Search Results (1,086)

Underground mining robots are increasingly modeled for planning, operator training, and digital-twin workflows, where reliable actuator-level kinematics is needed to reduce hazardous in situ trials. Unlike typical open-chain industrial manipulators, representative mining machines are often linear-actuator-driven closed-chain mechanisms with planar four-bar linkages, making reusable kinematic modeling and real-time FK/IK solving challenging. We present MineRobot, an actuator-centered framework for modeling and solving the kinematics of this representative mechanism class. MineRobot introduces the Mining Robot Description Format (MRDF), a domain-specific representation that parameterizes mining-robot kinematics with native semantics for actuators and loop closures. It then contracts planar four-bar substructures into generalized joints and extracts, for each actuator, an Independent Topologically Equivalent Path (ITEP) classified into four canonical types. Based on this decomposition, per-type solvers are composed into a sequential forward-kinematics (FK) pipeline, while inverse kinematics (IK) is formulated as a bound-constrained actuator-length optimization solved by a Gauss–Seidel-style update scheme. By converting coupled closed-chain kinematics into small topology-aware solves, MineRobot reduces robot-specific hand derivations and supports efficient repeated FK/IK computation without treating each query as a full coupled constraint-solving problem. Experiments on representative underground mining robots demonstrate real-time FK performance and robust IK convergence within the tested operating ranges, supporting the use of MineRobot as an actuator-centered kinematic layer for planning, training, and digital-twin workflows. Full article

Accurate distance measurement in outdoor environments remains a challenging problem for mobile augmented reality (AR) systems due to sensor noise, environmental variability, and the limitations of single-modality approaches. Existing consumer AR solutions often prioritize usability over metric robustness, leading to performance degradation in large-scale or heterogeneous outdoor scenarios. This work presents EfMAR, an adaptive framework for outdoor mobile AR-based geospatial measurements that integrates multiple sensing modalities through a structured sensor fusion architecture. EfMAR combines visual SLAM, inertial sensing, depth information, and global positioning cues to improve robustness and consistency in distance estimation across diverse outdoor conditions. Beyond implementation, the framework formalizes a reusable architectural model for adaptive multi-sensor fusion, supporting reproducibility and future comparative research. A dedicated dataset is described, comprising 584 unique real-world evaluation instances collected across representative outdoor scenarios. External literature-derived data were utilized strictly as calibration baselines for modeled operational degradation profiles, maintaining methodological transparency. Performance evaluation focuses on analyzing relative behavior, stability, and variability across sensing approaches rather than establishing absolute accuracy benchmarks. Comparative results across multiple distance ranges and environments indicate that hybrid sensor fusion strategies exhibit more stable and consistent performance trends compared to single-modality solutions, particularly in challenging urban contexts. Dispersion analysis further highlights the influence of environmental factors such as lighting conditions and spatial scale on measurement variability. Overall, the results position EfMAR as a flexible and adaptive framework designed to enhance robustness in outdoor AR-based geospatial measurement tasks. By emphasizing consistency, transparency, and architectural generalization, this work contributes a practical foundation for future research and development in mobile AR sensing for real-world outdoor applications. Full article

This paper proposes a novel fault-identification method for micro-coils in relays with forcibly guided contacts, a type of electromechanical elementary component (EMEC), combining a composite analytical model, a 2D thermal model, and a 1D-CNN. A low-order thermal circuit with one central node and four boundary nodes is established, while a two-dimensional anisotropic Poisson equation is used as a high-order calibration model. The two models are coupled through iterative correction of reusable thermal resistances. For thermal aging, enamel-film delamination, and inter-turn short-circuit faults, thermal-conductivity attenuation, asymmetric branch-resistance perturbation, and localized abnormal heat-source injection are introduced to generate physically constrained temperature sequences. Orthogonal centerline temperature distributions are extracted as one-dimensional feature vectors for 1D-CNN classification. Simulation results show that the hybrid model has an error of approximately 1.7% compared with finite-element results, and the trained 1D-CNN achieves 98.13% accuracy on 160 test samples. Experimental reconstruction and deep-feature visualization further verify its ability to distinguish normal, aging, delamination, and local short-circuit states. Full article

Graphite nozzles remain the dominant choice for small hybrid and solid rocket motors operating on laboratory and university budgets, owing to their low cost, ease of machining, and rapid turnaround during iterative design campaigns. These same programs, however, must contend with the fact that graphite erodes through coupled thermochemical and mechanical mechanisms when exposed to the oxidizing species generated by high-energy propellant combustion, and the resulting throat-area growth fundamentally alters the time histories of chamber pressure, thrust, and delivered specific impulse. This paper presents a nozzle-erosion reconstruction model that extracts the time-resolved throat area from coupled thrust and chamber-pressure measurements using the thrust coefficient relationship, scales the reconstructed area history against pre- and post-test throat measurements, identifies the onset and rate of erosion, and accounts for variable sensor lag between the thrust-stand and pressure-transducer signal chains. The model is exercised on two complementary sets of laboratory-scale GOX/ABS hybrid hot-fire data that together span roughly two orders of magnitude in total throat-area change and peak chamber pressures from 0.5 to 3.4 MPa: a controlled three-operating-point campaign conducted in support of the NASA Plume-Surface Interaction (PSI) program, and a set of higher-pressure firings from the laboratory development series in which the technique was matured. Reconstructed erosion-onset times, erosion rates, and total throat-diameter change are reported for each firing, the reconstruction accuracy is characterized as a function of erosion magnitude. A correlation of graphite erosion with chamber pressure is examined across the combined envelope. The results demonstrate the robustness of the reconstruction technique and provide a reusable framework for post-test reconstruction of transient nozzle geometry in rocket-engine ground testing. Full article

Agile teams often struggle to translate business requirements into maintainable, high-quality software due to the persistent ambiguity in the roles and relationships of behavior-driven development (BDD), Acceptance Test-driven Development (ATDD), and Test-driven Development (TDD). These approaches are frequently misunderstood, inconsistently applied, and only loosely connected within a unified delivery lifecycle. This article introduces BLOW (Behavior-Driven Development in a Layered Organization of Work-Centers), a systematic approach that establishes BDD as the coordinating methodology between ATDD (business-focused) and TDD (technology-focused). BLOW structures scenario-driven development across layered domains of accountability with clearly defined roles and responsibilities, organizing delivery through nested work-centers that transform user stories into executable specifications and production code. This approach integrates two complementary collaboration practices: the Three Amigos for discovering and formulating business scenarios, and the proposed Technical Three Amigos for linking those scenarios to Technical Domain Contexts, identifying required Enablers, and deriving technical scenarios when additional architectural support is needed. The proposed operating model emphasizes observability through executable scenarios as first-class artifacts, introducing native, test-anchored metrics that support reasoning about progress, technical effort, and value delivery within scenario-driven development. An exploratory longitudinal case study, consisting of a single-sprint proof of concept followed by an 18-month production deployment, reports patterns in which technical enablement precedes business value delivery and reusable infrastructure supports sustained growth of business scenarios over time. The findings also indicate that changes in the applied operating model are associated with measurable shifts in scenario evolution and internal quality indicators. Overall, BLOW provides a governance-compatible, end-to-end approach for organizing scenario driven development and improving alignment between stakeholder intent and technical implementation in complex software systems. Full article

Lead (Pb²⁺) and cadmium (Cd²⁺) are among the most hazardous heavy metal pollutants in wastewater owing to their high toxicity, environmental persistence, and detrimental impacts on human health and aquatic ecosystems. In this study, a novel ternary magnetic composite, SnFe₂O₄/activated carbon/polypyrrole (SnFe₂O₄/AC/PPy), was effectively synthesized and tested as an effective adsorbent in the removal of Pb²⁺ and Cd²⁺ from aqueous water. The composite was prepared by depositing spinel SnFe₂O₄ nanoparticles on activated carbon, followed by in situ polymerization of polypyrrole to enhance surface functionality and adsorption affinity. The successful fabrication of the porous SnFe₂O₄/AC/PPy hybrid composite was confirmed through FTIR, XRD, SEM–EDS, BET, XPS, and VSM characterization. The composite demonstrated a relatively high surface area (352.3 m²/g) and adequate magnetic responsiveness (12.33 emu/g), ensuring facile magnetic separation following wastewater treatment. Batch adsorption experiments showed great removal efficiency of 95.02 and 92.48% for Pb²⁺ and Cd²⁺ ions, respectively, at optimum conditions. The adsorption equilibrium data followed the Langmuir isotherm model with maximum adsorption capacities of 187.07 mg/g for Pb²⁺ and 96.45 mg/g for Cd²⁺ ions, which were attributed to monolayer adsorption on homogenous active sites. The kinetic and isothermal model indicated that the adsorption process was controlled by the combination of physical and chemical interactions. Thermodynamic parameters showed negative Gibbs free energy and enthalpy changes (ΔH° = −49.74 kJ/mol for Pb²⁺ and −38.82 kJ/mol for Cd²⁺ ions), confirming the spontaneous and exothermic nature of adsorption. Furthermore, the increasingly negative ΔG° values at lower temperatures indicated that the adsorption was thermodynamically more favorable under cooler conditions. According to the regeneration studies, the composite maintained a high removal efficiency after five consecutive cycles. In general, SnFe₂O₄/AC/PPy composite has good potential as a stable, reusable, and high-performance adsorbent to treat heavy metal wastewater. Full article

Cost overrun remains a pervasive issue in building construction projects, yet most predictive studies operate within a single data domain, ignoring the systemic interactions across project, schedule, resource, quality, and safety dimensions. This study quantifies the incremental predictive value of integrating these five construction data domains and identifies the cross-domain interaction patterns that explain prediction accuracy. As a simulation-based methodological study, an integrated dataset of 100,000 records was synthesised with theory-grounded causal structures derived from the construction management literature; no real project data were used. Gradient Boosting (GB), Random Forest (RF), and Linear Regression were evaluated on an 80/20 hold-out test split, with robustness verified through alternative domain orderings and hyperparameter sensitivity. SHAP analysis, including exact interaction values, was used to interpret feature importance and cross-domain synergies. The full five-domain GB model achieved R² ≈ 0.97 and MAPE ≈ 6%, a 220% relative R² improvement over the Project-domain baseline (R² rising from 0.305 to 0.975), robust across three ordering schemes. Schedule and Quality contributed the largest marginal gains (ΔR² = +0.312 and +0.255), whereas Resource integration yielded approximately one-thirty-first of Schedule’s return. Because the dataset is synthetic, the results are interpreted as a methodological demonstration rather than empirical evidence from real projects; they provide a reusable framework for prioritising data-integration investment and show that, within the simulated causal structure, cross-domain interactions—particularly Schedule × Risk and Project Type × Change Cost—carry predictive information that single-domain analyses cannot recover. Validation on real, partially integrated datasets is identified as essential future work. Full article

CRISPR-Cas9 has transformed microbial biotechnology by enabling precise genome modifications; however, achieving high editing efficiency remains a challenge due to multiple determinants, including on-target specificity, off-target events, PAM sequence, sgRNA scaffold composition, and RNA secondary structure. Our review foresees how artificial intelligence (AI) can address those challenges by enabling automated identification as well as highly active guide RNA (gRNA) optimisation. We highlight the influence of a data-driven training strategy that is focused on high-quality, diverse, and accurately labelled microbial datasets—mainly, given the limitations of models derived from mammalian systems that are not directly transferable to microbial organisms. Moreover, we discuss the key role of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and centralised, curated CRISPR-Cas databases as foundational elements for developing robust and predictive frameworks. Emerging directions are also explored, including generative AI approaches capable of supporting automated experimental planning. By considering the potential dual use of such technologies, the review further addresses bioethical considerations and regulatory frameworks necessary to ensure responsible genome engineering as a milestone, as well as the implementation of safeguards against misuse, particularly in pathogenic microorganisms. Furthermore, the convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications. Full article

Accurate tire–soil traction prediction is critical for agricultural and off-road vehicle design, yet rigorous comparisons of advanced discretization strategies under controlled-slip conditions remain limited. This study compares MM-ALE and Hybrid FE-SPH (H-SPH) discretization in LS-DYNA for SRTT (225/60R16) traction prediction on sandy loam (0.4% gravimetric moisture content) across 5–40% slip ratios. A CT-scan-based tire model using Yeoh visco-hyperelastic rubber (Material_2) was validated against experimental data, achieving CORA scores of 0.989 (radial deflection), 0.999 (loaded radius), 0.947 (footprint area), and 0.985 (contact pressure), outperforming the Mooney–Rivlin formulation (Material_1; CORA = 0.618). Soil moisture content (0.4%, 8%, 14%) was included as a design variable through a Latin Hypercube Sampling framework. Both methods reproduced a monotonic increase in traction; inter-method differences ranged from 29 to 36% at low slip, converging to a 7.8% coefficient of variation at 40% slip. A 27-run full-factorial DOE-I identified normal load as the dominant traction driver (90.1%), followed by velocity (8.6%) and inflation pressure (1.3%). An LHS-based DOE-II revealed moisture content as the primary driver of traction coefficient (67.5%), via a non-monotonic cohesion mechanism peaking at 8% gravimetric moisture. H-SPH reduced runtime by 38% versus MM-ALE. The validated framework provides reusable traction prediction protocols for variable conditions. Full article

Background/Objectives: First-generation CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib) target the conserved ATP-binding pocket of CDK4 and, despite clinical success, are limited by acquired resistance and insufficient exploration of alternative regulatory sites. This study aimed to identify a putative allosteric small-molecule candidate at the CDK4 αE-helix–Cyclin D1 α1-helix protein–protein interaction (PPI) interface within the CDK4/Cyclin D1/p21 ternary complex using RapidFunnel-AI, a decision-interpretable virtual-screening pipeline. Methods: Starting from 50,000 ChEMBL 33 molecules, the pipeline sequentially applied a Q-Fold/RapidFunnel topological Tanimoto scan based on clinical CDK4/6 inhibitor motifs, fragment-level electronic-property enrichment, ADMET/PAINS filtering, dry Vina-GPU docking, hydration-mediated AutoDock-GPU (Version 1.6) docking, explicit-solvent molecular dynamics, contact-retention analysis, and MM-GBSA energy decomposition. The Q-Fold Thermo-Core surrogate model provided fragment-level enrichment, predicting the HOMO–LUMO gap (R² = 0.93) and isotropic polarizability (R² = 0.98) on QM9. Candidate selection did not rely on the lowest docking or MM-GBSA score alone, but on pose persistence, contact continuity, and energy-component consistency. Results: The workflow reduced the initial library to 43 topologically prioritized candidates, 25 ADMET/PAINS-filtered ligands, and 9 docking-derived complexes for MD validation. Ligand_020 emerged as the only candidate that preserved a persistent binding mode at Site 2 during a 500 ns simulation—an interface engagement reproduced across three independent 500 ns replicates with no full dissociation in any replicate—with a protein Cα RMSD of 2.88 ± 0.32 Å, a ligand heavy-atom RMSD of 3.56 ± 0.28 Å, and a van der Waals-dominated MM-GBSA profile (ΔGbind = −28.23 ± 3.57 kcal/mol). In contrast, palbociclib and ribociclib, forcibly placed at Site 2 as negative controls, lost most initial contacts within 5 ns and tended to detach despite more favorable MM-GBSA values. Conclusions: These results suggest that single-score docking or MM-GBSA ranking can generate false positives at shallow PPI interfaces. By integrating AI-assisted prioritization, multipocket docking, explicit-solvent MD, contact-retention analysis, and energy-component consistency, RapidFunnel-AI nominated Ligand_020 as an experimentally testable putative allosteric hit targeting the CDK4/Cyclin D1 interface, offering a reusable platform for PPI-focused oncological drug discovery. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

Cultural heritage is a critical pillar of identity, social cohesion and continuity within ethnocultural communities. However, the preservation of cultural heritage across Southern Africa is largely constrained by fragmented colonial policy implementation, and limited community engagement. This study critically examines the application of the Evidence-Based Policy and Practice (EBPP) model as a decolonizing framework for sustainable management of cultural heritage. The study conducts a structured scoping review of literature to explore the integration of EBPP with the principles of Collective Benefit, Authority to Control, Responsibility, Ethics (CARE), and the principles of Findable, Accessible, Interoperable, Reusable (FAIR) to support inclusive and ethical governance. The findings of the study reveal that sustainable management of cultural heritage is dependent upon community-led governance, alignment between research, policy, and practice, and strengthening of intellectual property protections. The study identifies persistent gaps in the operationalization of indigenous knowledge policies and highlighted the need for participatory approaches to ensure the long-term sustainability of cultural heritage. The study argues that the integration of EBPP, alongside the principles of CARE and FAIR, significantly enhances accountability, fosters data sovereignty, and supports the decolonization of knowledge systems. Thus, the study makes a significant contribution to the growing global discourse on sustainable development by positioning cultural heritage as a dynamic resource for social transformation. Full article

Asymmetric SiC membranes with surface pore sizes ranging from 0.12 to 0.31 μm at a constant open porosity of approximately 42% were fabricated by dip-coating SiC support followed by sintering from 1700 to 2000 °C. The effect of membrane structural constants (hydraulic resistance (k₁), pore size exponent (k₂), and shape factor (k₃)) on PWP were evaluated by comparing the symmetric and asymmetric structures. In addition, the experimentally determined values of PWP were quantitatively analyzed by comparing with theoretically predicted values obtained using the Kozeny–Carman (K–C) and Hagen–Poiseuille (H–P) models. Despite having a smaller pore size, the asymmetric membranes exhibited high PWP (1257-3883 LMH) due to decreased flow resistance (low k₁), enhanced pore size effect (high k₂), and improved flow network (high k₃) as compared to symmetric membranes. The hydrophilicity of the prepared membranes improved remarkably, with increasing average surface roughness (102.3 nm to 161.0 nm) due to an increase in pore size, which also caused a decrease in water contact angle (WCA) from approximately 27.44° to 21.67° with increasing sintering temperature (1700–2000 °C). Furthermore, the prepared membrane separation performance was found to be affected by its pore size, and the 1900 °C sintered SiC membrane showed optimal gradient profile and pore structure, demonstrating its practical reusability and scalability for O/W wastewater treatment. Full article

Artificial intelligence (AI) has reshaped higher education over six decades, evolving from drill-and-practice programs to adaptive cognitive tutors and, most recently, transformer-based generative models. This article presents a critical review of publisher-built generative AI assistants, adopting an explicitly socio-technical perspective that combines a technological lens with a pedagogical one. It makes three contributions. First, it synthesizes the technical and algorithmic evolution of educational AI, from rule-based and expert systems through knowledge tracing and learning analytics to large language models and retrieval-augmented generation, and organizes these mechanisms into a taxonomy. Second, it introduces a reproducible evaluation framework that couples the TRIAD rubric (Trust, Relevance, Impact, Adoption, and Design) with a Jobs-to-Be-Done (JTBD) lens, complete with anchored scoring criteria, an evidence-and-confidence grading scheme, and reported inter-rater reliability. Third, it applies the framework to eleven assistants released by U.S. publishers, distinguishing peer-reviewed evidence from institutional reports and commercial claims. The analysis reflects a mid-2025 snapshot and is presented as a reusable template rather than a static ranking. Findings reveal substantial variation in privacy safeguards, curricular alignment, documented impact, adoption, and usability. The review identifies application scenarios and recommendations for researchers and institutional leaders seeking to guide the responsible integration of AI in higher education. Full article

The rapid growth in the use of carbon fiber-reinforced polymers (CFRPs) and fused-deposition-modeled (FDM) polylactic acid (PLA) has generated substantial non-biodegradable and thermoplastic waste streams, creating urgent needs for scalable recycling and valorization strategies. This study develops and evaluates an integrated route that chemically recovers carbon fibers (CFs) from CFRP waste and converts them into high-performance reinforcements for recycled PLA matrices. CFRP fragments were pre-swollen in acetic acid (120 °C, 1 h), then depolymerized by means of oxidation with 1 M KMnO₄ (100 °C, 2 h), washed, dried (100 °C, 24 h), and size-reduced by means of cryogenic milling. Recycled CFs (treated) and untreated CFRP fragments were blended with 3D-printing PLA waste at 10, 20 and 30 wt.% via melt mixing (175 °C, 5 min, 70 rpm) and molded into ASTM D638 dog-bone specimens. Materials were characterized via XRD, FTIR, Raman, SEM and mechanical testing. XRD and Raman confirmed retention of the graphitic backbone after treatment; FTIR and Raman revealed oxygen-containing surface functionalization consistent with oxidation, while SEM showed effective removal of epoxy and improved fiber surface cleanliness. Compared with neat PLA (tensile strength 45.4 MPa; modulus 2.6 GPa; elongation 6.3%), composites reinforced with chemically recycled CFs exhibited marked mechanical enhancement: at 30 wt.% treated CF, the tensile strength increased to 102.6 MPa (+126%), elastic modulus to 11.7 GPa (+350%), and toughness to 250.3 MPa, while ductility decreased to 2.9%. Equivalent composites with untreated CFRP exhibited smaller gains (30 wt.%: tensile 87.3 MPa; modulus 10.3 GPa), highlighting the benefit of epoxy removal and surface activation for fiber–matrix adhesion. The proposed chemical recycling pathway is operationally simple and cost-effective, produces reusable CFs with preserved graphitic structure and enhanced surface chemistry, and enables the fabrication of high-performance, waste-derived PLA composites suitable for structural and engineering applications. This work demonstrates a viable waste-to-value approach that advances circularity for both CFRP and 3D-printing polymer waste streams. Full article