Search Results (97)

To address the challenges of model uncertainty, strong nonlinearities, and controller tuning in high-precision trajectory tracking for hydraulic servo systems, this paper proposes a hierarchical GA-PID-MPC fusion strategy. The architecture integrates three functional layers: a Genetic Algorithm (GA) for online parameter optimization, a Model Predictive Controller (MPC) for future-oriented planning, and a Proportional–Integral–Derivative (PID) controller for fast feedback correction. These modules are dynamically coordinated through an adaptive cost-aware blending mechanism based on real-time performance evaluation. The MPC module operates on a linearized state–space model and performs receding-horizon control with weights and horizon length $\theta =[q,r,T_p]$ tuned by GA. In parallel, the PID controller is enhanced with online gain projection to mitigate nonlinear effects. The blending coefficient $\sigma \left(t\right)$ is adaptively updated to balance predictive accuracy and real-time responsiveness, forming a robust single-loop controller. Rigorous theoretical analysis establishes global input-to-state stability and $H_{\infty }$ performance under average dwell-time constraints. Full article

The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a reduction in corrective maintenance and safety-related failures, an increase in productivity and reliability and a reduction in maintenance costs. Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. The instantaneous reliability of ceramic plates is then used in the online prediction of the remaining useful life of the components. The model obtained from the instantaneous reading of 12 sensors led to the estimation of the remaining useful life for ceramic plates with up to 5600 h of use, allowing the adoption of a strategy of replacing these components by condition instead of replacing them by a fixed time, leading to increased process reliability and improved stock planning. The linear regression model for reliability prediction had an R² of 78.32%, whereas the random forest regression model had an R² of 63.7%. The final model for predicting the remaining useful life had an R² of 99.6%. Full article

To address the issue of coating accumulation model failure in unstable environments, this paper proposes a dynamic control method based on visual differential feedback. An image difference model is constructed through online image data modeling and real-time reference image feedback, enabling real-time correction of the coating accumulation model. Firstly, by combining the Arrhenius equation and the Hagen–Poiseuille equation, it is demonstrated that pressure regulation and temperature changes are equivalent under dataset establishment conditions, thereby reducing data collection costs. Secondly, online paint mist image acquisition and processing technology enables real-time modeling, overcoming the limitations of traditional offline methods. This approach reduces modeling time to less than 4 min, enhancing real-time parameter adjustability. Thirdly, an image difference model employing a CNN + MLP structure, combined with feature fusion and optimization strategies, achieved high prediction accuracy: R² > 0.999, RMSE < 0.79 kPa, and σ_e < 0.74 kPa on the test set for paint A; and R² > 0.997, RMSE < 0.67 kPa, and σ_e < 0.66 kPa on the test set for aviation paint B. The results show that the model can achieve good dynamic regulation for both types of typical aviation paint used in the experiment: high-viscosity polyurethane enamel (paint A, viscosity 22 s at 25 °C) and epoxy polyamide primer (paint B, viscosity 18 s at 25 °C). In summary, the image difference model can achieve dynamic regulation of the coating accumulation model in unstable environments, ensuring the stability of the coating accumulation model. This technology can be widely applied in industrial spraying scenarios with high requirements for coating uniformity and stability, especially in occasions with significant fluctuations in environmental parameters or complex process conditions, and has important engineering application value. Full article

The direct-drive turntable serves as the core actuator in high-precision macro-micro drive systems, where its positioning accuracy fundamentally determines overall system performance. Accurate error prediction and compensation technology represent a critical prerequisite for achieving continuous error compensation and predictive control in direct-drive turntables, making research on positioning error modeling, prediction, and compensation of vital importance. This study presents a dynamic continuous error compensation model for direct-drive turntables, based on an analysis of positioning error mechanisms and the implementation of a “decomposition-modeling-integration-correction” strategy, which features high flexibility, adaptability, and online prediction-correction capabilities. Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. Validation results demonstrate substantial performance improvements, with compensated positioning error ranges reduced from [−31.83″, 41.59″] to [−15.09″, 12.07″] (test set) and from [−22.50″, 9.15″] to [−8.15″, 8.56″] (extrapolation test set), corresponding to standard deviation reductions of 71.2% and 61.6%, respectively. These findings conclusively establish the method’s effectiveness in significantly enhancing accuracy while maintaining prediction stability and operational efficiency, underscoring its considerable theoretical and practical value for error compensation in precision mechanical systems. Full article

Willingness to pay (WTP) measurements often contain a hypothetical bias (HB) when participants’ responses result from ‘fictitious’ survey scenarios rather than actual purchasing behavior or field studies. This discrepancy usually leads to inaccurate WTP values, which affect pricing strategies. Our quantitative online survey with German consumers (N = 215) examines the HB of WTP for different mobile phone plans as an example of a widespread consumer good. The aim is to focus on the correlation between hypothetical and actual WTP and the influence of socio-demographic factors on the HB. We used the Certainty Approach to correct hypothetical WTP data to reflect actual payment behavior. The findings show that hypothetical WTP values are generally higher than current expenditure, which demonstrates that HB significantly affects WTP measurements in the context of mobile communications products. The applied Certainty Approach successfully reduced this discrepancy. We found a moderate negative correlation between actual WTP and the extent of the HB, indicating that higher actual WTP is associated with lower bias. Moreover, socio-demographic factors such as age and income do not significantly influence the HB. This study suggests pricing strategies should consider HB-adjusted WTP values to avoid management decisions based on inflated hypothetical data. Full article

Background: Despite the significant global disease burden associated with HPV infection, the vaccination coverage among female college students in China remains suboptimal. This study aimed to examine HPV vaccination coverage, knowledge levels, and determinants influencing vaccination behavior among female college students in northern China, utilizing the Health Belief Model (HBM) as a theoretical framework. Methods: A cross-sectional online survey was conducted from December 2024 to January 2025, involving 4076 female students from six universities in Jinan, China. The participants were categorized into three groups: vaccinated (VG), willing-to-vaccinate (WTG), and unwilling-to-vaccinate (UTG). Data on sociodemographic characteristics, HPV knowledge, health beliefs, and vaccination behavior were analyzed using ANOVA, chi-square tests, correlation analysis, and multivariate logistic regression. Results: The vaccination rate was 18.11%, with 40.19% expressing willingness to vaccinate and 41.71% expressing unwillingness. Vaccinated students demonstrated higher levels of HPV knowledge (6.66 ± 2.67 compared to 4.76 ± 3.10 in the UTG, p < 0.001) and were predominantly from urban areas (OR = 0.64, p < 0.001). The key determinants of vaccination uptake included perceived benefits (OR = 1.54, p < 0.001), perceived barriers (OR = 3.34, p < 0.001), self-decision-making ability (OR = 1.80, p < 0.001), and social motivation (OR = 0.21, p < 0.001). Notably, increased knowledge was associated with vaccine hesitancy in the WTG group (OR = 0.45, p < 0.001), indicating that information overload may adversely affect decision-making processes. Structural barriers, such as cost (42.63%), safety concerns (46.59%), and misconceptions (e.g., 57.76% cited “no sexual activity” as a reason for refusal), significantly impeded vaccine uptake. Conclusions: The low coverage of HPV vaccination is indicative of deficiencies in knowledge, socioeconomic disparities, and cultural perceptions. Tailored interventions should focus on educational efforts to correct misconceptions, provide subsidized access to vaccines, and implement empowerment strategies that enhance self-efficacy and informed decision-making. Policymakers should incorporate these findings into national cervical cancer prevention programs to address the gap between vaccination intention and behavior among young women in China. Full article

To address the issue of control inaccuracy caused by the zero-offset fault in current sensors within the multiphase brushless DC motor (BLDCM) drive system, this paper proposes a fault diagnosis and fault-tolerant control method based on current signals. Different from traditional solutions that rely on hardware redundancy or precise modeling, this method constructs a dual-channel fault diagnosis framework by integrating the steady-state amplitude offset of the phase current after the fault and the abnormal characteristics of dynamic sector switching. Firstly, sliding time window monitoring is used to identify steady-state amplitude anomalies and locate faulty sectors. Subsequently, an algorithm for detecting the difference in current changes during sector switching is designed, and a logic interlocking verification mechanism is combined to eliminate false triggering and accurately locate single or multiple fault phases. Furthermore, based on the diagnostic information, a repeated iterative online correction method is adopted to restore the accuracy of the current measurement. This method only relies on phase current signals and rotor position information, does not require additional hardware support or accurate system models, and is not affected by the nonlinear characteristics of the motor. Finally, the experimental verification was carried out on a nine-phase BLDCM drive system. Experimental results indicate that the torque fluctuation of the system can be controlled within 5% through the fault-tolerant control strategy. Full article

Background: Dietary supplements (DSs) are widely used to complement diets, particularly among individuals with specific nutritional needs. While DSs can promote health and prevent disease, improper use may lead to adverse effects or medication interactions, highlighting the need for safe, informed consumption. This study assesses knowledge, attitudes, and practices (KAP) related to DSs in Western Saudi Arabia, providing insights into national consumption patterns. Method: A cross-sectional online survey was conducted between December 2023 and February 2024. A 28-item questionnaire covering demographics, knowledge, attitudes, and practices regarding DSs was administered. Participants aged 18 years and older residing in the study regions were recruited through convenience sampling. Data were analyzed with SPSS version 22 using descriptive statistics and chi-square tests. Results: A total of 1006 participants completed the survey, with 70.5% (n = 709) reporting DS use. Most respondents were female (71.8%) and 34.3% were aged between 18 and 30 years. Although 82.5% were aware of DSs, only 41% knew the correct dosages, and 30.3% expressed uncertainty about DS safety. About 28.1% of DS users experienced side effects. Multivitamins were the most commonly used DS type (44.9%), followed by mono-vitamins (39.9%) and minerals (7.5%). The primary motivations for DS use were health improvement and ensuring adequate nutrition (66%), while 20% used DSs to address specific deficiencies. Most participants sourced DSs from pharmacies (85.5%), and 46.6% relied on the internet for information. Significant differences in DSs use were observed based on gender and age (p < 0.001). Conclusion: This study highlights the widespread use of DSs, with significant gaps in knowledge, attitudes, and practices concerning dosage and safety. Associations between DS use and demographic factors indicate that public health strategies should address these variables. Targeted education and clear guidance on safe DS use are essential for promoting informed consumption and reducing potential health risks. Full article

Background: Cancer diagnosis causes a range of different emotions. It is also a factor that causes feelings of severe stress. Coping with stress is individual and depends on the person’s nature, environment, and support they receive. Aim: This study aimed to assess how women diagnosed with breast cancer cope with stress caused by the disease. Methods: A total of 111 women diagnosed with breast cancer participated in the study. The questionnaires were distributed electronically using Google Forms in online forums and groups on social media. The survey consisted of two parts: the original questions and the Mini-COPE questionnaire. The relationships between stress-coping strategies and age, having children, marital status, and life satisfaction were tested. The Mann–Whitney U test, Kruskal–Wallis test, and Dunn’s post-hoc test with Bonferroni correction were used for the analyses (p < 0.05). Results: Of the surveyed women, 54.9% reported that the moment of diagnosis was the most stressful. Feelings of fear and anxiety accompanied 30.5% of the women, and 24.7% at the time of diagnosis could not provide information about the disease. The dominant strategies were seeking emotional support (mean 2.12 ± 0.56) and seeking instrumental support (mean 2.06 ± 0.48). Women in the older age group, married women, and women with children were most likely to adopt the strategy of turning to religion. Conclusions: The dominant strategies were seeking emotional and instrumental support. The strategy of turning to religion was used more often by older patients and patients with children. Full article

Background and Objectives: Cluster headaches (CHs) are one of the most painful primary headaches and negatively affect the lives of patients due to misdiagnosis. Family medicine (FM) and emergency medicine (EM) physicians are one of the most important steps in making the correct diagnosis and directing patients to headache specialists. In this study, the knowledge and management approaches of these two groups regarding CH were evaluated. Materials and Methods: Two online questionnaires were developed to gather the demographic data of physicians and to assess their knowledge about the characteristics, diagnosis, and treatment of CHs. Results: A total of 120 FM doctors and 98 EM doctors participated in this study. Answers about diagnostic criteria were similar in both groups. It was found that 70% of the participating physicians had concerns about misdiagnosing cluster headaches, and only 15% considered themselves sufficiently knowledgeable on the topic. Additionally, nearly half of the physicians were unaware that autonomic symptoms are mandatory for diagnosis and believed that NSAIDs are effective in treatment. Conclusions: In our study, for the first time, EM and FM physicians’ knowledge about the diagnosis and treatment of and professional competence in CHs was evaluated. It was found that the participants had knowledge about CHs but still considered themselves incompetent. For the correct and early diagnosis and for the proper management of CHs, EM and FM physicians, who can be called gatekeepers of CHs, need more medical education-based strategies. Full article

Background: Patient education is a key aspect of physical therapy practice; however, the differences in how experienced and novice physiotherapists perceive and apply patient education practice remain underexplored. Understanding these differences influences training approaches and improves physical therapy practice quality. This research aims to determine the difference in self-reported patient education practice between experienced and novice physiotherapists. Methods: A previously published online survey instrument was used to collect data from physiotherapists employed in public health institutions in Bosnia and Herzegovina. The survey questions included demographic characteristics and questions about approaches to patient education, perceived importance, and factors contributing to skills development. Participants were recruited in two groups: experienced physiotherapists with work experience ≥ 11 years (n = 139) and novice physiotherapists with work experience ≤ 5 years (n = 45). Descriptive statistics, such as numbers and percentages, were used to summarize participant responses. Results: Experienced physiotherapists more frequently provided advice on posture, movement correction, daily activity strategies, and pacing while addressing patient concerns and exploring perceptions (p < 0.05). In contrast, novice physiotherapists placed significantly greater value on continuing education courses, considering them an important factor in developing patient education skills (p < 0.05). Conclusions: Experienced physiotherapists prioritize patient education focusing on posture, movement, and self-care strategies compared to novice physiotherapists. However, novice physiotherapists place a higher importance role on continuous education. Identifying these differences may help tailor training and mentorship to improve physiotherapy practice, ensuring better patient outcomes. Full article

Background: Knee osteoarthritis is a prevalent condition that significantly impacts patients’ quality of life. Effective management typically involves a combination of pharmacological and non-pharmacological treatments. However, establishing a consensus on the optimal treatment strategy is crucial for standardizing care. The present study is the result of a rigorous process that combines artificial intelligence with human expertise to improve the reliability of medical recommendations. Methods: A new software platform (Butterfly Decisions, 2021, Italy) was employed to leverage AI-assisted decision-making, facilitating the digitalization of the entire consensus process. The process started with data collection through an online survey including simulated clinical cases of knee osteoarthritis collected by 30 orthopedic surgeons; artificial intelligence (AI) analyzed the collected clinical data and identified the key concepts and relevant patterns. Subsequently, AI generated detailed statements summarizing key concepts extracted from the data and proposed a reformulation of the statements to be discussed during the discussion session of the advisory board. The advisory board, composed of four qualified, experienced specialists of knee osteoarthritis, evaluated statements, providing their agreement levels, confidence, and supporting evidence. The AI tools calculated the degree of certainty and contradiction for each statement based on these evaluations. The literature was critically evaluated to ensure that there was an evidence-based evaluation of the proposed treatment statements. Finally, revised versions were proposed to address the feedback, evidence was collected to refine the scientific report, and the board members evaluated the AI performance too. Results: The consensus analysis revealed a high level of agreement in the need for a multimodal approach to treating knee osteoarthritis. The feedback highlighted the importance of integrating physical therapy and weight management, non-pharmacological methods, with Symptomatic Slow-Acting Drug for Osteoarthritis (SYSADOAs) and pharmacological treatments, such as anti-inflammatory drugs and intra-articular knee injections. The board members found that AI was easy to use and understand and each statement was structured clearly and concisely. Conclusions: The expert consensus about knee osteoarthritis conservative management being facilitated with AI met with unanimous agreement. AI-assisted decision-making was shown to have excellent analytical capabilities, but algorithms needs to be trained by orthopaedic experts with the correct inputs. Future additional efforts are still required to evaluate the incorporation of AI in clinical workflows. Full article

Introduction: The survival in early breast cancer (BC) has been significantly improved thanks to numerous new drugs. Nevertheless, the information about the need for systemic therapy, especially chemotherapy, represents an additional stress factor for patients. A common coping strategy is searching for further information, traditionally via search engines or websites, but artificial intelligence (AI) is also increasingly being used. Who provides the most reliable information is now unclear. Material and Methods: AI in the form of ChatGPT 3.5 and 4.0, Google, and the website of PINK, a provider of a prescription-based mobile health app for patients with BC, were compared to determine the validity of the statements on the five most common side effects of nineteen approved drugs and one drug with pending approval (Ribociclib) for the systemic treatment of BC. For this purpose, the drugs were divided into three groups: chemotherapy, targeted therapy, and endocrine therapy. The reference for the comparison was the prescribing information of the respective drug. A congruence score was calculated for the information on side effects: correct information (2 points), generally appropriate information (1 point), and otherwise no point. The information sources were then compared using a Friedmann test and a Bonferroni-corrected post-hoc test. Results: In the overall comparison, ChatGPT 3.5 received the best score with a congruence of 67.5%, followed by ChatGPT 4.0 with 67.0%, PINK with 59.5%, and with Google 40.0% (p < 0.001). There were also significant differences when comparing the individual subcategories, with the best congruence achieved by PINK (73.3%, p = 0.059) in the chemotherapy category, ChatGPT 4.0 (77.5%; p < 0.001) in the targeted therapy category, and ChatGPT 3.5 (p = 0.002) in the endocrine therapy category. Conclusions: Artificial intelligence and professional online information websites provide the most reliable information on the possible side effects of the systemic treatment of early breast cancer, but congruence with prescribing information is limited. The medical consultation should still be considered the best source of information. Full article

The demand for flexible automation in manufacturing has increased, incorporating vision-guided systems for object grasping. However, a key challenge is in-hand error, where discrepancies between the actual and estimated positions of an object in the robot’s gripper impact not only the grasp but also subsequent assembly stages. Corrective strategies used to compensate for misalignment can increase cycle times or rely on pre-labeled datasets, offline training, and validation processes, delaying deployment and limiting adaptability in dynamic industrial environments. Our main contribution is an online self-supervised learning method that automates data collection, training, and evaluation in real time, eliminating the need for offline processes. Building on this, our system collects real-time data during each assembly cycle, using corrective strategies to adjust the data and autonomously labeling them via a self-supervised approach. It then builds and evaluates multiple regression models through an auto machine learning implementation. The system selects the best-performing model to correct the misalignment and dynamically chooses between corrective strategies and the learned model, optimizing the cycle times and improving the performance during the cycle, without halting the production process. Our experiments show a significant reduction in the cycle time while maintaining the performance. Full article

Non-animal assessment of skin sensitization is a global trend. Recently, scientific efforts have been focused on the integration of multiple evidence for decision making with the publication of OECD Guideline No. 497 for defined approaches to skin sensitization. The integrated testing strategy (ITS) methods reported by the guideline integrates in chemico, in vitro, and in silico testing to assess both hazard and potency of skin sensitization. The incorporation of in silico methods achieved comparable performance with fewer experiments compared to the traditional two-out-of-three (2o3) method. However, the direct application of current ITSs to agrochemicals can be problematic due to the lack of agrochemicals in the training data of the incorporated in silico methods. To address the issue, we present ITS-SkinSensPred 2.0 for agrochemicals and agrochemical formulations using a reconfigured in silico model SkinSensPred for pesticides. Compared to ITSv2, the proposed ITS-SkinSensPred 2.0 achieved an 11% and 16% improvement in the accuracy and correct classification rate for hazard identification and potency classification, respectively. In addition, an online ITS tool was implemented and available on the SkinSensDB website. The tool is expected to be useful for evaluating skin sensitization of substances. Full article