Search Results (177)

The acid number is widely recognized as one of the most essential and frequently used indicators for evaluating the degradation state of lubricants. Changes in acid number serve as a direct reflection of the oil’s oxidative deterioration. Conventional prediction methods, however, often neglect the coupling effects among multiple physical factors and lack sufficient dynamic adaptability. Therefore, this study proposes a method for predicting the variation trend of lubricating oil acid number by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Subtractive Clustering (SC), establishing an SC-ANFIS-based predictive model. The subtractive clustering technique automatically determines the number of fuzzy rules and initial parameters directly from the dataset, thereby eliminating redundant rules and simplifying the model architecture. The SC-ANFIS model further optimizes the parameters of the fuzzy inference system through the self-learning ability of neural networks. Lubricant aging tests were conducted using a laboratory oxidation stability tester. Regular sampling was carried out to acquire comprehensive lubricant performance degradation data. The input variables of the model include the current acid number, carbonyl peak intensity, metal element concentrations (Fe and Cu), viscosity, and water content of the lubricating oil, while the output variable corresponds to the rate of change in the acid number of the lubricating oil relative to the previous time step. The proposed model demonstrates effective prediction of the lubricating oil acid number variation trend. Posterior difference tests confirmed its high predictive accuracy, with all three evaluation metrics—RMSE, MAE, and MAPE—outperforming those of the BP model. Full article

The macro-economic growth rate of Peru is analyzed for sensitivity to climatic conditions. Year-on-year fluctuations in the inflation-adjusted gross domestic product (GDP) per capita over the period 1970–2024 are subjected to correlation and composite statistical methods. Upturns relate to cool east Pacific La Niña, downturns with warm El Niño. Composites are analyzed by subtracting upper and lower terciles, representing a difference of ~USD 40 B at current value. These reveal how the regional climate exerts a partial influence among external factors. During the austral summer with southeasterly winds over the east Pacific, sea temperatures undergo a 2.5 °C cooling. Consequently, atmospheric subsidence draws humid air from the Amazon toward the Peruvian highlands, improving crop production. Dry weather along the coast sustains transportation networks and urban infrastructure, ensuring good economic performance over the year. The opposing influence of El Niño is built into the statistics. A multi-variate algorithm is developed to predict changes in the Peru growth rate. Austral summer winds and subsurface temperatures over the tropical east Pacific account for a modest 23% of year-on-year variance. Although external factors and the varied landscape weaken macro-economic links with climate, our predictors significantly improve on traditional indices: SOI and Nino3. Adaptive measures are suggested to take advantage of Southern Oscillation’s influence on Peru’s economy. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

Students with autism spectrum disorder (ASD) often face difficulties in solving arithmetic word problems, particularly in transitioning from informal counting strategies to more efficient methods based on number facts and formal operations. This study examined the development of problem-solving strategies in a single student with ASD and intellectual disability across two sequential single-case experiments using multiple baseline designs. Study 1 (age 13 years 9 months; 17 sessions) employed Modified Schema-Based Instruction (MSBI) to teach addition and subtraction change problems, while Study 2 (age 14 years 10 months; 18 sessions) utilized the Conceptual Model-based Problem Solving (COMPS) approach for multiplication and division equal-group problems. Success was defined as both correctness of the response and correctly identifying the required operation. Results indicated that the student’s performance improved in all problem types in both studies, with maintenance observed 8 weeks after Study 1 and 5 weeks after Study 2. Instruction effects generalized to two-step addition and subtraction problems in Study 1, and to two-step addition and multiplication problems in Study 2. The findings indicate that both MSBI and COMPS facilitated the student’s shift from informal strategies to efficient operation-based problem solving. Implications for practice include the need for individualized reinforcements, careful adaptation of instruction, and providing teachers with a variety of problems and knowledge of these teaching methods to support students with ASD in developing advanced problem-solving skills. Full article

Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, which can address the fuzziness of customers’ emotional responses in comments and the nonlinearity of modeling. However, due to the black box problem in ANFIS, the nonlinearity of the modeling cannot be shown explicitly. To solve the above problems, a chaos-driven ANFIS approach is proposed to develop customer preference models using online comments. The model’s nonlinear relationships are represented transparently through the fuzzy rules obtained, which provide human-readable equations. In the proposed approach, online reviews are analyzed using sentiment analysis to extract the information that will be used as the data sets for modeling. After that, the chaos optimization algorithm (COA) is applied to determine the polynomial structure of the fuzzy rules in ANFIS to model the customer preferences. Using laptop products as a case study, several approaches are evaluated for validation, including fuzzy regression, fuzzy least-squares regression, ANFIS, ANFIS with subtractive cluster, and ANFIS with K-means. Compared to the other five approaches, the values of mean relative error, variance of error, and confidence interval of validation error are improved based on the proposed approach. Full article

Road extraction from high-resolution remote sensing images (HRSIs) provides critical support for downstream tasks such as autonomous driving path planning and urban planning. Although deep learning-based pixel-level segmentation methods have achieved significant progress, they still face challenges in handling occlusions caused by vegetation and shadows, and often exhibit limited model robustness and generalization capability. To address these limitations, this paper proposes the SAM2MS model, which leverages the powerful generalization capabilities of the foundational vision model, segment anything model 2 (SAM2). Firstly, an adapter-based fine-tuning strategy is employed to effectively transfer the capabilities of SAM2 to the HRSI road extraction task. Secondly, we subsequently designed a subtraction block (Sub) to process adjacent feature maps, effectively eliminating redundancy during the decoding phase. Multiple Subs are cascaded to form the multi-scale subtraction module (MSSM), which progressively refines local feature representations, thereby enhancing segmentation accuracy. During training, a training-free lossnet module is introduced, establishing a multi-level supervision strategy that encompasses background suppression, contour refinement, and region-of-interest consistency. Extensive experiments on three large-scale and challenging HRSI road datasets, including DeepGlobe, SpaceNet, and Massachusetts, demonstrate that SAM2MS significantly outperforms baseline methods across nearly all evaluation metrics. Furthermore, cross-dataset transfer experiments (from DeepGlobe to SpaceNet and Massachusetts) conducted without any additional training validate the model’s exceptional generalization capability and robustness. Full article

It is challenging to manufacture complex and intricate shapes and geometries with desired surface characteristics using a single manufacturing process. Parts often need to undergo post-processing and must be transported from one machine into another between steps. This makes the whole process cumbersome, time-consuming, and inaccurate. These shortcomings play a major role during the manufacturing of micro and nano products. Hybrid manufacturing (HM) has emerged as a favorable solution for these issues. It is a flexible process that combines two or more manufacturing processes, such as additive manufacturing (AM) and subtractive manufacturing (SM), into a single setup. HM works synergistically to produce complex, composite, and customized components. It makes the process more time efficient and accurate and can prevent unnecessary transportation of parts. There are still challenges ahead regarding implementing and integrating sensors that allow the machine to detect defects and repair or customize parts according to needs. Even though modern hybrid machines forecast an exciting future in the manufacturing world, they still lack features such as real-time adaptive manufacturing based on sensors and artificial intelligence (AI). Earlier reviews do not profoundly elaborate on the types of laser HM machines available. Laser technology resolutely handles additive and subtractive manufacturing and is capable of producing groundbreaking parts using a wide scope of materials. This review focuses on HM and presents a compendious overview of the types of hybrid machines and setups used in the scientific community and industry. The study is unique in the sense that it covers different HM setups based on machine axes, materials, and processing parameters. We hope this study proves helpful to process, plan, and impart productivity to HM processes for the betterment of material utilization and efficiency. Full article

Improving the decoding accuracy of biological signals has been a research focus for decades to advance health, automation, and robotic industries. However, challenges like inter-subject variability, data scarcity, and multifunctional variability cause low decoding accuracy, thus hindering the practical deployment of biological signal paradigms. This paper proposes a multifunctional biological signals network (Multi-BioSig-Net) that addresses the aforementioned issues by devising a novel blind few-shot learning (FSL) technique to quickly adapt to multiple target domains without needing a pre-trained model. Specifically, our proposed multimodal similarity extractor (MMSE) and self-multiple domain adaptation (SMDA) modules address data scarcity and inter-subject variability issues by exploiting and enhancing the similarity between multimodal samples and quickly adapting the target domains by adaptively adjusting the parameters’ weights and position, respectively. For multifunctional learning, we proposed inter-function discriminator (IFD) that discriminates the classes by extracting inter-class common features and then subtracts them from both classes to avoid false prediction of the proposed model due to overfitting on the common features. Furthermore, we proposed a holistic-local fusion (HLF) module that exploits contextual-detailed features to adapt the scale-varying features across multiple functions. In addition, fractal dimension estimation (FDE) was employed for the classification of left-hand motor imagery (LMI) and right-hand motor imagery (RMI), confirming that proposed method can effectively extract the discriminative features for this task. The effectiveness of our proposed algorithm was assessed quantitatively and statistically against competent state-of-the-art (SOTA) algorithms utilizing three public datasets, demonstrating that our proposed algorithm outperformed SOTA algorithms. Full article

Obstructive sleep apnea–hypopnea syndrome (OSAHS) is a prevalent sleep disorder strongly linked to increased cardiovascular and metabolic risk. While prior studies have explored snore-based analysis for OSAHS, they have largely focused on either detection or classification in isolation. Here, we present a two-stage framework that integrates precise snoring event detection with deep learning-based classification. In the first stage, we develop an Adaptive Multi-Feature Fusion Endpoint Detection algorithm (AMFF-ED), which leverages short-time energy, spectral entropy, zero-crossing rate, and spectral centroid to accurately isolate snore segments following spectral subtraction noise reduction. Through adaptive statistical thresholding, joint decision-making, and post-processing, our method achieves a segmentation accuracy of 96.4%. Building upon this, we construct a balanced dataset comprising 6830 normal and 6814 OSAHS-related snore samples, which are transformed into Mel spectrograms and input into ERBG-Net—a hybrid deep neural network combining ECA-enhanced ResNet18 with bidirectional GRUs. This architecture captures both spectral patterns and temporal dynamics of snoring sounds. The experimental results demonstrate a classification accuracy of 95.84% and an F1 score of 94.82% on the test set, highlighting the model’s robust performance and its potential as a foundation for automated, at-home OSAHS screening. Full article

The evaluation of pressure drops across the length of production wells is a crucial task, as it influences both the cost-effective selection of tubing and the development of an efficient production strategy, both of which are vital for maximizing oil recovery while minimizing operational expenses. To address this, our study proposes an innovative hybrid intelligent system designed to predict bottom-hole flowing pressure in vertical multiphase conditions with superior accuracy compared to existing methods using a data set of 150 field measurements amassed from Algerian fields. In this work, the applied hybrid framework is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which integrates artificial neural networks (ANN) with fuzzy logic (FL). The ANFIS model was constructed using a subtractive clustering technique after data filtering, and then its outcomes were evaluated against the most widely utilized correlations and mechanistic models. Graphical inspection and error statistics confirmed that ANFIS consistently outperformed all other approaches in terms of precision, reliability, and effectiveness. For further improvement of the ANFIS performance, a particle swarm optimization (PSO) algorithm is employed to refine the model and optimize the design of the antecedent Gaussian memberships along with the consequent linear coefficient vector. The results achieved by the hybrid ANFIS-PSO model demonstrated greater accuracy in bottom-hole pressure estimation than the conventional hybrid approach. Full article

Background/Objectives: Computer-aided manufacturing techniques are divided into subtractive (milling) and additive (3D printing) techniques. The accuracy of both techniques is measured only indirectly by testing the fabricated restorations. However, the role of the fabrication technique is masked by the differences in the materials used. Hence, this study used the same printing resin to print crowns and blocks for milling. Methods: Ten maxillary first premolars were prepared for full crowns and scanned with Primescan Connect IOS, and then crown restorations were designed using Exocad. A CAD/CAM block equal to size C14 was designed in CAD software (Microsoft 3D Builder) (Version 18.0.1931.0). The designed crowns and blocks were printed using three hybrid ceramic materials, namely, Ceramic Crown (SprintRay), Varseosmile Crown plus (Bego), and P-crown (Senertek), using a SprintRay Pro95S 3D-printer. The printed blocks were then used to fabricate the designed crowns using an In-Lab MCXL milling machine. The trueness and marginal and internal gaps of the crowns were then measured using Geomagic Control X metrology software (Version 2022.1). Statistical analysis was performed using the Kruskal–Wallis test, Dunn’s test, one-way ANOVA test, and Tukey’s HSD test. Results: Generally, the milled crowns showed significantly higher trueness but lower fitness than their 3D-printed counterparts (p < 0.05). A significant reverse correlation was found between the trueness and fitness of the fabricated restorations. Conclusions: The fabrication technique significantly influenced the accuracy of the hybrid ceramic crowns. Milling offered superior trueness, whereas 3D printing resulted in better internal and marginal adaptation. Full article

Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, the impact of two clustering techniques, Subtractive Clustering (SC) and Fuzzy C-Means (FCM), along with their cogent hyperparameters, were investigated, yielding several sub-models. The efficacy of the proposed models was evaluated using five standard statistical metrics, while the optimal model was also compared with other variants and hybrid models. Results obtained showed that the GA-ANFIS-FCM with four clusters achieved the best performance, recording the lowest Root Mean Square Error (RMSE) of 3.83, Mean Absolute Error (MAE) of 2.40, Theil’s U of 0.87, and Standard Deviation (SD) of 3.82. The developed model contributes valuable insights towards informed energy decisions. Full article

Background/Objectives: Back table fluorescence imaging performed on freshly excised tissue specimens represents a critical step in fluorescence-guided surgery, enabling rapid assessment of tumor margins before final pathology. While most preclinical NIR-II imaging platforms, such as the IR VIVO (Photon, etc.), offer high-resolution and depth-sensitive imaging under controlled, enclosed conditions, they are not designed for intraoperative or point-of-care use. This study compares the IR VIVO with the LightIR system, a more compact and clinically adaptable imaging platform using the same Alizé 1.7 InGaAs detector, to evaluate whether the LightIR can offer comparable performance for back table NIR-II imaging under ambient light. Methods: Standardized QUEL phantoms containing indocyanine green (ICG) and custom agar-based tissue-mimicking phantoms loaded with IR-1048 were imaged on both systems. Imaging sensitivity, spatial resolution, and depth penetration were quantitatively assessed. LightIR was operated in pulse-mode under ambient lighting, mimicking back table or intraoperative use, while IR VIVO was operated in a fully enclosed configuration. Results: The IR VIVO system achieved high spatial resolution (~125 µm) and detected ICG concentrations as low as 30 nM in NIR-I and 300 nM in NIR-II. The LightIR system, though requiring longer exposure times, successfully resolved features down to ~250 µm and detected ICG to depths ≥4 mm. Importantly, the LightIR maintained robust NIR-II contrast under ambient lighting, aided by real-time background subtraction, and enabled clear visualization of subsurface IR-1048 targets in unshielded phantom setups, conditions relevant to back table workflows. Conclusions: LightIR offers performance comparable to the IR VIVO in terms of depth penetration and spatial resolution, while also enabling open-field NIR-II imaging without the need for a blackout enclosure. These features position the LightIR as a practical alternative for rapid, high-contrast fluorescence assessment during back table imaging. The availability of such clinical-grade systems may catalyze the development of new NIR-II fluorophores tailored for real-time surgical applications. Full article

Dental implant components are typically fabricated using subtractive manufacturing, often involving metal materials that can be costly, inefficient, and time-consuming. This study explores the use of additive manufacturing (AM) with zirconia for dental implant overdenture bars, focusing on mechanical performance, stress distribution, and fit. Solid and lattice-structured bars were designed in Fusion 360 and produced using LithaCon 210 3Y-TZP zirconia (Lithoz GmbH, Vienna, Austria) on a CeraFab 8500 printer. Post-processing included cleaning, debinding, and sintering. A 3D-printed denture was also fabricated to evaluate fit. Thermography and optical imaging were used to assess adaptation. Custom fixtures were developed for flexural testing, and fracture loads were recorded to calculate stress distribution using finite element analysis (ANSYS R2025). The FEA model assumed isotropic, homogeneous, linear-elastic material behavior. Bars were torqued to 15 Ncm on implant analogs. The average fracture loads were 1.2240 kN (solid, n = 12) and 1.1132 kN (lattice, n = 5), with corresponding stress values of 147 MPa and 143 MPa, respectively. No statistically significant difference was observed (p = 0.578; α = 0.05). The fracture occurred near high-stress regions at fixture support points. All bars demonstrated a clinically acceptable fit on the model; however, further validation and clinical evaluation are still needed. Additively manufactured zirconia bars, including lattice structures, show promise as alternatives to conventional superstructures, potentially offering reduced material use and faster production without compromising mechanical performance. Full article

The Online Droplet Inspection system plays a vital role in closed-loop control for OLED inkjet printing. However, generating realistic synthetic droplet images for reliable restoration and precise measurement of droplet parameters remains challenging due to the complex, multi-factor degradation inherent to microscale droplet imaging. To address this, we propose a physics-informed degradation model, Diffraction–Gaussian–Motion–Noise (DGMN), that integrates Fraunhofer diffraction, defocus blur, motion blur, and adaptive noise to replicate real-world degradation in droplet images. To optimize the multi-parameter configuration of DGMN, we introduce the MISABO (Multi-strategy Improved Subtraction-Average-Based Optimizer), which incorporates Sobol sequence initialization for search diversity, lens opposition-based learning (LensOBL) for enhanced accuracy, and dimension learning-based hunting (DLH) for balanced global–local optimization. Benchmark function evaluations demonstrate that MISABO achieves superior convergence speed and accuracy. When applied to generate synthetic droplet images based on real droplet images captured from a self-developed OLED inkjet printer, the proposed MISABO-optimized DGMN framework significantly improves realism, enhancing synthesis quality by 37.7% over traditional manually configured models. This work lays a solid foundation for generating high-quality synthetic data to support droplet image restoration and downstream inkjet printing processes. Full article