Search Results (19,633)

Accurate phase identification is essential for characterising complex mineral systems but remains a challenge in SEM-based automated mineralogy (AM) for compositionally variable rock-forming or accessory minerals. While platforms such as the Tescan Integrated Mineral Analyzer (TIMA) offer high-resolution phase mapping through BSE-EDS data, classification accuracy depends on the quality of the user-defined phase library. Generic libraries often fail to capture site-specific mineral compositions, resulting in misclassification and unclassified pixels, particularly in systems with solid solution behaviour, compositional zoning, and textural complexity. We present a refined approach to developing and validating custom TIMA phase libraries. We outline strategies for iterative rule refinement using mineral chemistry, textures, and BSE-EDS responses. Phase assignments were validated using complementary microanalytical techniques, primarily electron probe microanalysis (EPMA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS). Three Queensland case studies demonstrate this approach: amphiboles in an IOCG deposit; cobalt-bearing phases in a sediment-hosted Cu-Au-Co deposit; and Li-micas in an LCT pegmatite system. Targeted refinement of phases improves identification, reduces unclassified phases, and enables rare phase recognition. Expert-guided phase library development strengthens mineral systems research and downstream applications in geoscience, ore deposits, and critical minerals while integrating datasets across scales from cores to mineral mapping. Full article

Drone delivery has gained significant traction in e-commerce, particularly for parcel and food delivery. However, existing systems face challenges such as limited delivery range, low efficiency, high costs, and suboptimal customer satisfaction. This paper proposes a novel drone–rider joint delivery model incorporating an Arc Obstacle Avoidance (AOA) strategy to address these issues in complex urban environments. We formulate a multi-objective optimization model aimed at minimizing delivery costs and maximizing customer satisfaction, solved by a Logistic-Logarithmic Dung Beetle Optimization algorithm (LLDBO). Using a modified Solomon dataset and real-world urban simulations in Shenzhen, our experiments demonstrate that the proposed model achieves a 15.3% reduction in delivery costs and a 27.1% increase in delivery efficiency compared to traditional rider-only delivery. Furthermore, customer satisfaction, measured by the on-time delivery rate, shows a 12.4% improvement (from 83.1% to 95.5%) over the rider-only baseline. The AOA strategy also extends the effective delivery range by up to 22.5% compared to conventional linear obstacle avoidance approaches, as measured by the maximum service radius achievable while maintaining 95% on-time delivery performance. These findings validate the practicality and scalability of the proposed approach for real-world last-mile logistics. Full article

Historic floors, including mosaics, stone slabs, and decorated pavements, are fragile elements that can be easily damaged during restoration works. Risks arise from falling tools, concentrated loads of scaffolding or equipment, and the repeated passage of workers. Traditional protection methods, such as plywood sheets, mats, multilayer systems, or modular plastic panels, have been applied in different sites but often present limitations in adaptability to irregular surfaces, in moisture control, and in long-term reversibility. This paper introduces an innovative approach developed within the 3D-EcoCore project. The proposed solution consists of a bio-inspired modular sandwich system manufactured by 3D printing with biodegradable polymers. Each module contains a Voronoi-inspired cellular core, shaped to match the geometry of the floor obtained from digital surveys, and an upper flat skin that provides a safe and resistant surface. The design ensures mechanical protection, adaptability to uneven pavements, and the possibility to integrate ventilation gaps, cable pathways, and monitoring systems. Beyond heritage interventions, the system also supports routine architectural maintenance by enabling safe, reversible protection during inspections and minor repairs. The solution is strictly temporary and non-substitutive, fully aligned with conservation principles of reversibility, recognizability, and minimal intervention. The Ninfeo Ponari in Cassino is presented as a guiding example, showing how multilevel knowledge and thematic mapping become essential inputs for the tailored design of the modules. The paper highlights both the technical innovation of the system and the methodological contribution of a knowledge-based design process, opening future perspectives for durability assessment, pilot installations, and the integration of artificial intelligence to optimise core configurations. Full article

Current evaluation methods for Dual-clutch Transmission (DCT) control systems typically focus on certain performance metrics while neglecting the assessment and quantitative diagnosis of system intelligence. To address this limitation, this paper employs a customized Analytic Hierarchy Process to determine the utility weights of scenario categories and scenario performance. A discrete choice model based on utility decision criteria is introduced, with the overall utility quantifying the intelligence of DCT control systems. This approach culminates in a diagnostic model for the intelligence of DCT control systems based on utility weights and the Analytic Hierarchy Process. Experimental validation involved comparative testing of two distinct DCT control systems installed on identical vehicles under multi-dimensional scenarios. Results demonstrate that the proposed model can accurately identify, analyze, compare, and evaluate the intelligence of DCT control systems. It shows broad applicability in vehicle intelligence, DCT control systems research and related fields. Full article

In recent years, the rapid advancement of large language models (LLMs) has driven significant breakthroughs in artificial intelligence. Leveraging LLMs in conjunction with domain-specific knowledge to develop intelligent assistants can reduce operational costs and facilitate industrial upgrading. In the field of automotive fault diagnosis, traditional methods rely heavily on technicians’ experience, resulting in limitations in both efficiency and accuracy. Misdiagnosis or insufficient expertise can lead to repair delays, while information asymmetry may cause trust issues between service providers and customers. To address these challenges, we propose a vehicle fault diagnosis framework based on knowledge graphs and large language models. Unlike traditional retrieval-augmented generation (RAG) methods, our framework actively queries for missing information and delivers precise repair recommendations. Experimental evaluations demonstrate that our framework achieves a diagnosis accuracy of 77.3%, representing a 46.1% improvement over direct diagnosis using a pretrained LLM (GPT-3.5) and over a 14% increase compared to other existing frameworks. Ablation studies confirm the effectiveness of each module, and our findings are further illustrated through detailed charts and visualizations. Overall, this study highlights the potential of integrating knowledge graphs with large language models for automotive fault diagnosis, with promising applicability to other traditional industries. Full article

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

This study presents the design and preliminary validation of a novel portable, wireless, and wearable sensing system—The Breast Impact Monitoring System (BIMS)—for female athletes, developed to monitor and quantify localised mechanical impacts to the breast during high-intensity sporting activity. The platform addresses a critical gap in sports biomechanics by enabling, for the first time, objective measurement of breast forces in both controlled mechanical impact testing and preliminary on-body tackling trials for female athletes. Its application extends to advancing understanding of sports-related breast injuries, informing prevention strategies, and assessing the effectiveness of protective equipment. The BIMS leverages an array of 16 thin-film Force Sensitive Resistors (FSRs) and employs a dual-core microcontroller architecture to manage the trade-off between wireless constraints and high-speed data fidelity, successfully achieving uninterrupted acquisition at 856 Hz for each channel. The system was rigorously validated against a reference instrument using a commercial Force Plate and a custom mechanical drop rig, demonstrating high accuracy with a calibration model ($R^2=0.9988$). Preliminary wearable testing confirmed the system’s capability to detect and spatially map high localised impact forces, including peak forces up to 550 N (across an area diameter of 20 mm), during preliminary rugby tackling activities. By offering a practical and scalable solution for capturing previously inaccessible data, this system establishes a foundation for future research into athlete welfare and long-term breast health. Full article

This study presents the development of a prototype anthropomorphic soft robotic gripper intended for applications in rehabilitation and assistive robotics, where safe and adaptive interaction with humans is required. The device consists of three elastomeric fingers, fabricated in TPU via FFF 3D printing and actuated through pneumatic soft actuators that ensure compliant contact with both biological tissue and rigid objects. A custom 3D-printed pneumatic rotary actuator enables finger reconfiguration, thereby extending the range of grasping modalities. The actuation system comprises six 2/2 solenoid valves controlled by an Arduino Uno and integrated into a dedicated pneumatic circuit. Experimental characterization demonstrated a peak grasping force exceeding 17 N on rigid targets, while functional tests in table-picking scenarios confirmed adaptability to objects of varying shapes and sizes. Owing to its anthropomorphic configuration, mechanical compliance, and ease of fabrication and control, the proposed gripper represents a versatile solution for rehabilitation-oriented devices as well as assistive robotic end-effectors in pick-and-place tasks. Full article

The integration of generative artificial intelligence, particularly large language models, into education presents opportunities for both personalised learning and pedagogical challenges. This study focuses on electrical engineering laboratory education. We developed a configurable prototype of a generative artificial intelligence powered tutoring tool, implemented it in an undergraduate electrical engineering laboratory course, and analysed 208 student–tutoring tool interactions using a mixed-methods approach that combined research team evaluation with learner feedback. The findings show that student prompts were predominantly procedural or factual, with limited conceptual or metacognitive engagement. Structured prompt styles produced clearer and more coherent responses and were rated the highest by students, while approaches aimed at fostering reasoning and reflection were valued mainly by the research team for their pedagogical depth. This contrast highlights a consistent preference–pedagogy gap, indicating the need to embed stronger instructional guidance into artificial intelligence tutoring. To bridge this gap, a promising direction is the development of pedagogically enriched AI tutors that integrate features such as adaptive prompting, hybrid strategy blending, and retrieval-augmented feedback to balance clarity, engagement, and depth. The results provide practical and conceptual value relevant to educators, developers, and researchers interested in artificial intelligence tutors that are both engaging and pedagogically sound. For educators, the study clarifies how students interact with tutors, helping align artificial intelligence use with instructional goals. For developers, it highlights the importance of designing systems that combine usability with educational value. For researchers, the findings identify directions for further study on how design choices in artificial intelligence tutoring affect learning processes and pedagogical alignment across STEM contexts. On a broader level, the study contributes to a more transparent, equitable, and sustainable integration of generative AI in education. Full article

This study aimed to (1) demonstrate the intra- and interrater reliability of quadriceps (QUADS) and hamstring (HAMS) mineral-free lean (MFL) mass measures using DXA scanning, (2) determine the association of total thigh MFL mass measures with MFL mass measures of the hamstrings and quadriceps combined and (3) analyze the association between total thigh MFL mass and total body MFL mass measures. A total of 80 young children (aged 5 to 11 yrs) participated and unique regions of interest were created using custom analysis software with manual tracing of the QUADS, HAMS, and total thigh MFL mass measures. Repeated-measure analysis of variance was used to determine if there were significant differences among the MFL measures while intraclass correlation coefficients (ICC), coefficients of variation (CV), and regression analysis were used to determine the intra- and interrater reliability and the explained variance in the association among MFL mass measures. The right interrater QUADS MFL mass was the only significant group mean difference, and ICCs between (≥0.961) and within (≥0.919) raters were high for all MFL measures with low variation across all MFL measures (≤6.13%). The explained variance was 92.5% and 96.3% for the between-investigator analyses of the right and left total thigh MFL mass measures, respectively. Furthermore, 97.5% of the variance in total body MFL mass was explained by the total thigh MFL mass. DXA MFL mass measures of the QUADS, HAMS and total thigh can be confidently used in young children and may provide an alternative to CT or MRI scanning when assessing changes in MFL cross-sectional area or volume measures due to disease progression, training and rehabilitative strategies. Full article

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust. Full article

Mobile banking applications play a crucial role in providing users with access to financial services, and the quality of user experience is a key factor for their sustainability. This study investigates the predictability of application quality signals derived from user ratings of five leading mobile banking apps in Türkiye. The main problem addressed is understanding how these user-driven quality indicators evolve over time and identifying effective methods for forecasting them. This research problem is critical for understanding how banks can monitor customer satisfaction and reputational risk in real time, as fluctuations in app ratings directly affect user trust and engagement. For this purpose, daily average rating series collected from the Google Play Store were analyzed using LSTM-based time series models, and the results were benchmarked against the seasonal naïve (SNaive) method. The findings show that LSTM consistently achieved lower error rates across all banks, with particularly reliable forecasts for YapıKredi and Akbank, where MAPE values ranged between 16% and 28%. However, low R² values for some banks suggest limitations in long-term forecasting. The contribution of this study lies in demonstrating that user experience signals in mobile banking can be systematically monitored from a time series perspective, and that LSTM-based approaches provide a more effective method for capturing these quality dynamics. Full article

We evaluate tactile-first robotic traversal on the Department of Homeland Security (DHS) figure-8 mobility test using a two-way repeated-measures design across various algorithms (three tactile policies—M1 reactive, M2 terrain-weighted, M3 memory-augmented; a monocular camera baseline, CB-V; a tactile histogram baseline, T-VFH; and an optional tactile-informed replanner, T-D* Lite) and lighting conditions (Indoor, Outdoor, and Dark). The platform is the custom-built Eleven robot—a quadruped integrating a joint-mounted tactile tentacle with a tip force-sensitive resistor (FSR; Walfront 9snmyvxw25, China; 0–10 kg range, ≈0.1 N resolution @ 83 Hz) and a woven Galvorn carbon-nanotube (CNT) yarn for proprioceptive bend sensing. Control and sensing are fully wireless via an ESP32-S3, Arduino Nano 33 BLE, Raspberry Pi 400, and a mini VESC controller. Across 660 trials, the tactile stack maintained ∼21 ms (p50) policy latency and mid-80% success across all lighting conditions, including total darkness. The memory-augmented tactile policy (M3) exhibited consistent robustness relative to the camera baseline (CB-V), trailing by only ≈3–4% in Indoor and ≈13–16% in Outdoor and Dark conditions. Pre-specified, two one-sided tests (TOSTs) confirmed no speed equivalence in any M3↔CB-V comparison. Unlike vision-based approaches, tactile-first traversal is invariant to illumination and texture—an essential capability for navigation in darkness, smoke, or texture-poor, confined environments. Overall, these results show that a tactile-first, memory-augmented control stack achieves lighting-independent traversal on DHS benchmarks while maintaining competitive latency and success, trading modest speed for robustness and sensing independence. Full article

The gravity forward modeling algorithm is a compute-intensive method and is widely used in scientific computing, particularly in geophysics, to predict the impact of subsurface structures on surface gravity fields. Traditional implementations rely on CPUs, where performance gains are mainly achieved through algorithmic optimization. With the rise of domain-specific architectures, FPGA offers a promising platform for acceleration, but faces challenges such as limited programmability and the high cost of nonlinear function implementation. This work proposes an FPGA-based co-processor to accelerate gravity forward modeling. A RISC-V core is integrated with a custom instruction set targeting key computation steps. Tasks are dynamically scheduled and executed on eight fully pipeline processing units, achieving high parallelism while retaining programmability. To address nonlinear operations, we introduce a piecewise linear approximation method optimized via stochastic gradient descent (SGD), significantly reducing resource usage and latency. The design is implemented on the AMD UltraScale+ ZCU102 FPGA (Advanced Micro Devices, Inc. (AMD), Santa Clara, CA, USA) and evaluated across several forward modeling scenarios. At 250 MHz, the system achieves up to 179× speedup over an Intel Xeon 5218R CPU (Intel Corporation, Santa Clara, CA, USA) and improves energy efficiency by 2040×. To the best of our knowledge, this is the first FPGA-based gravity forward modeling accelerate design. Full article

Computer-assisted surgery (CAS) and virtual surgical planning (VSP) have transformed jaw reconstruction, allowing immediate insertion of dental implants during surgery for better rehabilitation of occlusal function. However, traditional planning for optimal location and angulation of dental implants and fibula relies on experience and can be time-consuming. This study aimed to propose a function-driven workflow and develop an automatic computer program for optimal positioning of simultaneous dental implants and fibula segments. A customized computer program was developed using MATLAB. Computed tomography (CT) of the lower limbs of ninety-one Southern Chinese individuals was retrieved and cross-sections of three-dimensional (3D) fibula models were comprehensively investigated for implant installation. Our research proves that the accuracy of the program in identifying the anatomical orientation of the fibula was 92%. The ideal location, angulation and length of implant could be automatically generated based on any selected implant diameter, with a surgical feasibility of 94%. To the best of our knowledge, this is the first study to develop and validate a customized automatic computer program for osseointegrated implant design in fibula flap surgery. This program can be incorporated into the current workflow of CAS to further the development of reliable and efficient surgical planning for function-driven jaw reconstruction. Full article