Search Results (6,680)

Quality control data for construction materials is frequently exchanged as heterogeneous documents, limiting traceability and making element-level retrieval slow and error-prone. This study develops and assesses a digital methodology that integrates laboratory test results with BIM-referenced assets and delivers the integrated information via interactive 3D-enabled dashboards. The methodology comprises three stages, starting with the acquisition of data from laboratory deliverables and 3D models, followed by data standardisation and relational structuring in the software Power BI Desktop (version 2.157.879.0, Microsoft Corporation, Redmond, WA, USA), and finally the publishing of generated dashboards embedded in a web application environment. The methodology is assessed through a real case study of a railway infrastructure asset, showing how laboratory records can be accessed and interpreted within a 3D model context, while preserving stakeholder-specific visibility through access control. The proposed approach supports element-level navigation of quality control and provides a practical pathway for laboratories to centralise, filter, and communicate test results without embedding full datasets into the BIM environment. Full article

Agile outcome models commonly map Jira variables directly to a retrospective label and then explain the prediction through fragmented feature attributions; they rarely separate domain concepts, team clustering, unresolved work, and concept-label coupling. This study evaluates a domain-informed, concept-bottleneck-style explainable AI architecture for retrospective diagnosis of Agile Epic outcomes. A frozen Jira export of 10,000 unique issue-level records was linked to a pre-specified analytical cohort of 180 Epics across 14 teams. Six experts rated efficiency, effectiveness, sustainability, and contextual risk, while outcomes were recorded as Successful, Challenged, or Unsuccessful. Because the outcome labels and concept ratings were informed by the same Jira evidence, the models estimate consistency with an expert labelling procedure, rather than independent project success. Under five-fold group-aware cross-validation, the fixed-configuration flat LightGBM achieved macro-F1 = 0.864 ± 0.053 and the fixed-configuration HMXAI/CBM-style model achieved 0.843 ± 0.084. These descriptive primary scores are not a joint nested-model-selection comparison. The proposed method, therefore does, not demonstrate a performance improvement; its contribution is an inspectable diagnostic structure. Performance fell materially on the resolved-only subset (LightGBM macro-F1 = 0.645), and model-specific nested, leave-one-team-out, calibration, uncertainty, correlation, and intervention analyses further bound the claims. Concept interventions were not uniformly monotone, so the concept layer is domain-interpretable in form but not yet user-validated as actionable. The study contributes a transparent audit of when concept-level diagnosis can complement flat classification and when circularity, censoring, and shortcut learning restrict interpretation. Full article

Point clouds are widely used to represent 3D visual content in immersive media, digital twins, and autonomous systems, but acquisition, compression, transmission, and rendering can introduce visible geometry and attribute distortions. Full-reference point cloud quality assessment (FR-PCQA) aims to predict the perceptual quality of a distorted point cloud by comparing it with a reference. A reliable FR-PCQA model should consider both the perception of 3D content by the human visual system via projected views and the manifestation of quality degradation in the point cloud geometry, color, and spatial structure. In this paper, we propose a multi-view projection and 3D feature fusion model for FR-PCQA. The proposed model integrates two complementary branches. In the projection branch, DISTS is applied to multi-view renderings aligned with the reference to capture perceptual similarity, and an additional six groups of geometric and photometric fidelity features (e.g., occupancy, depth fidelity and gradient domain fidelity) are developed to describe explicit geometric and photometric differences in the projected observations. In the 3D Feature Fusion branch, PCQM measures local geometry and color degradation, while a global structural descriptor with eight groups covering point count, position, scale, spatial distribution, and density is constructed to characterize the global properties of point clouds. Finally, a gradient boosting regression tree (GBRT) regressor is employed to predict the final quality score. Extensive experimental results show that the Spearman rank order correlation coefficient (SROCC) values are 0.91537, 0.9101, and 0.9778 on the SJTU-PCQA, WPC, and ICIP2020 datasets, respectively, outperforming the existing PCQA methods. These results indicate that the proposed multi-view projection and 3D feature fusion model provides an accurate and interpretable solution for FR-PCQA. Full article

Retrieval-augmented generation has emerged as a core technological paradigm for addressing the bottlenecks of hallucinations and knowledge lag in large language models. However, many existing reviews focus on a single technical branch or vertical application scenario, making only scattered references to hardware, evaluation methods, and cross-industry empirical evidence, and lacking a systematic, end-to-end integration. This paper conducts research based on a total of 115 papers, comprising foundational literature from 1998–2019 and core RAG literature from 2020–2026, systematically cataloging end-to-end technologies and supporting solutions, establishing a quantitative hardware comparison table and comparing 11 categories of open-source and commercial APIs, constructing a two-tier, four-level standardized evaluation framework, and compiling empirical evidence and implementation challenges across eight industries from 2024 to 2026. Based on this, the paper identifies four major structural contradictions—the retrieval–creation trade-off, the geometric–semantic misalignment as a symmetry problem between representation space and semantic structure, the autonomy–reliability paradox, and evaluation blind spots—as a unified analytical framework for the five major technological strands. Finally, this paper proposes four research directions for practical implementation—differentiable joint optimization, hybrid geometric space learning, interpretable causal reasoning, and multidimensional diagnostic evaluation—providing a systematic reference for both theoretical research on RAG and its deployment in the private sector. Full article

Artificial intelligence has increasingly been applied to residential floor plan generation, producing outputs in diverse forms such as semantic raster images, graph-structured layouts, and vector polygons. However, these outputs still require substantial interpretation and reconstruction before they can enter conventional CAD and BIM workflows. This study develops a unified engineering–semantic framework for transforming heterogeneous AI-generated floor plans into editable CAD drawings and preliminary BIM information models. Three route-specific procedures—ColorPlan, TopoPlan, and PolyPlan—are established to parse and regularize the three representative input forms. The processed information is reorganized into a common engineering–semantic JSON core and subsequently mapped to native AutoCAD entities and Revit objects. The workflow was evaluated using 165 transformation-ready single-story residential samples. The effective CAD/BIM transformation rates reached 100.0%/97.7% for ColorPlan, 93.3%/85.3% for TopoPlan, and 100.0%/97.9% for PolyPlan. The results show that transformation stability is closely related to the completeness of semantic, geometric, topological, and opening information in the source representation. A controlled human–machine experiment further showed that the proposed workflow reduced CAD drafting time by approximately 85.2% and BIM modeling time by 90.0% compared with fully manual production. The study demonstrates a feasible approach for extending heterogeneous AI-generated layouts into editable and information-bearing engineering representations while substantially reducing repetitive drafting and preliminary modeling work. Full article

Since the observations of groundwater level (GWL) in Ukraine are not conducted by automated means, the regularity of the data is affected by the human factor as well as social unrest. Since 2022, this has been a full-scale war launched by the russian federation. Continuous long-term GWL observations (to 2011, sometimes until 2017) were used to reconstruct periods with missing measurements, combining autocorrelation analysis, wavelet decomposition, Mann–Kendall trend testing, and artificial neural networks (ANNs). The strongest reconstruction performance was achieved by separating GWL fluctuations into short-, medium-, and long-period components and modeling the dominant medium- and long-period structures. Compared with linear autoregressive baselines, multilayer perceptrons (MLPs) better approximated nonlinear relationships present in the historical record. At the same time, these data-driven models remain sensitive to nonstationarity and should be interpreted as predictive tools rather than causal process models. The data reconstruction study covers the transboundary basin of the Bug River, which is significant for Ukraine and Poland as a water resource. Full article

The rapid growth of large-scale astronomical surveys and advances in data-driven analysis techniques have transformed the study of late-stage stellar evolution. Modern facilities are producing large volumes of photometric, spectroscopic, and astrometric data, enabling systematic investigations of compact stellar populations across the Milky Way. Among the most important tracers of these advanced evolutionary phases are hot subdwarfs and white dwarfs: hot subdwarfs are core-helium-burning tracers of late, binary-driven stellar evolution, while white dwarfs represent the final evolutionary endpoint of low- and intermediate-mass stars. These compact objects provide important laboratories for studying stellar interiors, binary evolution, and the long-term fate of planetary systems. This paper explores how recent advances in machine learning are being applied to the detection, characterization, and, when combined with follow-up spectroscopy and modeling, the physical interpretation of hot subdwarfs and white dwarfs. By combining photometric, spectroscopic, and time-domain observations with these computational tools, it is now possible to efficiently discover rare objects, detect stellar variability, and probe the internal structure and evolutionary pathways of compact stars. Ultimately, these developments highlight the growing role of advanced algorithms in supporting the study of the final stages of stellar evolution, provided their outputs are validated against physical observables. Full article

Robotic joint motor drive systems must combine torque density and dynamic response with low mechanical impedance, safe interaction, and thermal robustness. This review treats gear ratio as a system-level design coordinate realized jointly by the motor, transmission, thermal path, sensing, and control. It synthesizes how ratio selection changes torque–speed capability, reflected inertia, losses, thermal duty, reducer nonidealities, backdrivability, and control bandwidth. The proposed spectrum uses nominal ratio as its primary coordinate while treating reducer topology, application domain, integration level, and compliance as distinct, overlapping descriptors. Mechanism-level conclusions are based on peer-reviewed studies; manufacturer specifications, open-source structures, and model-based engineering examples are identified and interpreted within narrower evidence boundaries. Representative robotic-joint cases connect these mechanisms to application demands, and an iterative framework translates the synthesis into checks on the task envelope, motor–reducer matching, thermal feasibility, transmission nonlinearity, sensing, and control. Relative to gearbox-centered reviews and task-specific motor–transmission optimization studies, this review provides a cross-domain decision map rather than a product ranking or universal predictive model. Full article

This study presents an integrated geochemical approach to distinguish between genetic and reservoir-related factors controlling oil compositional variability, evaluate reservoir compartmentalization, and reconstruct hydrocarbon migration pathways within the Nuraly field and the Akshabulak group of fields in the South Turgay Basin, Kazakhstan. The study aims to develop and validate an integrated approach combining biomarker analysis and oil fingerprinting to improve the reliability of oil genetic interpretation, assess reservoir fluid communication, and reconstruct secondary hydrocarbon migration pathways. This study analyzed 164 unique crude oil samples from the Akshabulak and Nuraly fields. Oil fingerprinting was performed on all 164 samples, including 128 samples from the Akshabulak group and 36 samples from the Nuraly field. A representative subset of 75 samples, comprising 39 Akshabulak oils and 36 Nuraly oils, was additionally analyzed for biomarkers. Oil fingerprinting was conducted using low thermal mass multidimensional gas chromatography (LTM-MD-GC), whereas biomarker analysis was performed using gas chromatography–mass spectrometry (GC–MS). Principal component analysis (PCA) and hierarchical cluster analysis were applied separately to the oil-fingerprinting and biomarker datasets. The resulting classifications were subsequently compared and integrated to distinguish source-related genetic variability from reservoir-related compositional effects, including hydrocarbon migration, oil mixing, and reservoir compartmentalization. The proposed approach is based on the complementary diagnostic capabilities of the applied geochemical methods. Biomarkers provide information on the origin of organic matter, depositional environment, and thermal maturity of the source rocks, whereas oil fingerprinting is sensitive to hydrocarbon migration processes and the degree of hydrodynamic connectivity between reservoirs. The results indicate that the investigated oils are predominantly derived from terrigenous organic matter of lacustrine origin. The Akshabulak group is characterized by genetic homogeneity of oils despite pronounced reservoir compartmentalization, whereas the Nuraly field contains at least two genetically distinct oil populations and hydrocarbon mixing zones. Regional hydrocarbon migration was reconstructed from southeast to northwest. Paleochannel sandstones were identified as high-permeability migration conduits, while tectonic faults and facies heterogeneity were recognized as the principal controls on reservoir hydrodynamic isolation. The results demonstrate that integrating biomarker analysis with oil fingerprinting provides an effective tool for distinguishing between genetic and reservoir-related controls on oil compositional variability, evaluating reservoir compartmentalization, and improving the reliability of geological and reservoir models in structurally complex petroleum systems. Full article

Background: Community-based health insurance is an important strategy for extending healthcare access and financial protection to informal-sector populations. In Tanzania, the Improved Community Health Fund (iCHF) is implemented through region-specific models that differ in benefit packages, premium arrangements, and provider participation. However, evidence on how members experience these different implementation models remains limited. This study compared members’ experiences and perceptions of CHF implementation in Dodoma and Kilimanjaro, two regions operating different iCHF models. Methods: The study used data from a cross-sectional household survey conducted among 420 CHF-enrolled members in Dodoma and Kilimanjaro in January 2018. Data were collected using a structured, interviewer-administered household questionnaire covering socio-demographic characteristics, CHF enrolment history, registration and premium-payment experiences, benefit-package perceptions, trust in CHF management and community participation, perceived healthcare service quality, and perceived benefits of membership. Data were analyzed using descriptive statistics, cross-tabulations, and Pearson’s chi-square tests to assess regional differences. Results: Members generally reported positive experiences with CHF administration. Most had used their CHF cards, renewal intentions were high, premiums were considered affordable, payment arrangements were convenient, and local CHF management was generally perceived as trustworthy. Statistically significant regional differences were observed in renewal intention, satisfaction with CHF officers, perceived healthcare service quality, and perceived household benefit (p < 0.05). Dodoma respondents reported higher renewal intention and a higher proportion of households benefiting from CHF. The distribution of healthcare-quality ratings also differed significantly (p < 0.001): Kilimanjaro had a higher proportion rating care as excellent (16.1% vs. 2.9%), whereas Dodoma had a higher proportion rating care as good (44.6% vs. 38.9%). Challenges in both regions included limited benefit coverage, additional out-of-pocket payments, long waiting times, and limited perceived advantages of CHF membership. Conclusions: Regional differences were observed in members’ experiences; however, because the regional samples differed socioeconomically and the analysis was not adjusted for potential confounders, these differences should not be attributed to the iCHF models themselves. The findings describe members’ experiences during the 2018 pilot period and should not be interpreted as a direct description of current iCHF performance. The results nevertheless highlight the importance of benefit coverage, service responsiveness, financial protection, and healthcare quality alongside efficient administration. Full article

Romanian agriculture is characterized by fragmented land structures, limited capitalization, and high labour dependence, conditions that may constrain the economic performance of family farms. This study integrates two complementary components: (i) a systematic literature review conducted within the PRISMA 2020 framework, synthesizing evidence from 34 empirical studies concerning Romanian and Central and Eastern European family farms; and (ii) an original sectoral analysis based on publicly available Eurostat data for 2016–2025, including national trends and NUTS-2 regional comparisons. The reviewed evidence indicates that market integration, production specialization, technological adoption, investment capacity, and human capital are frequently associated with stronger labour-productivity outcomes, whereas land fragmentation and surplus labour are more frequently associated with weaker performance, particularly among smaller farms. The Eurostat analysis identifies an overall upward, although fluctuating, trajectory in nominal agricultural output and gross value added per AWU. The polynomial models provide a descriptive fit to these temporal patterns but do not identify their causal determinants. Overall, the findings support a size-differentiated interpretation of labour-productivity patterns and underline the relevance of targeted policies addressing the different structural conditions of Romanian family farms. Full article

Background/Objectives: Stingless bees (tribe Meliponini) are the most species-rich group of eusocial bees and critical pollinators across tropical ecosystems. Over the past seven decades, a growing number of studies have addressed their genetics and genomics, but coverage of the tribe remains taxonomically and geographically uneven. Here, we present a systematic evidence map and bibliometric science-mapping synthesis of this literature. Methods: We searched Scopus and Web of Science, retained 410 peer-reviewed articles published between 1950 and 2026, and applied structural topic modeling (STM) to characterize the thematic, temporal, taxonomic, and biogeographic structure of the corpus. Results: STM with K = 10 topics identified ten research themes, ranging from classical marker-based genetics and cytogenetics to phylogenomics, mitochondrial genomics, microbiome, and functional genomics. The estimated prevalence of phylogenomics/taxonomy and mitogenomics increased most steeply in recent years, a publication pattern consistent with—although not proof of—a shift toward genome-scale comparative approaches. Topic prevalence differed across biogeographic regions and subtribes: Neotropical and Meliponina-dominated studies were concentrated in population genetics, cytogenetics, and gene expression, whereas Indo-Australasian and Hypotrigonina-associated studies showed higher relative representation of DNA barcoding, mitogenomics, and microbiome research. Taxonomic representation was strongly skewed toward a few genera, with Melipona alone accounting for 43% of the corpus and most lineages across the Meliponini phylogeny remaining poorly studied. Conclusions: The principal contribution is a reproducible quantitative map of publication patterns; proposed research and conservation priorities are evidence-informed interpretations rather than direct outputs of STM. Full article

Recent advances in digital technologies are transforming the production and visualization of 3D models for Augmented Reality (AR), Virtual Reality (VR), and cultural heritage reconstruction. Within this evolving context, ensuring scientifically grounded, transparent, and interpretable reconstruction processes remains essential, particularly for the hypothetical reconstruction of lost or unbuilt architecture. This article discusses the theoretical framework defined by the Critical Digital Model (CDM) and the Scientific Reference Model (SRM), both of which aim to define the 3D model as a scientific product generated through a transparent and falsifiable research process. The proposed approach integrates a structured reconstruction methodology based on source analysis, semantic segmentation, and iterative validation, distinguishing between Raw and Informative Models and applying these principles to selected case studies. Attention is devoted to visualization as an integral component of the reconstruction process. Rather than serving solely as a presentation tool, visualization is examined as a means of analysis, interpretation, and communication of uncertainty. Different visualization strategies—including photorealistic, non-photorealistic, uncertainty-driven, and diplomatic representations—are assessed together with XR visualization modalities, from static images and spherical panoramas to fully interactive VR environments. The results demonstrate how immersive and methodologically grounded visualization approaches can enhance both scholarly investigation and public dissemination, supporting informed choices according to specific research and communication objectives. Full article

Afforestation can contribute to climate-change mitigation when tree establishment is matched to ecological context and sustained by long-term management, but its net climatic effect is not uniformly cooling. Existing syntheses often emphasize carbon sequestration while treating biophysical, hydrological, disturbance, and socioeconomic evidence separately. Here, we provide a structured integrative review. We distinguish afforestation from reforestation, natural regeneration, forest restoration, and improved management. We explicitly assess evidence from global modeling, remote sensing, meta-analyses, long-term observations, and regional case studies. Global forests cover about 4.14 billion ha in 2025, while annual net forest loss remained about 4.12 million ha yr⁻¹ during 2015–2025. Global forests were a sink of about 3.5 ± 0.4 Pg C yr⁻¹ in the 2010s, but this existing-forest sink should not be interpreted as an afforestation-specific removal rate. Humid tropical and subtropical regions generally have the greatest potential for net climatic cooling. In contrast, afforestation at snow-covered high latitudes may cause substantial albedo-driven warming, while water-limited regions require careful species selection and conservative planting densities. Soil carbon gains are most consistent on former croplands and other low-carbon degraded lands, but responses on carbon-rich grasslands are highly variable. Long-term benefits further depend on disturbance resilience, permanence, land competition, financing, and credible monitoring. Additionally, we identify five priorities for the future: climate-smart adaptive silviculture, digital forestry with field-calibrated uncertainty, permanence and disturbance-risk accounting, sustainable forest bioeconomy, and integrated international governance and finance. Full article

Arid mountain–oasis–desert regions exhibit strong terrain and surface-cover heterogeneity that is difficult to represent using conventional land-cover classification schemes. This study developed an ecosystem-oriented hierarchical framework for the Kashi region by combining a terrain-constrained Mountain extraction method with Automatic Deep Forest Shrinkage model (ADeFS). Nine ecosystem elements were defined: Mountain, Water, Forest, Cropland, Lake, Grassland, Desert, Ice, and Human. Mountain was first delineated as an independent physiographic element using a locally derived baseline surface, relative relief, slope, and topographic position, thereby reducing semantic overlap between terrain units and spectrally similar surface-cover classes. ADeFS was then adapted to classify the seven non-mountain classes, and Lake was subsequently separated from the unified Water class through visual interpretation. The results show that ADeFS achieved the highest accuracy, with an overall accuracy of 88.7% and a Kappa coefficient of 0.868. Independent field validation of the 2026 map yielded an overall accuracy of 86.2% and a Kappa coefficient of 0.825. From 2015 to 2026, the mountain-oasis-desert structure remained broadly stable, while Desert and Ice decreased and Forest, Grassland, and Cropland expanded. Ecosystem-element transitions were concentrated before 2021 and weakened thereafter. Landscape metrics showed that Desert remained the dominant matrix, Grassland had the highest patch density and edge density, and Cropland became increasingly aggregated within oasis agricultural areas. The framework provides an ecologically interpretable approach for ecosystem-element mapping and long-term monitoring in arid heterogeneous regions. Full article