Search Results (252)

Carminic acid (CA) is a high-value natural anthraquinone pigment used in foods, cosmetics, textiles, and pharmaceuticals, but its current industrial supply depends largely on extraction from the scale insect Dactylopius coccus, creating constraints in yield, cost, sustainability, and allergen control. This review summarizes recent progress from pathway elucidation to microbial production. We first outline the structure, occurrence, applications, and biosynthetic logic of CA, emphasizing the convergence of type III polyketide assembly with insect-associated tailoring reactions, especially C-glycosylation. We then compare heterologous production strategies in Escherichia coli, Saccharomyces cerevisiae, Yarrowia lipolytica, and Aspergillus nidulans, focusing on chassis-specific advantages, bottlenecks, precursor supply, malonyl-CoA engineering, dynamic regulation, enzyme compatibility, compartmentalization, and downstream processing. Structurally related anthraquinone pigments are further discussed to extract broader design principles for pathway diversification and synthetic biology. Finally, we highlight key challenges for industrial translation, including low titers, incomplete enzyme characterization, host–pathway incompatibility, and scalable purification, and propose integrated strategies combining precursor-pathway rewiring, AI-assisted enzyme engineering, biosensor-based regulation, and process optimization to develop competitive microbial cell factories. Full article

Automated surface inspection of balsa wood panels is challenging because defects may be small, elongated, weakly contrasted, or visually similar to natural grain patterns. This study proposes YOLOv11–BiFPN–DAAF, an enhanced object-detection architecture that combines bidirectional multi-scale feature fusion with adaptive dual-attention feature refinement. The task was formulated as single-class detection, with all anomalous surface regions labeled as Defect. The dataset comprised 508 manually annotated RGB images, including independent internal and external production test sets. A preliminary screening identified YOLOv11-m512 as the reference configuration, followed by a controlled $2\times 2$ factorial ablation comprising the baseline, BiFPN, DAAF, and their combined integration. Each configuration was trained using five independent random seeds under identical experimental conditions. On the validation set, the combined architecture achieved a precision of $0.893\pm 0.004$, recall of $0.848\pm 0.006$, mAP@0.5 of $0.897\pm 0.004$, and mAP@0.5:0.95 of $0.389\pm 0.004$. Relative to the baseline, the largest improvement was obtained for mAP@0.5:0.95, with a relative gain of 9.89%, indicating improved localization under stricter IoU thresholds. The improvement was retained on the independent internal test set, where the proposed architecture reached mAP@0.5 and mAP@0.5:0.95 values of $0.892\pm 0.005$ and $0.384\pm 0.006$, respectively. On the external production test set, the corresponding values were $0.865\pm 0.007$ and $0.358\pm 0.008$, representing absolute improvements of 0.034 and 0.042 over the original YOLOv11 baseline. Under the matched experimental protocol, YOLOv11–BiFPN–DAAF also achieved the highest principal detection metrics among the evaluated representative detectors. Although BiFPN and DAAF introduced a moderate computational overhead, the architecture maintained an inference time of $9.6\pm 0.3$ ms per image. These findings support the potential of the proposed architecture for automated balsa wood panel inspection, while broader multi-site and hardware-level validation remains necessary before large-scale industrial deployment. Full article

Large language models (LLMs) such as ChatGPT have advanced machine translation, but their quality on low-resource language pairs remains uneven and is typically assessed with automatic metrics alone. This study examines how prompting strategy and source-text type jointly affect LLM translation quality for the low-resource Chinese–Vietnamese pair and whether automatic and human assessments agree. Using a 2 × 3 mixed factorial design, we compared an English-mediated pivot strategy with a direct strategy across informative, expressive, and operative texts, evaluating 60 ChatGPT translations with COMET and with 15 bilingual readers who rated adequacy, fluency, faithfulness, trustworthiness, willingness to use, and need for revision. On COMET, pivoting significantly improved overall quality (p < 0.001), text type was the dominant factor (η² = 0.91; informative > operative > expressive), and strategy interacted with text type, with the largest pivot gain for expressive texts. Human ratings reproduced this ordering but diverged sharply for expressive texts: although COMET favoured the pivot output, readers reported that pivoting raised fluency yet substantially reduced faithfulness (4.10 → 1.77 on a 7-point scale) and were unwilling to accept it. These results show that the value of a prompting strategy is text-type-dependent and that fluent LLM output is not necessarily faithful, underscoring the need to pair automatic metrics with human evaluation when benchmarking low-resource translation. Full article

Constitutive models for clays have historically treated yield geometry, plastic-flow direction, and compression as separate problems, with little regard for their interaction. This paper presents a controlled numerical assessment of how three components—Chatwong et al.’s verified teardrop yield surface, a stress-fractional flow rule, and an AJOP-derived hardening modulus— interact when coupled in a 2 × 2 × 2 factorial design. The components are integrated incrementally along one idealized shear-strain-controlled constant-p′ path with an approximate undrained variant for two independently calibrated clays (Boston Blue Clay and London Clay) under a specified state-dependent fractional order. Within this scope, the main flow effect is consistently the largest single quantity for both soils, and the flow × compression interaction is comparably large wherever defined. Compression’s role grows substantially with the overconsolidation ratio, and the main geometry effect is markedly soil-dependent, scaling with the surface-shape parameter. Two structural singularities are identified: a phase-transformation point in the teardrop surface’s non-associated flow rule, absent from the fractional rule, and a hardening singularity in the AJOP-based modulus, whose tangent falls to the swelling index at a finite, soil-dependent preconsolidation stress, bounding the evaluable overconsolidation range of the compression-related interactions; a proportional-κ variant removes this singularity by construction while preserving the factorial ranking, identifying it as a property of the constant-κ embedding, not of AJOP itself. Under an approximate undrained path, the geometry × flow interaction carries over unchanged, while compression’s role is suppressed several-fold. The borrowed yield surface and flow rule are validated independently against 379 points from real undrained triaxial tests across four calibrated soils using this paper’s own re-calibrated predictions; the fractional–AJOP framework itself is assessed for internal consistency only, and its laboratory validation, together with K₀, cyclic and multi-axial paths, remains for future work. Full article

Brain tumor detection in magnetic resonance images (MRI) is difficult because lesions often have weak boundaries, low contrast, and large variations in scale. Here, we present RD-YOLO, a YOLOv10n-based detector that redesigns neck-level feature fusion for lesion localization. It combines scale-normalized interpolation (SNI), enhanced group-shuffle convolution (GSConvE), and detail-preserving contextual fusion (DPCF). These modules regulate upsampled deep responses, enrich multi-receptive-field representation, and balance shallow structural and deep semantic features. On a public three-class brain tumor MRI detection dataset, RD-YOLO achieved Recall and F1-score values of $0.8326\pm 0.0144$ and $0.8441\pm 0.0058$. Its mAP@0.5 and mAP@0.5:0.95 values were $0.8858\pm 0.0048$ and $0.5490\pm 0.0076$, respectively. Among representative YOLO- and DETR-based baselines, the largest gain occurred for mAP@0.5:0.95, indicating more stable localization under stricter IoU evaluation. Full-factorial ablation showed that the complete configuration achieved the best F1-score and mAP@0.5 and maintained a balanced metric profile across the evaluated indicators. Full article

Symbolic execution of embedded systems faces path explosion, Satisfiability Modulo Theories (SMT) solver bottlenecks, interrupt nondeterminism, and environment modeling complexity. Recent artificial intelligence (AI)-guided approaches using reinforcement learning, graph neural networks, and large language models improve exploration efficiency, yet all reason over raw symbolic states and ignore structural equivalences that arise from symmetry in embedded software. This paper presents S³E, a formal framework that organizes symbolic execution around equivalence classes of states under symmetry transformations. Symmetry groups partition the state space into orbits, and exploration proceeds over canonical representatives within quotient transition systems. Symmetry-aware AI components operate on orbit representatives rather than raw states. Four theoretical results support the framework: orbit preservation, quotient soundness, canonicalization correctness, and constraint reuse correctness. An illustrative case study based on a FreeRTOS-like scheduling environment shows how symmetry reduction collapses equivalent states into orbits, with the potential for reductions that scale factorially with symmetric components. S³E is a theoretical framework; a toy-model prototype validates the core quotient-exploration and constraint-caching mechanis, while empirical evaluation on production firmware remains future work. Full article

Regression testing is a critical activity for maintaining software quality by identifying faults and ensuring system reliability. However, in large-scale software systems, executing all available test cases within limited time and computational resources is often impractical. Therefore, test case prioritization aims to arrange test cases in an effective execution order to maximize testing effectiveness, particularly during the early stages of regression testing. Existing test case prioritization approaches consider various optimization objectives, including fault detection capability, code coverage, risk reduction, and execution cost. In this study, we focus on the requirement-based test case prioritization problem, where the main goal is to maximize the rate of requirement coverage as early as possible. Since the possible orderings of test cases form a factorial-sized search space, this problem exhibits NP-hard characteristics, leading to the widespread use of heuristic and metaheuristic optimization techniques. However, sparse requirement traceability matrices (RTMs) introduce additional challenges, particularly due to isolated requirements and delayed coverage of critical requirement elements. To address these challenges, this study proposes a bottleneck-aware heuristic and metaheuristic framework for requirement-based test case prioritization under sparse RTMs. The main empirical contribution of the study is the deterministic AG+BH strategy, which combines Additional Greedy with the proposed Bottleneck Hunter mechanism. This strategy uses the structure of the RTM to identify test cases associated with delayed coverage, especially singleton requirements. The MH-DBO-GA component is included as a secondary metaheuristic extension based on Dragon Boat Optimization, Genetic Algorithm operators, and memetic local search. Its role is to provide additional search diversity rather than to replace the deterministic AG+BH strategy in the current APRC setting. The proposed framework is evaluated using two sparse requirement traceability matrix datasets. The results show that AG+BH provides the strongest practical deterministic trade-off on the evaluated datasets. It obtains the highest APRC on Dataset 2 and a near-best APRC on Dataset 1, where 2-Optimal gives a slightly higher APRC but requires 23 h 23 min of execution time. MH-DBO-GA does not outperform AG+BH in this setting, but it performs better than the standard stochastic metaheuristic baselines and can be considered as an exploratory extension when additional search diversity is needed. Furthermore, an ablation analysis is performed to examine the individual contributions of informed initialization, Bottleneck Hunter, and hybrid optimization components. Overall, the findings indicate that sparse RTM-based test case prioritization benefits primarily from problem-specific bottleneck-aware heuristic reasoning, while metaheuristic refinement should be interpreted as a complementary layer for more complex or future multi-objective settings. Full article

Proteases are among the most commercially important industrial enzymes, yet their large-scale production is often limited by complex and costly downstream processing. In this study, an integrated bioprocess was developed for the production, in situ recovery, and purification of an extracellular protease produced by Aspergillus sp. (SIS 22/UCP 1287) under submerged fermentation. Enzyme extraction was coupled directly to fermentation using a polyethylene glycol (PEG)–phosphate aqueous two-phase system (ATPS), aiming to enhance recovery while preserving enzymatic activity. The effects of PEG molecular weight, polymer and phosphate concentrations, and pH on enzyme partitioning were systematically investigated through a full factorial experimental design. Low-molecular-weight PEG and near-neutral pH conditions significantly favored enzyme migration to the PEG-rich phase. Under optimized conditions (15% PEG 3500, 20% phosphate, pH 7.0), the ATPS achieved a partition coefficient of 65.55, enzyme recovery of 209%, and a purification factor of 1.64. Subsequent purification by DEAE–Sephadex ion-exchange chromatography yielded a tenfold increase in specific activity, with optimal elution at 0.5 M NaCl. SDS–PAGE analysis confirmed the homogeneity of the purified protease, revealing a single band at approximately 59 kDa. Overall, the proposed integrated ATPS–chromatography strategy represents a robust, scalable, and environmentally friendly platform that significantly simplifies downstream processing while maintaining high enzyme activity, highlighting its potential for industrial and biotechnological applications. Full article

The long-term transformation of mono-industrial cities has become a critical issue in sustainable urban development, particularly where urban growth was initially shaped by state-led industrialization and strategic security concerns. This paper examines China’s Third Front Construction, a large-scale Cold War programme that relocated industrial and defence facilities to inland regions, through a paired comparison of Yuan’an and Xiaogan in Hubei Province. Focusing on Base 066 as a city-forming enterprise, the study combines archival research, local gazetteers, factory records, field investigation, historical satellite imagery, and urban morphological analysis to examine how policy shifts reshaped urban form, industrial layout, infrastructure, and public facilities. The findings show that Yuan’an developed as a dispersed, mountain-based industrial enclave structured by concealment, air defence requirements, and work unit organization, whereas Xiaogan evolved into a more compact and integrated urban industrial district after the relocation of Base 066. This transformation changed not only production space but also urban–rural relations, residential organization, and public service provision. The study demonstrates that Third Front cities should be understood as policy-produced urban systems whose later decline or integration reflects the changing relationship between security, industry, and urban sustainability. It further suggests that industrial heritage, adaptive reuse, and intercity memory networks can support the regeneration of former mono-industrial settlements. Full article

Optimizing oxygen supply and nitrogen removal in recirculating aquaculture systems (RAS) requires species-specific metabolic benchmarks. This study quantified the effects of body size and stocking density on oxygen consumption and total ammonia nitrogen (TAN) excretion in the silverside Odontesthes bonariensis under controlled RAS conditions. A 2 × 2 factorial design was used to compare small (48–140 g) and large (>140–250 g) fish stocked at low (3.2 kg m⁻³) and high (6.2 kg m⁻³) densities. Oxygen consumption was significantly influenced by both factors, with mean routine rates ranging from 146.12 ± 35.07 mg O₂ kg⁻¹ h⁻¹ in high-density large fish to 226.31 ± 50.71 mg O₂ kg⁻¹ h⁻¹ in low-density small fish. Smaller fish exhibited higher mass-specific rates, and individuals held at lower densities consumed more oxygen, consistent with allometric scaling and density-dependent metabolic suppression. In contrast, TAN excretion was unaffected by size or density, with mean values ranging from 4.59 ± 0.77 to 10.81 ± 3.76 mg TAN kg⁻¹ h⁻¹ across treatments, indicating stable protein catabolism under a uniform feeding regime. Both parameters displayed pronounced diurnal fluctuations, with postprandial peaks associated with specific dynamic action: oxygen consumption fluctuated between 61.75 and 333.03 mg O₂ kg⁻¹ h⁻¹, while TAN excretion ranged from 0 to 78.63 mg TAN kg⁻¹ h⁻¹ over 24 h cycles. These findings demonstrate that oxygen demand in O. bonariensis is strongly modulated by bioenergetic scaling and stocking density (ranging from 146 to 226 mg O₂ kg⁻¹ h⁻¹), whereas ammonia excretion (4.6–10.8 mg TAN kg⁻¹ h⁻¹) is primarily driven by dietary input. These results provide species-specific baseline benchmarks for aeration sizing and biofilter design, thereby supporting the sustainable intensification of silverside aquaculture in RAS. Full article

Physalis peruviana L. is an Andean crop of high nutritional and commercial value; however, the limited availability of uniform planting material restricts its large-scale propagation. Indole-3-butyric acid (IBA) is widely used to promote adventitious rooting, although its concentration and application methods influence the response observed throughout the different stages of root development. This study evaluated how IBA concentration and explant origin influenced rooting-related traits and early vegetative growth of in vitro-regenerated P. peruviana shoots during a 30-day ex vitro acclimatization phase. The evaluated variables included rooting percentage, root number, longest root length, root fresh and dry mass, shoot length, leaf and node number, stem diameter, shoot fresh and dry mass, leaf area, and photosynthetic pigment contents. The experiment followed a completely randomized design with a 2 × 4 factorial arrangement consisting of two explant origins (cotyledon and hypocotyl) and four IBA concentrations (0, 400, 800, and 1600 mg L⁻¹), with five biological replicates per treatment combination. All shoots formed at least one visible root, resulting in 100% rooting across all treatment combinations. IBA concentration significantly affected root fresh and dry mass and several shoot-growth traits, whereas root number and longest root length were not significantly affected. Among the concentrations tested, 800 mg L⁻¹ produced the highest root biomass and favorable responses in selected shoot-growth traits, whereas 1600 mg L⁻¹ was associated with lower values for some growth variables. Hypocotyl-derived shoots had more leaves and nodes, greater stem diameter, and higher shoot dry mass than cotyledon-derived shoots. These results indicate a concentration- and trait-dependent response to IBA and identify 800 mg L⁻¹ as the most favorable concentration among those tested for increasing root biomass and selected shoot-growth traits under the evaluated acclimatization conditions. Full article

Multi-Agent Path Finding (MAPF) is a core technology for logistics automation in factories and warehouses. Guidance-based approaches that reflect the structural properties of factory environments have been widely adopted for computational efficiency and execution feasibility. However, these approaches generally assume static environments in which predefined guidance policies remain valid. Therefore, unexpected obstacles can cause inter-robot collisions or deadlocks. To make nominal MAPF plans robust against execution uncertainty, prior studies have incorporated bounded execution delays into MAPF. A representative method is k Robust Multi-Agent Path Finding (kR-MAPF), which models allowable execution delay using a global robustness parameter k. However, when large obstacle-induced delays are represented by a single global robustness parameter, kR-MAPF imposes unnecessary conservatism and increases the search space. This increase in search space raises planning runtime and reduces path efficiency in large-scale robot fleet operation. This paper proposes a multi-robot path planning framework that updates guidance policies for each segment based on real-time obstacle information. The proposed framework identifies robots affected by obstacles and selectively replans their paths, thereby reducing unnecessary computation while maintaining path planning success and path efficiency. Simulation results in a $100\mathrm{m}\times 100\mathrm{m}$ factory environment with up to 100 robots demonstrate that the proposed framework maintains a 100% success rate under all tested conditions. Compared with kR-MAPF with different values of k, the proposed framework reduces planning runtime by approximately 35–79% and flowtime by approximately 7–24%. These results demonstrate that obstacle-aware selective replanning can improve both real-time performance and path efficiency in dynamic factory environments. The proposed framework provides a technical basis for stable large-scale multi-robot operation in structured industrial environments. Full article

Adventitious rooting remains a major constraint for the clonal propagation of Coffea arabica, limiting the large-scale multiplication of elite genotypes. This study evaluated the effects of genotype, auxin treatment, and their interaction on adventitious rooting and basal callus formation in coffee cuttings under controlled nursery conditions, while morphophysiological traits were assessed as complementary indicators of cutting performance. A completely randomized 3 × 3 factorial design was used, including three hybrids (H3, Excelencia, and Milenio) and three plant growth regulator (PGR) treatments: control, indole-3-butyric acid (IBA), and a commercial auxin formulation (RH; NAA + IBA). Rooting probability and root number were significantly affected by PGR treatment, whereas the longest root length was influenced by hybrid, PGR treatment, and their interaction. Model-estimated rooting probability increased from 4.76% in the control to 17.05% under IBA and 35.73% under RH. Similarly, the estimated number of roots per cutting increased from 0.14 in the control to 0.99 under IBA and 1.85 under RH. Although the hybrid × PGR interaction was not significant for rooting probability, the highest observed rooting percentage was recorded in H3 under RH (52.38%), followed by Milenio under RH (33.33%). For the longest root length, the strongest responses were observed under RH, particularly in Milenio (71.15 mm) and H3 (70.08 mm). Callus formation varied among treatments, but its association with rooting performance was weak and inconsistent. Morphophysiological traits provided complementary information on cutting status but were not interpreted as direct mechanistic drivers of rooting. These findings indicate that adventitious rooting in C. arabica was more closely associated with genotype-dependent responsiveness to exogenous auxin than with the extent of callus formation. However, anatomical studies are needed to determine the developmental origin of root primordia and their possible relationship with callus tissue. Full article

Grape marc (Vitis labrusca), a major by-product of the winemaking industry, is generated in large quantities and represents a promising source of bioactive compounds. This residue is particularly rich in phenolic metabolites associated with antioxidant activity. In this study, supercritical CO₂ extraction was investigated as a sustainable strategy for the recovery of bioactive compounds from Vitis labrusca grape marc. A 2⁴⁻¹ fractional factorial design was employed to evaluate the effects of temperature (30–60 °C), pressure (137.9–275.8 bar), ethanol concentration (0–10 wt%), and particle size (116–601 µm) on extraction yield, total phenolic content (TPC), and antioxidant capacity (AC). Extraction performance was strongly influenced by operating conditions, revealing a clear trade-off between recovery and selectivity. The highest extraction yield (8.3 wt%) was obtained using 10 wt% ethanol as co-solvent, whereas the highest antioxidant capacity (365.3 µmol TE/g extract) was achieved under neat CO₂ conditions. TPC values reached approximately 69 mg GAE/g extract and were significantly affected by the combined effects of temperature, particle size, and ethanol concentration. The results revealed two distinct extraction regimes: a high-recovery regime promoted by ethanol addition and a high-selectivity regime under neat CO₂ conditions. Representative extracts were further characterized by UHPLC-QTOF-MS/MS. Ethanol-modified extraction was associated with higher relative abundance and diversity of flavonoids, stilbenes, and phenolic acids, whereas neat CO₂ extraction favored lipophilic metabolites such as oxylipins and unsaturated fatty acids. Selected operating conditions were successfully reproduced at pilot scale, supporting the scalability of the process. Overall, the results demonstrate that supercritical CO₂ extraction can be tailored to recover bioactive compounds from grape marc as extracts with distinct chemical profiles and provide a viable strategy for the valorization of Vitis labrusca winemaking residues. Full article

Ventilation network feature graphs (Q-H graphs) are a key visualisation tool for mine ventilation systems, and their automated generation reduces to a combinatorial optimisation problem over independent-path permutations. Existing methods, however, exhibit three limitations: a single-dimensional evaluation criterion, inadequate nodal pressure-energy assignment, and unstable convergence in factorial-scale search spaces. This paper proposes an adaptive NSGA-II (A-NSGA-II) framework with coordinated enhancements at the evaluation, modelling, and algorithmic levels. A three-objective system that minimises split-block count, topological-spatial discrepancy, and layout fragmentation is established, together with an aggregate evaluation score (AES) for engineering decision-making; nodal pressure energies are reconstructed via the longest path on a directed acyclic graph; and topology-aware initialisation, Lagrange three-point interpolated adaptive operators, and periodic memetic local search are integrated within NSGA-II. Experiments on two mine ventilation networks (75 and 112 branches) over 30 independent trials show that A-NSGA-II consistently outperforms four benchmarks (NSGA-II, MOEA/D, SPEA2, and MOSA) in terms of split-block count, AES, and hypervolume; statistical tests confirm significant, large-effect HV advantages on the 112-branch network, while the 75-branch network shows a 56.6–71.5% reduction in HV standard deviation. Full article