primarily due to clay water absorption and dilution effects. Thermal calcination (650–850 °C) and washing pretreatments, combined with supplementary cementitious materials (supplementary cementitious materials, SCMs, like ground granulated blast-furnace slag, fly ash, and metakaolin), effectively mitigate these adverse effects by consuming Portlandite, stabilizing Friedel’s salt, and refining pore tortuosity. An application-oriented utilization framework is established: fine clayey sediments are best suited as calcined SCMs at a 5–15% replacement, washed sand fractions serve structural applications up to 30%, and stabilized bulk sediments are ideal for non-structural masonry and sub-bases at 40–70%. These insights provide concrete guidelines for safely adopting marine-derived resources, diverting harbor waste from landfills, and advancing resource circularity in coastal civil infrastructure. Full article

Federated learning (FL) enables collaborative training across distributed clients without directly sharing their raw data. However, non-IID data distributions and malicious behaviors can produce unreliable and conflicting client updates, severely degrading aggregation stability and global generalization. Existing robust aggregation methods often depend on additional trust assumptions, server-side clean data, or expensive pairwise update comparisons, which not only limit their practicality but also increase computational and resource overhead in real-world FL scenarios. To address these limitations, we propose FedRGEA, a reliability-guided bio-inspired evolutionary aggregation framework for resource-aware robust federated aggregation. FedRGEA integrates self-calibrated reliability assessment (SCRA) with reference-guided conflict-aware evolutionary aggregation (RCEA). Specifically, SCRA constructs dynamic references from client updates themselves and evaluates update reliability through multi-criteria deviation analysis without relying on externally trusted references. The resulting reliability priors are incorporated into RCEA, which constructs a refined reference from the retained updates and formulates robust aggregation as a simplex-constrained evolutionary weight search problem by jointly considering reliability priors, reference-based conflicts, and weight concentration. Experimental results show that FedRGEA outperforms representative FL and robust aggregation baselines under the 40% sign-flipping attack, attaining accuracies of 77.88%, 83.97%, and 97.92% on ISIC 2019, HAM10000, and BrainTumor, respectively. These results demonstrate the effectiveness of FedRGEA in maintaining robust aggregation under a high proportion of malicious clients. Full article

High-stakes and regulated decision-support systems require predictive models that are simultaneously accurate and interpretable, yet accuracy is frequently obtained through opaque models whose behaviour is difficult to audit. This paper develops an explainable-by-design classification framework in which interpretability is treated as a constructive objective rather than a post hoc add-on. We aggregate thirteen complementary relevance metrics into a single, stable ranking and then cast the selection of a minimal relevant feature subset as a multi-objective optimization problem that trades predictive quality against structural complexity. The resulting accuracy–complexity Pareto front is approximated with the NSGA-II algorithm, and an equivalence-aware rule selects, among statistically indistinguishable models, the most parsimonious and hence most transparent one. We further (i) characterize the feature-subset $\epsilon$-Rashomon set of each problem, (ii) quantify the stability of the aggregated ranking under resampling and contrast it with the individual metrics, and (iii) benchmark the induced subsets against established selectors (mRMR, Boruta, RFE, LASSO) and against opaque full-feature baselines, including gradient boosting. The framework is validated with a leakage-free nested cross-validation—in which the ranking, the search and the selection are recomputed inside each training fold—across six heterogeneous domains (healthcare, industrial production, climate, socio-economics, and education). Empirically, reducing the feature space to about three features preserves predictive accuracy: under paired tests corrected for fold dependence (Nadeau–Bengio) and for multiple comparisons (Holm), the minimal model is statistically indistinguishable from the full-feature model on all six datasets and is never significantly worse: reducing to a handful of features is essentially free. The main payoff is therefore not an accuracy gain but the characterization it enables—a simple ranked prefix performs comparably to the equivalence-aware selection (a direct signature of the Rashomon effect), and every problem admits a large set of near-equivalent minimal subsets sharing a compact stable core, which we map explicitly. The aggregated ranking is also more stable than the average individual metric in four of six datasets. These results support an explainability-by-design paradigm in which the accuracy cost of transparency—incurred only on the hardest multiclass tasks—is small, explicit, and justified when auditability and human oversight are required. Full article

With the acceleration of industrialization and increasing human activities, antibiotic pollutants enter aquatic environments through various pathways and pose serious threats to ecological safety and human health. Ofloxacin (OFX), a typical fluoroquinolone antibiotic, is persistent in water media owing to its high chemical stability and poor biodegradability. In this study, MgO/CuFeO₂-based Fenton-type composite catalysts were prepared to investigate their efficiency in removing OFX from water. Among the as-prepared materials, MgO/CuFeO₂-20 wt% exhibited the optimum catalytic activity: under the conditions of 20 mg L⁻¹ initial OFX concentration, 0.5 g L⁻¹ catalyst dosage and 20 mM H₂O₂ at natural pH, it achieved approximately 95% ofloxacin removal within 120 min, with a pseudo-first-order kinetic rate constant k₁ of 0.0461 min⁻¹. Benefiting from MgO modification, the catalyst maintained high degradation efficiency over a broad pH range of 5–9 and retained satisfactory catalytic performance after five consecutive cyclic runs with only minor activity decay. Characterizations including XRD, SEM-TEM, XPS, BET, LSV-EIS and ESR-DMPO verified that the construction of tight MgO-CuFeO₂ heterointerfaces accelerated interfacial electron transfer and promoted the regeneration of Cu⁺/Cu²⁺ and Fe²⁺/Fe³⁺ redox couples. Hydroxyl radicals (·OH) were identified as the dominant reactive oxygen species for pollutant oxidation. This work provides a facile modification strategy to alleviate particle aggregation and metal-ion leaching drawbacks of bare CuFeO₂, offering experimental support for designing high-efficiency delafossite-based heterogeneous Fenton-like catalysts for antibiotic-containing wastewater remediation. Full article

Automotive manufacturers face interacting financial pressures arising from capital intensity, technological transition, supply-chain uncertainty, and changing financing conditions. This study develops a Heun Equation-Based Digital Twin Framework (HEDTF) to represent corporate financial stability as an evolving multidimensional state rather than a binary distress outcome. A cross-sectional proof-of-concept uses 16 accounting ratios for 15 publicly listed automotive manufacturers, aggregated into five dimensions: liquidity and balance-sheet structure, profitability, financial structure, solvency, and operational efficiency. Indicators are normalized to a 1–5 scale, combined using transparent hybrid calibrated weights, and propagated through a Heun predictor–corrector scheme under baseline, adverse, and strategic-improvement scenarios. The weighted Financial Stability Index (FSI) closely corresponds to an equal-weight comparison index (MAE = 0.056; RMSE = 0.075; Pearson r = 0.993; R² = 0.986). Because both indices are constructed from the same five normalized dimensions, these statistics are interpreted as internal-consistency measures rather than independent evidence of forecasting accuracy. Scenario simulations show lower FSI values under coordinated adverse shocks and higher values under coordinated strategic improvements, with the magnitude varying by initial financial configuration. Overall, the HEDTF is best interpreted as a transparent scenario-based financial decision-support prototype; longitudinal out-of-sample validation is required before predictive claims can be made. Full article

In regions where suitable natural aggregates are scarce or expensive, soils are often treated with cement to produce pavement base and subbase materials. This study evaluated soil–RCA geopolymer mixtures as a sustainable alternative for these applications. Response surface methodology (RSM) was used to prepare mixtures containing soil, recycled concrete aggregate (RCA), Class F fly ash (FA), sodium hydroxide, and sodium silicate. The effects of FA, RCA, and liquid sodium silicate (Na₂SiO₃) in the alkaline activator solution on compressive strength ($f_c$), elastic modulus (E), and density were examined using regression analysis, Analysis Of Variance, and sensitivity analysis. Selected mixtures were further evaluated using wetting and drying, freeze and thaw, and scanning electron microscopy (SEM). Geopolymer stabilization increased strength and stiffness compared with untreated soil. FA improved both $f_c$ and E, while RCA produced a nonlinear response, with 15% RCA providing favorable mechanical performance. Increasing Na₂SiO₃ reduced both responses under the tested conditions. The mixtures remained within the mass and volume change limits after 12 cycles. SEM observations showed geopolymer reaction products, particle coating, and matrix densification. The findings indicate that soil–RCA geopolymer mixtures can provide suitable performance for pavement base and subbase layers while reducing reliance on cement and virgin aggregates. Full article

Background: Trophoblast cell surface antigen 2 (TROP2) is widely overexpressed in epithelial cancers and associated with poor prognosis, representing an attractive target for cancer treatment. Recent clinical studies reported favorable outcomes of TROP2-targeting antibody–drug conjugates. However, heterogeneity of human cancers and development of resistance encourage the exploration of alternative cytotoxic payloads. In this regard, bacterial immunotoxin is a viable treatment option owing to the unique and potent mechanism of action. Methods: We first constructed three immunotoxins by fusing deimmunized Pseudomonas Exotoxin A (PE24) to novel TROP2-targeting single-chain variable fragments identified through phage display, which were screened for high binding and cytotoxic activity against tumor cell lines with distinct TROP2 expression. The lead immunotoxin T3PE was engineered by introducing extra disulfide bonds and an albumin-binding domain (ABD) to generate disulfide-stabilized T3PE (dsT3PE and dsT3PE_ABD_CC). Then, both variants were characterized in stability, cell binding, internalization, pharmacokinetic profiles, and in vitro and in vivo antitumor activities. Results: T3PE bound strongly to TROP2 (EC₅₀ = 0.3824 μg/mL) and exerted potent cytotoxicity against NCI-N87 cells (EC₅₀ = 0.09074 μg/mL), but it displayed poor serum stability and was prone to aggregation. Introduction of disulfide bonds stabilized the immunotoxins while preserving the potent cytotoxic activity, and fusion of ABD extended the plasma half-life 6-fold in mice. Additionally, five doses of either dsT3PE or dsT3PE_ABD_CC at 3 mg/kg could significantly reduce tumor progression in the NCI-N87 mouse xenograft model. Conclusions: Together, these results demonstrated the therapeutic potential of these novel anti-TROP2 immunotoxins against TROP2-overexpressing tumors. Full article

Requirements prioritization methods increasingly consider multiple quality dimensions simultaneously, but the way these dimensions are aggregated determines what information reaches decision-makers: methods that combine dimensions into a single score can mask trade-offs that Pareto-based or outranking methods keep visible, and no evidence currently guides which choice is appropriate for a given project. This paper presents a systematic literature review of 56 studies of requirements prioritization methods published between 2010 and 2025, identified through a search of Scopus, IEEE Xplore, Web of Science, and ACM Digital Library and screened against explicit inclusion and exclusion criteria. The review characterizes how aggregation strategies are distributed across this literature and examines whether prior studies have empirically evaluated their effects on rank stability and decision transparency. The results show that 33 of 56 papers (58.9%) use compensatory aggregation, 16 (28.6%) produce dimensionality-preserving Pareto fronts, 6 are partially compensatory, and only 1 is non-compensatory under this classification framework, with compensatory methods accelerating in the most recent publication period as fuzzy extensions grow more sophisticated. No study in the retrieved corpus directly evaluates the effect of aggregation strategy on rank stability or decision transparency. This review provides the first systematic characterization of aggregation strategies in requirements prioritization and outlines the design of the comparison study needed to give practitioners and method designers an evidence-based basis for choosing among them. Full article