Search Results (183)

In the era of urban stock development, the regeneration of historic districts must abandon the misguided approach of large-scale, sweeping transformations and shift toward a micro-regeneration model characterized by small-scale, precise, and incremental interventions. However, as urban renewal enters this stock-based phase, the issues of “physical dissonance” and “cultural discontinuity” in the heritage landscapes of historic districts are becoming increasingly pronounced. To solve this problem, this paper aims to identify the heritage landscape genes of historical districts, explore the characteristics of historical districts, provide operational targets for the micro-renewal of historical districts, guide the implementation of micro-regeneration policies of historical districts, and then improve the quality of historical district heritage landscapes. Taking the Chinese Baroque Block in Harbin as an example, this paper proposes a genetic recognition method for the heritage landscape of historical districts based on the spatial translation of historical information, spatial topology analysis, an improved U-Net deep learning model, and text mining theme analysis. The micro-regeneration path of historical blocks of “gene identification-feature mining-targeted operation-quality improvement” is proposed. The micro-regeneration countermeasures of “gene replacement and texture repair in open space, gene repair and targeted acupuncture in street and alley, gene embedding and catalyst adjustment in courtyard layout, gene recombination and embroidery treatment of architectural style, and retrospective and contextual narrative of intangible genes” are formulated. The heritage landscape gene of historical districts is conducive to the refined control of the characteristics and quality of historical districts and provides new ideas for the micro-regeneration of historical districts in the stock era. Full article

The eukaryotic translation initiation factor 4E (eIF4E) family plays a dual role in plants, regulating cap-dependent protein synthesis and mediating susceptibility to viruses in the family Potyviridae. In cowpea (Vigna unguiculata (L.) Walp.), an economically important legume cultivated worldwide, the structural determinants of these isoforms remain largely unexplored. This study characterizes the genomic organization, evolutionary history, and conformational dynamics of eIF4E, eIF(iso)4E, and nCBP in cowpea using a multi-omics approach. Genome mining identified three paralogous genes located on chromosomes 4, 6, and 7, showing high synteny with Phaseolus vulgaris. Phylogenetic analysis confirmed nCBP as the ancestral Class I lineage, distinct from the Class II eIF4E and eIF(iso)4E clades. Theoretical models for the isoforms were generated and subsequently validated by molecular dynamics simulations, revealing that while all isoforms preserve the canonical tertiary architecture and an electropositive cap-binding pocket, eIF(iso)4E exhibits superior structural compactness and hydrogen-bond stability. These biophysical features highlight their role as a stable anchor for viral VPg proteins. By elucidating the atomic-level landscape of these factors, we provide a robust structural framework to guide allele mining and genome-editing strategies aiming to engineer virus-resistant cowpea cultivars without compromising agronomic performance. Full article

Plant growth-promoting microorganisms (PGPMs) and microbial biocontrol agents have emerged as key tools for improving crop productivity while maintaining environmental sustainability. However, central questions remain regarding which factors determine their consistent field performance and how these factors interact under real agronomic conditions. Previous research has demonstrated that PGPMs enhance nutrient acquisition, regulate phytohormone balance, improve stress tolerance, and suppress plant pathogens through diverse biochemical and ecological mechanisms. Advances in omics technologies, genome mining, and synthetic microbial communities have further expanded understanding of their functional potential. Nevertheless, many studies rely on laboratory-scale experiments or short-term trials, with limited multi-season and cross-regional validation. This gap contributes to inconsistent field outcomes and restricts large-scale agricultural adoption. Long-term multi-season validation and reproducibility assessment remain essential priorities for improving reliability of microbial agricultural products. This review synthesizes recent advances in PGPM-based biofertilizers and microbial biocontrol technologies, critically examining their mechanisms of action, scalability constraints, formulation challenges, and regulatory limitations. It identifies major translational barriers, including context dependency, mechanistic uncertainties, reproducibility gaps, and insufficient systems-level integration. Full article

Underground mining environments are complex and hazardous operations where emergencies continue to happen. Underground mine emergencies require rapid, high-stakes decision-making under conditions of uncertainty, stress, and limited visibility. Conventional mine emergency training largely relies on instruction-based approaches which provide insufficient exposure to the cognitive and behavioral demands of real underground emergency situations. There has been an identified need to train miners for knowledge, skills, abilities, and other characteristics (KSAOs). This study proposes an Adaptive Immersive Training Framework (AITF), a cognitively grounded architecture that integrates cognitive task analysis (CTA), KSAOs, and situational awareness assessment for miner self-escape training and readiness. The AITF aligns NIOSH-identified self-escape competencies with immersive training scenarios designed to assess and develop cognitive readiness and decision-making. CTA of historical mine accidents is introduced as a foundational design method for translating accident investigation findings into simulation scenarios and performance metrics. A CTA of 2006 Darby Mine No. 1 explosion is presented as a proof of concept. The proposed framework supports individualized assessment, iterative scenario refinement, and data-driven feedback. The AITF advances miner training toward cognitive preparedness during mine emergencies and provides a foundation for future training systems that leverage digital tools, digital twins, and artificial intelligence for the mines of the future. Full article

Accurate registration of multi-temporal LiDAR point clouds is essential for reliable monitoring of mining subsidence. Systematic errors in point clouds acquired at different times can arise from GNSS/INS positioning drift, sensor calibration bias, and differences in observation geometry. These errors typically manifest as global reference shifts or gradual distortions. When such errors are superimposed on real terrain changes, they can mask subsidence signals and introduce observational pseudo-differences, thereby increasing the difficulty of separating actual subsidence from artifacts. To address this issue, this study proposes Robust-Registration-Based Systematic Error Correction for Time-Series Point Clouds (RR-SEC), which establishes a consistent reference framework across epochs. The method does not assume that stable areas remain strictly unchanged. Instead, it identifies regions whose local change patterns are more temporally consistent using an information entropy analysis of multi-temporal differences. Under complex terrain, the method selects points with lower difference entropy as stable control points and uses them to constrain the registration process. It then performs Generalized Iterative Closest Point (GICP) rigid registration under these constraints to estimate the overall three-dimensional translation and rotation between point clouds from different periods. The estimated transformation is applied to the entire point cloud to correct inter-epoch reference mismatches and unify the coordinate reference across all epochs. Comprehensive validation using simulated complex terrain data containing rigid reference biases and non-rigid deformations, as well as UAV LiDAR data collected from the MuduChaideng Coal Mine, shows that, compared with the baseline GICP method, RR-SEC reduces alignment errors. It decreases the mean residual in stable areas by approximately 85%. The subsidence values computed from the corrected point clouds are more consistent with measured values, and the spatial deformation patterns are easier to interpret. RR-SEC demonstrates robust performance and can serve as a practical approach to improve the accuracy of deformation monitoring in mining areas and potentially other geoscientific applications. Full article

The escalating complexity of airport surface operations challenges traditional risk quantification methods. Conventional linear models often fail to capture the non-linear interactions within sociotechnical systems. While hybrid System-Theoretic Process Analysis (STPA) and Bayesian Network (BN) models provide an alternative, existing integrations are frequently constrained by ad hoc structural translations and rare-event data sparsity. To address these methodological limitations, this study proposes an enhanced STPA-BN framework. A formalized mapping mechanism (M1–M4) translates qualitative STPA scenarios into a BN topology to quantify non-linear causal dependencies across environmental precursors, operator cognitive states, unsafe control actions, and systemic hazards. Parameterization is achieved via a logic-guided strategy, fusing historical incident data mining with deterministic physical constraints to correct rare-event probabilities. The framework is validated through a reconstruction of the 2023 Austin–Bergstrom runway incursion incident. Results indicate that under low visibility and degraded surveillance, incursion probability escalates to 86%. Sensitivity analysis reveals that while restoring surveillance infrastructure reduces collision risk by ~13%, communication compliance improvements prove insufficient in sensory-deprived environments. These findings quantitatively demonstrate that administrative controls cannot substitute for robust engineering safeguards in complex operations. Full article

Discretisation is a processing step often included in the preliminary data preparation. Typically, when the input features have continuous domains and their discrete forms are needed, all are translated into categorical type at the same time, before data mining takes place. However, proceeding this way is not always the most advantageous to performance. The paper presents results from the research where the discretisation transformations were carried out sequentially forward for variables, and their selection was based on their values and also importance of the attributes estimated by the constructed rankings. The experiments were executed on the datasets from the area of stylometric analysis of texts, the application domain focused on recognising authorship based on individual characteristics of writing styles. For the selected data mining techniques, the performance was studied in the context of transformed features. The observed trends indicate that along with enhanced understanding of the nature of the data, partial discretisation of feature sets could bring higher accuracy than transformation of entire input domain, showing the merits of the described research methodology. Full article

Low temperature and drought are among the most pervasive abiotic stresses limiting crop productivity worldwide, and their frequent co-occurrence or alternation imposes compounded constraints on agricultural sustainability. Increasing evidence supports cross-tolerance, whereby exposure to one stress enhances resistance to another, as an emergent property of shared signaling networks and integrative regulatory layers. In this review, we summarize recent advances in understanding cold–drought cross-talk, from early stress perception and secondary messengers to hormonal coordination via abscisic acid, transcriptional reprogramming centered on dehydration responsive element binding protein/C repeat binding factor (DREB/CBF) modules, and longer-term regulatory memory mediated by chromatin remodeling and biomolecular condensates. Importantly, we further discuss how these mechanistic insights can be translated into precision breeding strategies, including genome editing, allele mining, and backcross-assisted introgression, to accelerate the development of crop varieties with stable multi-stress tolerance. Finally, we highlight future directions for integrating multi-omics, high-throughput phenotyping, and data-driven approaches to enable efficient molecular design breeding for complex stress environments. Full article

Background: Noninvasive biomarkers for the detection and monitoring of glioblastoma (GBM) are needed to improve clinical outcomes for patients. The objective of this pilot study was to evaluate the expression of a panel of 48 pre-selected microRNAs (miRNAs) in plasma specimens from GBM patients versus healthy controls to identify candidate miRNA biomarkers for noninvasive diagnosis of GBM. Methods: Selection of candidate miRNA biomarkers was based on a comprehensive literature review and data mining. RNA was extracted from plasma samples obtained prior to resection from patients with GBM (n = 30) and age- and sex-matched healthy controls (n = 30), as well as from matched FFPE GBM tissue samples when available (n = 3). Expression levels of 48 miRNAs were assessed in all samples, and expression data was processed using proprietary software to generate potential biomarkers and train linear classifiers. Results: Overall miRNA expression patterns were similar between matched plasma and FFPE tumor tissues in patients with GBM. miRNA levels were examined in pairs to determine the ratio between two miRNAs, which served to normalize the data. The top five miRNA pairs for distinguishing between GBM and healthy control plasma included miR-17-5p/miR-19b-3p (AUC 0.93, 95% CI = 0.870, 0.970), miR-20a-5p/miR-19b-3p (AUC 0.93, 95% CI = 0.870, 0.970), miR-93-5p/miR-92a-3p (AUC 0.92, 95% CI = 0.875, 0.965), miR-17-5p/miR-92a-3p (AUC 0.91, 95% CI = 0.865, 0.955), and miR-93-5p/miR-19b-3p (AUC 0.90, 95% CI = 0.850, 0.950). For the development of a multi-biomarker combination classifier consisting of up to three miRNA pair biomarkers, miRNA pairs with an AUC ≥ 0.8 were selected to build equal-weight linear classifiers. All possible combinations of three high-performing miRNA pairs were tested across the 60 samples. The top classifier (miR-20a-5p/miR-451a, miR-582-5p/miR-222-3p, and miR-17-5p/miR-222-3p) achieved an AUC value of 0.992, sensitivity of 0.93, specificity of 1, and accuracy of 0.97. Conclusions: These findings support the continued development of a plasma-based miRNA molecular diagnostic approach for the detection of GBM. The strong discriminatory performance observed in this study, including high AUC values, highlights the potential of circulating miRNA signatures as a minimally invasive diagnostic tool. As a pilot analysis, this work establishes a foundation for future prospective studies in larger, independent cohorts—including relevant disease control populations—to further define clinical performance, specificity, and utility in diagnostic and monitoring settings. Collectively, these results represent an important step toward the translation of plasma-based miRNA profiling into clinical application for GBM. Full article

Bombiscardovia sp. JNUCC 75 (=CH12) was isolated from the flowers of Prunus yedoensis on Jeju Island, representing one of the few known flower-associated members of the Bombiscardovia asteroides group. Whole-genome sequencing revealed a compact 2.28 Mb genome and a functional gene profile enriched in translation, amino-acid metabolism, and DNA repair. Although the strain contains a pseudogene content comparable to other B. asteroides-group members, the overall genomic architecture—together with the presence of stress-response and polyprenyl/terpenoid biosynthetic pathways—suggests adaptation to oxygen-variable, phenolic-rich, and UV-exposed floral environments. Comparative genomic analyses (OrthoANIu 97.16%; dDDH 72.7%) demonstrated that JNUCC 75 is closely related to B. polysaccharolytica yet forms a genetically distinct lineage within the group. Genome mining uncovered two previously unreported terpenoid/polyprenyl biosynthetic gene clusters, indicating a novel isoprenoid-derived metabolic repertoire with potential roles in membrane stabilization and oxidative-stress defense. These genomic features collectively position JNUCC 75 as a bridge between gut-associated and environmental bifidobacteria and highlight its potential as a promising microbial resource for postbiotic, antioxidant, and skin-barrier-enhancing applications. This study expands the ecological range of bifidobacteria and provides a genomic framework for evaluating flower-derived Bombiscardovia strains in cosmeceutical and functional food innovation. Full article

This article presents a prospective analysis of the corn agro-industrial chain in Colombia up until 2035, using a mixed-methods approach that integrates technological surveillance, two rounds of the Delphi method, S-curve analysis, and patent–publication matrices and quadrants. Text-mining analysis was conducted using VantagePoint^® v15.1 software, enabling the generation of multiple analytical outputs, including cluster maps, co-occurrence networks, and relational matrices. The study examines the dynamics of scientific and technological production related to the utilization of corn by-products and residues over the period 2003–2025. A total of 30 Delphi responses were collected from experts representing academia, industry, and government institutions in Argentina, Ecuador, Portugal, and Colombia. Based on expert consensus, the Delphi process identified 23 priority topics and 40 additional topics for discussion. Six priority themes were highlighted: (i) antioxidant and antimicrobial packaging derived from bioactive compounds extracted from corn by-products; (ii) bioethanol production; (iii) biodegradable straw manufactured from basket fibers; (iv) bioactive extracts for application in anti-aging cosmetic formulations; (v) modified biochar for the adsorption of ammonium and phosphate ions from aqueous systems; and (vi) the use of corn stover to enhance soil nitrogen content and grain yield. Finally, patent-based S-curve analysis and patent–publication matrices revealed notable asymmetries between scientific knowledge production and patenting activity, underscoring structural gaps in the translation of research into technological innovation within the corn agro-industrial sector. Full article

With rapid urbanization, urban renewal has become increasingly important. Traditional research has relied on expert assessments and objective indicators, lacking scalable frameworks that effectively translate street-level conditions into actionable renewal strategies. This study proposes a Vision–Language Model (VLM)-based framework to address these gaps, using the Hongshan Central District of Urumqi, China, as a case study. Specifically, we collected 4215 street-view images (SVIs) and employed VLMs to assess six perceptual dimensions (i.e., safety, liveliness, beauty, wealthiness, depressiveness, and boringness), together with textual descriptions. The best-performing model, selected by a 500-respondent perception survey validation, was used to conduct spatial pattern and text mining analyses to inform targeted urban renewal strategies. Results show that (1) VLMs have a high consistency with humans in evaluating the spatial perception of six dimensions; (2) spatial clustering analysis successfully delineated four distinct renewal priority tiers, confirming the method’s capability in translating perceptual data into actionable spatial strategies; and (3) textual mining of the VLM’s rationales revealed that areas with lower perceptual scores are predominantly characterized by deficiencies in foundational infrastructure and street-level order, thereby providing explanatory evidence directly linked to the generated renewal priorities. This study provides a generative artificial intelligence (GAI)-driven and interpretable evaluation framework for urban renewal decision-making, facilitating precision-oriented and intelligent urban regeneration. Full article

Under rapid urbanization and ecological transformation, balancing authenticity preservation with adaptive reuse presents a major challenge for industrial heritage landscapes. This study investigates the Dagushan Iron Mine in Anshan, China’s first large-scale open-pit iron mine and once the deepest in Asia, which is currently undergoing ecological backfilling that threatens its core landscape morphology and spatial integrity. Using a mixed-method approach combining archival research, spatial documentation, qualitative interviews, and expert evaluation through the Analytic Hierarchy Process (AHP), we construct a cross-validated evidence chain to examine how evidence-based industrial landscape heritage values can inform low-intervention digital narrative strategies for off-site learning. This study contributes theoretically by reframing authenticity and integrity under ecological transition as the traceability and interpretability of landscape evidence, rather than material survival alone. Evaluation involving key stakeholders reveals a value hierarchy in which historical value ranks highest, followed by social and cultural values, while scientific–technological and ecological–environmental values occupy the mid-tier. Guided by these weights, we develop a four-layer value-to-narrative translation framework and an animation design pathway that supports curriculum-aligned learning for off-site students. This study establishes an operational link between evidence chain construction, value weighting, and digital storytelling translation, offering a transferable workflow for industrial heritage landscapes undergoing ecological restoration, including sites with World Heritage potential or status. Full article

Understanding complex change scenes is a crucial challenge in remote sensing field. Remote sensing image change captioning (RSICC) task has emerged as a promising approach to translate appeared changes between bi-temporal remote sensing images into textual descriptions, enabling users to make accurate decisions. Current RSICC methods frequently encounter difficulties in consistency for contextual awareness and semantic prior guidance. Therefore, this study explores difference semantic prior guidance network to reason context-rich sentence for capturing appeared vision changes. Specifically, the context-aware difference module is introduced to guarantee the consistency of unchanged/changed context features, strengthening multi-level changed information to improve the ability of semantic change feature representation. Moreover, to effectively mine higher-level cognition ability to reason salient/weak changes, we employ difference comprehending with shallow change information to realize semantic change knowledge learning. In addition, the designed parallel cross refined attention in Transformer decoder can balance vision difference and semantic knowledge for implicit knowledge distilling, enabling fine-grained perception changes of semantic details and reducing pseudochanges. Compared with advanced algorithms on the LEVIR-CC and Dubai-CC datasets, experimental results validate the outstanding performance of the designed model in RSICC tasks. Notably, on the LEVIR-CC dataset, it reaches a CIDEr score of 143.34%, representing a 3.11% improvement over the most competitive SAT-cap. Full article

Equipped with one degree of freedom in one-dimensional translation of the base, a mobile dual-arm robot (MDAR) is proposed in this paper, and the two arms and the base move simultaneously. As a result, the motion of the base has a significant influence on the motion of both end-effectors at the same time, and the relative positions of the two end-effectors change all the time. Therefore, this paper focuses on the main issues related to the presented MDAR in two key areas: the relative dynamics and relative force/position hybrid control. First, based on the D-H parametric method, the relative kinematics of the proposed MDAR is established, and the relative Jacobian matrix of the robot is derived. Secondly, the dynamic model of the proposed MDAR is constructed using the Lagrangian method. Furthermore, a closed-loop control strategy for relative force/position hybrid control of the MDAR based on the relative dynamics is proposed to enable the two end-effectors of the MDAR to track the planned trajectory accurately. Finally, a simulation is carried out on a dual-arm cutting robot (DACR) for a coal mine to prove the effectiveness of the proposed relative dynamics and the proposed relative force/position hybrid control law in terms of the absolute error (AE) and root mean square error (RMSE). The results show that the proposed relative dynamic model and relative force/position hybrid control can significantly reduce error of the DACR, effectively improve the adaptability and operation accuracy of the system to complex environment, and verify the feasibility and superiority of the method in practical application. Full article