Search Results (6,335)

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

The urgent demand of wireless sensor nodes for long-life and maintenance-free miniature electrical sources with output power ranging from microwatts to milliwatts has accelerated the development of energy harvesting technologies. For the abundant and renewable nature of wind in environments, flow-induced vibration (FIV)-based wind energy harvesting has emerged as a promising approach. Electromagnetic FIV wind energy harvesters (WEHs) show great potential for realistic applications due to their excellent durability and stability. However, electromagnetic WEHs remain less studied than piezoelectric WEHs, with few dedicated review articles available. This review analyzes the working principle, device structure, and performance characteristics of electromagnetic WEHs based on vortex-induced vibration, galloping, flutter, wake galloping vibration, and Helmholtz resonator. The methods to improve the output power, broaden the operational wind speed range, broaden the operational wind direction range, and enhance the durability are then discussed, providing some suggestions for the development of high-performance electromagnetic FIV WEHs. Full article

Permeable pavement is an alternative to conventional impermeable pavement for various applications. However, a common issue with permeable pavement is clogging over time. Permeability is a parameter that reflects the capacity of the pavement to reduce surface runoff; a decline in permeability implies the occurrence of clogging. In this study, permeability data collected on pervious concrete (PC) and JW Eco-Technology (JW) revealed that JW maintained consistent permeability over time. However, PC displayed reduced values, and several locations along the edges had zero permeability, despite no regular vehicular and pedestrian use. Therefore, a portable pressure washer was used to clean the pavements. The cleaning procedure was able to recover the permeability of the areas that showed signs of clogging (0 to 2.69 cm/s) and restore the permeability of PC up to 4.60–5.58 cm/s for corner and center areas, respectively. Moreover, visual inspection using a borescope further revealed the full function of the JW pores (aqueducts), regardless of cleaning. Regardless, it is recommended that periodic cleaning maintenance be performed for both PC and JW using a pressure washer due to its convenience and efficacy, which will be discussed. Full article

The standard treatment for patients with locally advanced non-small cell lung cancer (NSCLC) is concurrent chemoradiation. However, clinical responses are heterogeneous and generally not known until after the completion of therapy. Multiple studies have investigated imaging predictors (radiomics) for different cancer histologies, but little exists for NSCLC. The objective of this study was to develop a multivariate CT-based radiomics model to a priori predict responses to definitive chemoradiation in patients with lung adenocarcinoma. Methods: Patients diagnosed with locally advanced unresectable lung adenocarcinoma who had undergone chemoradiotherapy followed by at least one dose of maintenance durvalumab were included. The PyRadiomics Python library was used to determine statistical, morphological, and textural features from normalized patient pre-treatment CT images and their wavelet-filtered versions. A nested leave-one-out cross-validation was used for model building and evaluation. Results: Fifty-seven patients formed the study cohort. The clinical stage was IIIA-C in 98% of patients. All but one received 6000–6600 cGy of radiation in 30–33 fractions. All received concurrent platinum-based chemotherapy. Based on RECIST 1.1, 20 (35%) patients were classified as responders (R) to chemoradiation and 37 (65%) patients as non-responders (NR). A three-feature model based on a KNN k = 1 machine learning classifier was found to have the best performance, achieving a recall, specificity, accuracy, balanced accuracy, precision, negative predictive value, F1-score, and area under the curve of 84%, 70%, 80%, 77%, 84%, 70%, 84%, and 0.77, respectively. Conclusions: Our results suggest that a CT-based radiomics model may be able to predict chemoradiation response for lung adenocarcinoma patients with estimated accuracies of 77–84%. Full article

A reliable estimation of protein requirements in lactating dairy cows is necessary for formulating nutritionally adequate diets, improving feed efficiency, and minimizing nitrogen excretion. This study aimed to develop machine learning-based models to predict net protein requirements for maintenance (NPm) and lactation (NPl) using random forest regression (RFR) and support vector regression (SVR). A total of 1779 observations were assembled from 436 peer-reviewed publications and open-access databases. Predictor variables included farm-ready variables such as milk yield, dry matter intake, days in milk, body weight, and dietary crude protein content. NPm was estimated based on the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) equations, while NPl was derived from milk true protein yield. The model adequacy was evaluated using 10-fold cross-validation. The RFR model demonstrated higher predictive performance than SVR for both NPm (R² = 0.82, RMSEP = 22.38 g/d, CCC = 0.89) and NPl (R² = 0.82, RMSEP = 95.17 g/d, CCC = 0.89), reflecting its capacity to model the rule-based nature of the NASEM equations. These findings suggest that RFR may provide a valuable approach for estimating protein requirements with fewer input variables. Further research should focus on validating these models under field conditions and exploring hybrid modeling frameworks that integrate mechanistic and machine learning approaches. Full article

Background: Cutaneous lichen planus (CLP) is a chronic inflammatory T cell-mediated disease driven by a mixed Th1 and Th2 lymphocyte population, for which many of the currently available treatments have poor efficacy. Aim: The aim of this study was to indicate the clinical success of dupilumab administration after two years of treatment in a case of longstanding CLP and to perform a review of the medical literature related to the use of dupilumab in different dermatologic settings and in CLP. Case presentation: One 26-year-old woman with a previous history of atopic dermatitis had a long-lasting skin condition, referred to as a suspected lichen, which started when she was 7 years old. Her disease exhibited a relapsing–remitting course with severe bouts of pruritus over a very long period. The final histological diagnosis of CLP was confirmed at the age of 26. Starting dupilumab (injected subcutaneously at a dose of 600 mg followed by a maintenance dose of 300 mg every two weeks) resolved the skin scenery of this patient, who is currently in full remission. Conclusions: The remarkable recovery from CLP obtained via treatment with dupilumab in this single-patient case study emphasizes the potential therapeutic implications of targeting the Th2 pathway in this skin disorder. Full article

This paper proposes a control method that combines a fractional-order sliding mode observer and a cooperative control strategy to address the problem of path-following for underactuated autonomous underwater vehicles (AUVs) in complex marine environments. First, a fractional-order sliding mode observer is designed, combining fractional calculus and double-power convergence laws to enhance the estimation accuracy of high-frequency disturbances. An adaptive gain mechanism is introduced to avoid dependence on the upper bound of disturbances. Second, a formation cooperative control strategy based on path parameter coordination is proposed. By setting independent reference points for each AUV and exchanging path parameters, formation consistency is achieved with low communication overhead. For the followers’ speed control problem, an error-based expected speed adjustment mechanism is introduced, and a hyperbolic tangent function is used to replace the traditional arctangent function to improve the response speed of the system. Numerical simulation results show that this control method performs well in terms of path-following accuracy, formation maintenance capability, and disturbance suppression, verifying its effectiveness and robustness in complex marine environments. Full article

This study analyzed the probability of damage in heat transport pipelines buried in urban areas using pipeline attribute information and damage history data and developed an AI-based predictive model. A dataset was constructed by collecting spatial and attribute data of pipelines and defining basic units according to specific standards. Damage trends were analyzed based on pipeline attributes, and correlation analysis was performed to identify influential factors. These factors were applied to three machine learning algorithms: Random Forest, eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The model with optimal performance was selected by comparing evaluation indicators including the F2-score, accuracy, and area under the curve (AUC). The LightGBM model trained on data from pipelines in use for over 20 years showed the best performance (F2-score = 0.804, AUC = 0.837). This model was used to generate a risk map visualizing the probability of pipeline damage. The map can aid in the efficient management of urban heat transport systems by enabling preemptive maintenance in high-risk areas. Incorporating external environmental data and auxiliary facility information in future models could further enhance reliability and support the development of a more effective maintenance decision-making system. Full article

Background/Objectives: To identify factors associated with the referral by a multiprofessional team to dental services for children and adolescents with rare genetic diseases. Methods: A cross-sectional study was developed with 87 children/adolescents with mucopolysaccharidosis (n = 26) and osteogenesis imperfecta (n = 61) and their caregivers. Recruitment took place at reference centers for rare genetic conditions in five Brazilian states. The caregivers answered a questionnaire on the children. They were examined for malocclusion, dental anomalies, caries experience, and gingivitis. Bivariate and multivariate analyses of the data were performed, considering a 95% confidence level. Results: The average age of children/adolescents was 10.4 years (±5.6) and 17.3% had never gone to a dentist. Among those with past dental experience, the reason for most appointments was oral prophylaxis/preventive maintenance (62.1%). With regard to referrals to a dentist by the multidisciplinary team, 29.9% had never received a referral. The likelihood of having been referred to a dentist by the multiprofessional team was 2.67 times greater for female patients (95% CI: 0.96–7.42) and 7.74 times greater for children/adolescents with a history of toothache (95% CI: 1.61–37.14). Conclusions: Female children/adolescents with mucopolysaccharidosis and osteogenesis imperfecta and those with a history of dental pain were more likely to have been advised by the multiprofessional team to seek dental treatment. Full article

Biofouling, the accumulation of marine organisms on submerged surfaces, presents a significant challenge to the design, performance, and maintenance of offshore wind turbines (OWTs). This work synthesizes current knowledge on the physical and operational impacts of biofouling on OWT marine substructures, with a particular focus on how it alters hydrodynamic loading, increases drag and mass, and affects fatigue and structural response. Drawing from experimental studies, computational modeling, and real-world observations, this paper highlights the critical need to integrate biofouling effects into design practices. Additionally, emerging mitigation strategies are explored, including advanced antifouling materials and AI-driven monitoring systems, which offer promising solutions for long-term biofouling management. By addressing both engineering and ecological perspectives, this paper underscores the importance of developing robust, adaptive approaches to biofouling that can support the durability, reliability, and environmental sustainability of the offshore wind industry. Full article

A solar air heater (SAH) converts solar energy into heated air without causing environmental pollution. It features a low initial cost and easy maintenance due to its simple design. However, owing to air’s poor thermal conductivity, its thermal efficiency is relatively low compared to that of other solar systems. To improve its thermal performance, previous studies have aimed at either enlarging the heat transfer surface or increasing the convective heat transfer coefficient. In this study, a novel SAH with fins and sequentially placed obstacles on the fin surface—designed to achieve both surface extension through a finned channel and enhancement of the heat transfer coefficient via the obstacles—was investigated using computational fluid dynamics analysis. The results confirmed that the obstacles enhanced heat transfer performance by up to 2.602 times in the finned channel. However, the obstacles also caused a pressure loss. Therefore, the thermo-hydraulic performance was discussed, and it was concluded that the obstacles with a relative height of 0.12 and a relative pitch of 10 yielded the maximum THP values among the investigated conditions. Additionally, correlations for the Nusselt number and friction factor were derived and predicted the simulation values with good agreement. Full article

Asphalt pavement cracking represents a prevalent form of deterioration that significantly compromises road performance and safety under the combined effects of environmental factors and traffic loading. Crack sealing has emerged as a widely adopted and cost-effective preventive maintenance strategy that restores the pavement’s structural integrity and extends service life. This paper presents a systematic review of the development of crack sealing technology, conducts a comparative analysis of conventional sealing materials (including emulsified asphalt, hot-applied asphalt, polymer-modified asphalt, and rubber-modified asphalt), and examines the existing performance evaluation methodologies. Critical failure mechanisms are thoroughly investigated, including interfacial bond failure resulting from construction defects, material aging and degradation, hydrodynamic scouring effects, and thermal cycling impacts. Additionally, this review examines advanced sensing methodologies for detecting premature sealant failure, encompassing both non-destructive testing techniques and active sensing technologies utilizing intelligent crack sealing materials with embedded monitoring capabilities. Based on current research gaps, this paper identifies future research directions to guide the development of intelligent and sustainable asphalt pavement crack repair technologies. The proposed research framework provides valuable insights for researchers and practitioners seeking to improve the long-term effectiveness of pavement maintenance strategies. Full article

The global shift toward sustainable transportation has gained increasing interest, promoting the use of electric vehicles (EVs) as an environmentally friendly alternative to conventional vehicles as a result of a complex interaction between economic incentives, social dynamics, and environmental imperatives. This study is based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to understand the key factors influencing consumers’ intentions in the Sultanate of Oman toward adopting electric vehicles. It is based on a mixed methodology combining quantitative data from a questionnaire of 448 participants, analyzed using ordinal logistic regression, with qualitative thematic analysis of in-depth interviews with 18 EV owners. Its results reveal that performance expectations, trust in EV technology, and social influence are the strongest predictors of EV adoption intentions in Oman. These findings suggest that some issues related to charging infrastructure, access to maintenance services, and cost-benefit ratio are key considerations that influence consumers’ intention to accept and use EVs. Conversely, recreational motivation is not a statistically significant factor, which suggests that consumers focus on practical and economic motivations when deciding to adopt EVs rather than on their enjoyment of driving the vehicle. The findings of this study provide valuable insights for decision-makers and practitioners to understand public perceptions of electric vehicles, enabling them to design effective strategies to promote the adoption of these vehicles in the emerging sustainable transportation market of the future. Full article

Domestic wastewater discharge is the major source of pollution in Malaysia. Phytoremediation under hydroponic conditions was initiated to treat domestic wastewater and, at the same time, to resolve the space limitation issue by installing a hydroponic system in vertical space at the site. Water hyacinth (WH) was selected in this study to identify its performance of water hyacinth in removing nutrients in raw sewage under batch operation. In the batch experiment, the ratio of COD_initial/plant_initial was identified, and SPSS ANOVA analysis shows that the number of plant size factors was not statistically different in this study. Therefore, four WH, each with an initial weight of 60 ± 20 g, were recommended for this study. Throughout the 10 days of the batch experiment, the average of COD, BOD, TSS, TP, NH4, and color removal was 73%, 73%, 86%, 79%, 77%, and 54%, respectively. The WH biomass weight increased by an average of 78%. The plants have also improved the DO level from 0.24 mg/L to 4.88 mg/L. However, the pH of effluent decreased from pH 7.05 to pH 4.88 below the sewage Standard B discharge limit of pH 9–pH 5.50. Four WH plant groups were recommended for future study, as the COD removal among the other plant groups is not a statistically significant difference (p < 0.05). Furthermore, the lower plant biomass is preferable for the high pollutant removal performance due to the fact that it can reduce the maintenance and operating costs. Full article