Background Acupuncture is increasingly utilized to address anxiety and depression in polycystic ovary syndrome (PCOS), yet evidence for non-pharmacological interventions remains limited. This study aimed to rigorously evaluate the efficacy and safety of acupuncture in alleviating anxiety and depression among women with PCOS, while exploring its potential mechanisms. Methods Eight Chinese/English databases (CNKI, Web of Science, PubMed, Embase, etc.) were searched from inception to March 1, 2025. Two investigators independently screened studies, extracted data, and assessed quality via the Cochrane risk-of-bias tool. The meta-analyses were performed with RevMan 5.4. Additionally, data mining methods were used, including frequency statistics to analyze the frequency of acupuncture points and the meridians involved. Results Twelve RCTs ( n = 2,127 patients; acupuncture = 1,059, control = 1,068) were included. Compared with the control, acupuncture significantly reduced anxiety scores [MD = −6.42, 95% CI (−8.91, −3.56); p < 0.00001] and depression scores [MD = −5.89, 95% CI (−9.01, −2.78); p = 0.0002] versus controls. Acupuncture also improved testosterone [MD = −0.05, 95% CI (−0.11, 0.00); p = 0.05], BMI [MD = −0.70, 95% CI (−1.19, −0.21); p = 0.005], and the waist-hip ratio [MD = −0.06, 95% CI (−0.11, −0.01); p = 0.03], with no significant adverse effects [OR = 0.08, 95% CI (0.01, 0.81); p = 0.03]. The effects on insulin resistance were not significant [MD = −0.41, 95% CI (−1.18, 0.37); p = 0.31]. Data mining revealed that Foot Taiyin Spleen Meridian (SP), Conception Vessel (CV), and Foot Yangming Stomach Meridian (ST) were the most frequently used, and the most commonly used combination of points included SP6, LR3, and ST36. Conclusion Acupuncture, particularly manual and short-term protocols, is a safe and effective adjunct for reducing anxiety and depression in PCOS. These benefits may be mediated via modulation of androgen levels, adiposity, and neuroendocrine pathways. Nevertheless, conclusions are limited by sample size, methodological heterogeneity, and inadequate adverse event reporting. Higher-quality RCTs are needed to confirm the safety and efficacy of these methods. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251000646 , Identifier CRD420251000646.

The prevalence of massive, multi-scale, high-dimensional, and dynamic data sets resulting from advances in information and network communication technologies is frequently hampered by data incompleteness, a consequence of complex network structures and constrained sensor capabilities. The necessity of complete data for effective data analysis and mining mandates robust preprocessing techniques. This comprehensive survey systematically reviews missing value interpolation methodologies specifically tailored for time series flow network data, organizing them into four principal categories: classical statistical algorithms, matrix/tensor-based interpolation methods, nearest-neighbor-weighted methods, and deep learning generative models. We detail the evolution and technical underpinnings of diverse approaches, including mean imputation, the ARMA family, matrix factorization, KNN variants, and the latest deep generative paradigms such as GANs, VAEs, normalizing flows, autoregressive models, diffusion probabilistic models, causal generative models, and reinforcement learning generative models. By delineating the strengths and weaknesses across these categories, this survey establishes a structured foundation and offers a forward-looking perspective on state-of-the-art techniques for missing data generation and imputation in complex networks.

The implementation of the Reduce, Reuse, and Recycle (3R) program in educational institutions plays a strategic role in fostering environmental awareness from an early age; however, its evaluation has often relied on descriptive approaches rather than objective data-driven analysis. This study aims to analyze the level of success of the 3R program implementation in schools and to identify the key factors influencing its success using a data mining approach with the C4.5 algorithm. A quantitative descriptive-analytic method was employed, utilizing primary data collected through observation and documentation of 3R program activities in schools. The data analysis followed the knowledge discovery in databases (KDD) process, including data selection, preprocessing, transformation, modeling, and evaluation. The results indicate that the C4.5 algorithm achieved a classification accuracy of 98.94%, demonstrating excellent model performance. The generated decision tree reveals that student participation is the most influential factor in determining the success of the 3R program, followed by parental involvement and teacher support. These findings suggest that the success of the 3R program is not solely determined by school policies, but largely depends on the active participation of key educational stakeholders. This study provides practical implications for schools and policymakers by offering a data-driven evaluation model that supports more objective decision-making and promotes the integration of environmental programs into the learning process within educational institutions.

Retraction Note: Analysis of transmission line icing prediction based on CNN and data mining technology

Personality is a fundamental basis of human behaviour. At most basic, personality including patterns of thought, feeling, behaviours that make an individual unique. Personality will directly or indirectly influence the interaction or preferences of a person. This research using different learning algorithms and concepts of data mining to mine on the data features and learn from the pattern. The aim of this experiment is to explore different options of the algorithm on modifying the personality prediction source code by using logistic regression algorithm, and to find whether the accuracy of the classification can be improved. There are five characteristics of different people that are known as the Big Five characteristic, which is openness, neuroticism, conscientiousness, agreeableness and extraversion that have been stored in the dataset used for training. Then, an overview and comparison will be provided on the different measures taken to reduce the issues faced by researchers in this field. Classification methods implemented are Support Vector Machine, Ridge Algorithm, Naive Bayes, Logistic Regression and Voting Classifier. Testing results showed that the Logistic Regression still outperformed the other methods.

ABSTRACT With more than two billion devices in use worldwide, Android devices have become favorite targets for cybercriminals. Kaspersky recently stated that mobile malwares mainly came from Android devices. Therefore, developing effective methods for detecting android malwares turn out to be an urgent need. Intelligent methods such as Data Mining and Machine Learning proved their merits in developing malware detection models and tools in different cybersecurity domains. However, as an established fact, Data Mining and Machine Learning models significantly affected by the quality of the training dataset. The number of features in the dataset plays an important role in developing intelligent models that balance discriminative power and low computational costs during both the training phase and the implementation (prediction) phase. In this research, we introduce a novel hybrid metaheuristic feature selection algorithm that leverages the exploration capacity of the Firefly Algorithm and the extrapolation capability of the Binary Bat Algorithm. Such an algorithm is called Binary Firefly Bat Algorithm (BFBA). In order to assess the performance of BFBA, a dataset containing an equal number of malware and benign Android applications is collected from different dependable sources. An initial feature set of 6292 attributes derived from API calls, opcodes, permissions, intents, and system commands was produced through static reverse engineering and analysis. Preliminary feature engineering using discrimination scoring and variance-threshold filtering reduced the feature space to 545 attributes while preserving discriminative information. Later, Random Forest and Support Vector Machine classifiers were trained using selected feature subsets produced by BFBA. Experimental results show that models created using BFBA-selected feature outperformed the models created using the feature subsets produced by several well-known metaheuristics like the Flower Pollination Algorithm, Grasshopper Optimization Algorithm, and Ant Colony Optimization. Such results confirm that exploration and exploitation were traded at an optimal trade-off in BFBA. Overall, experiments confirmed that BFBA positively participated in developing robust and efficient Android malware detection systems.

The regenerative potential of developing cortical axons depends on intrinsic mechanisms, such as axon-autonomous protein synthesis, that are still not fully understood. An emerging factor in this regenerative response is the bidirectional interplay between microtubule dynamics and the axonal ER. We hypothesize that locally synthesized ER proteins regulate microtubule dynamics and the regeneration of cortical axons. RNA data mining identified the ER-shaping protein Reticulon-1 as a relevant candidate across eight axonal transcriptomes. Using microfluidics, we show that axonal treatment with a small RNA against Reticulon-1 mRNA (Reticulon-1 knockdown) increases outgrowth of injured cortical axons while reducing their tubulin levels. We show by live-cell imaging that axonal Reticulon-1 knockdown increases microtubule growth rate in noninjured axons and restores this parameter after injury. Axonal inhibition of the microtubule-severing protein Spastin prevents the effects of Reticulon-1 knockdown over tubulin levels and outgrowth. We provide evidence that the Reticulon-1C isoform is synthesized within axons and attenuates Spastin-mediated microtubule severing. These findings support a model in which axonal protein synthesis regulates microtubule dynamics and axon outgrowth after injury.

Maintenance and repair play a crucial role in industry. Smart systems for technical diagnostics can help to save money and to prevent the breakdown of machines and plants. These systems and its classifiers benefit from plausible features because they tend toward robust classification. Although concepts for knowledge discovery are well-known in various scientific fields, they are not established in the field of rotating machines. Knowledge discovery from experimental data is a framework that combines valid methods for knowledge discovery with expert knowledge and automated experiments. For the central data mining step, feature selection algorithms based on heuristic or meta-heuristic search are established. The objective is to identify plausible pattern with a limited number of features and the best combination of these features. The results in this work show which strategies align the best with the requirements of knowledge discovery using experimental data to find plausible features. For this study, well-configured search strategies, namely, sequential forward selection and ant colony optimization, were applied on real data. The data represent several fault severity levels for parallel misalignment and cavitation. The plausible feature vectors and features exhibited good behavior when applied to new targets. It is expected that the obtained knowledge will be transferable to new classification tasks with only minimal optimization of the reference data or the classifier.

Maintaining color constancy in polymer extrusion processes is a key difficulty in manufacturing applications, as fluctuations in processing parameters greatly influence pigment dispersion and the quality of the finished product. Preliminary historical data mining analysis was conducted in 2009. This work concentrates on Opaque PC Grade 5, which constituted 2.43% of the pigment; it contained 10 PPH of resin2 with a Melt Flow Index (MFI) of 6.5 g/10 min and 90 PPH of resin1. It also employs a fixed resin composition with an MFI of 25 g/10 min. This research identified the significant processing parameters (PPs) contributing to the lowest color deviation. Interactions between processing parameters, for the same color formulation, were analyzed using statistical methods under various processing conditions. A principle-driven General Trends (GT) diagnostic procedure was applied, wherein each parameter was individually varied across five levels while holding others constant. Particle size distribution (PSD) and colorimetric data (CIE Lab*) were systematically measured and analyzed. To complete this, correlations for the impact of temperature (Temp) on viscosity, particle characteristics, and color quality were studied by characterizing viscosity, Digital Optical Microscopy (DOM), and particle size distribution at various speeds. The samples were characterized for viscosity at three temperatures (230, 255, 280 °C) and particle size distribution at three speeds: 700, 750, 800 rpm. This study investigates particle processing features, such as screw speed and pigment size distribution. The average pigment diameter and the fraction of small particles were influenced by the speed of 700–775 rpm. At 700 rpm, the mean particle size was 2.4 µm, with 61.3% constituting particle numbers. The mean particle size diminished to 2 µm at 775 rpm; however, the particle count proportion escalated to 66% at 800 rpm. This research ultimately quantifies the relative influence of particle size on the reaction, resulting in a color value of 1.36. The mean particle size and particle counts are positively correlated; thus, reduced pigment size at increased speed influences color response and quality. The weighted contributions of the particles, 51.4% at 700 rpm and 48.6% at 800 rpm, substantiate the hypothesis. Further studies will broaden the GT analysis to encompass multi-parameter interactions through design experiments and will test the diagnostic assessment procedure across various polymer grades and colorants to create robust models of prediction for industrial growth. The global quality of mixing polycarbonate compounding constituents ensured consistent and smooth pigment dispersion, minimizing color streaks and resulting in a significant improvement in color matching for opaque grades.

Discipline is a crucial factor influencing the effectiveness of learning processes and the quality of graduates in vocational education. SMK Swasta RK Bintang Timur Pematangsiantar maintains records of student attendance and academic performance that have the potential to be analyzed as indicators of student discipline. However, these data have not been optimally utilized as a basis for decision-making to provide early detection of students who are at risk of declining discipline. This research aims to develop a predictive model of student discipline by identifying patterns of attendance and academic achievement using a data mining approach.The study employs the CRISP-DM framework, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset includes daily attendance records, semester academic grades, and documented disciplinary behavior used as class labels. Several classification algorithms—Decision Tree (C4.5), KNN, Naive Bayes were implemented to compare model performance. Model evaluation was conducted using confusion matrix, accuracy, precision, recall, and F1-score, with k fold cross-validation.The results show that attendance and academic performance patterns significantly influence the prediction of student discipline levels. The Random Forest algorithm produced the highest performance results, with consistent F1-scores for at-risk student categories. The most influential features include attendance percentage, the number of unexcused absences, and average academic scores. The resulting model is implemented as a decision support prototype dashboard to assist counseling teachers and homeroom teachers in monitoring potential disciplinary violations and planning early intervention. This research is expected to support the development of data-driven discipline monitoring systems in schools and provide practical benefit in preventive actions to improve student behavior quality at SMK Swasta RK Bintang Timur Pematangsiantar.

The effect of pre-pandemic regular physical exercise on public mental health at the outbreak of emerging infectious diseases: Evidence from the COVID-19 pandemic using China Baidu index big data.

Epithelial ovarian cancer (EOC) has a high rate of incidence and mortality, seriously threatening women’s health. Artificial intelligence (AI) possesses functions such as image recognition, data mining and pattern recognition, which can solve problems that traditional statistical methods cannot handle, such as large amounts of data and data missing. It has achieved breakthrough progress in the fields of risk prediction, diagnosis, treatment and response assessment of malignant tumors. Most AI technologies are mainly applied in the preoperative diagnosis of EOC, as well as in imaging and pathological genomics. However, their application in treatment and prognosis assessment studies is relatively limited. This article reviews the AI application in the treatment and prognosis assessment of EOC in recent years, including the establishment of prediction models for complete cytoreduction (R0 resection), the prediction of chemotherapy and targeted drug efficacy, and the application of different AI technologies based on pathology, radiomics, and clinical data for the prognosis assessment of EOC, with the aim of providing more ideas for the application of AI in EOC.

The integration of advanced technologies in healthcare has opened new pathways for improving disease detection and diagnosis. Among the others, breast cancer (BC) indeed is one of the biggest concerns worldwide, and its early and precise diagnosis is indispensable. The present article gives an overview of the introduction of modern machinery such as ML, DL, IoT, blockchain, cloud computing, and data mining to aid the detection of breast cancer. By systematically reviewing the contemporary literature, we point out the advances accomplished in the application of AI-assisted techniques in the monitoring and diagnosing of breast cancer, whilst at the same time discussing the ongoing issues. Several ML algorithms are examined in detail, whereas the spotlight is on deep learning approaches such as CNNs and RNNs, which are known to be very effective in interpreting medical images. We examine the performance of models such as CNNs (both self-trained and those employing transfer learning), SPWO-based Deep Maxout networks, ShCNN (utilizing FACS features), and GRU-based RNNs across different publicly available datasets. Our findings aim to provide valuable insights for researchers and healthcare professionals by outlining current trends and evaluating the effectiveness of AI-based approaches. The review emphasizes the growing role of intelligent systems in supporting early diagnosis and improving treatment planning for breast cancer patients.

Retraction Note: Research and application of GIS and data mining technology in monitoring and assessment of natural geography environment

ABSTRACTBackgroundThere remains a gap in understanding lapatinib's real-world safety, particularly in rare adverse events (AEs). Thus, this study aims to evaluate lapatinib’s safety by (1) performing data mining of the FDA Adverse Event Reporting System (FAERS); and (2) detecting and analysing safety signals associated with lapatinib that may require monitoring.MethodsFAERS data from March 2007 to July 2024 were analysed via OpenVigil (version 2.1). AEs were categorised into preferred terms (PTs) and system organ classes (SOCs) using the Medical Dictionary for Regulatory Activities. We used descriptive analysis to analyse report characteristics and four signal detection algorithms to quantify risk signals, including Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN). Top novel strong suspected AEs were further assessed using a case-by-case analysis. The Naranjo algorithm was utilised to determine the potential relation between the suspected AEs and lapatinib.ResultsFrom 25,506,744 retrieved reports, 18,407 PTs identified lapatinib as the primary suspect, resulting in 10,959 signals analysed. AEs were predominantly females (77.9%) and individuals aged 18–64 (45.38%). Lapatinib-induced AEs affected 16 systems, with 155 lapatinib-related PTs; 115 of these were significantly disproportionate, including 57 new PTs. While gastrointestinal and dermatological disorders were the most common, the latter was more strongly associated with lapatinib, with diarrhoea being the only strong gastrointestinal signal. Notably, cardiac events were less reported, and the top new AEs based on signal strength, such as hypocapnia, lip ulceration, and hepatic infection, were mostly found to be ‘possibly’ related to lapatinib based on the case-by-case evaluation, warranting further clinical assessments. Initial or prolonged hospitalisation, death, and life-threatening events were the most common AEs outcomes reported, accounting for 28.79%, 13.79%, and 3.06%, respectively.ConclusionThis study provides valuable insights into lapatinib-induced toxicity in real-world settings.

The aerodynamic performance of multi-stage axial flow turbines is critical for advancing gas turbine efficiency and reducing carbon emissions. However, conventional optimization approaches rely heavily on computationally expensive CFD simulations, limiting their practicality for multistage systems. This study develops and validates a two-round optimization framework that integrates a rapid meanline solver, a multi-objective genetic algorithm, and multiple data mining techniques. In the first-round, all design variables are optimized using the meanline model, generating a comprehensive design space. Four complementary data mining methods—DACE-Kriging, parallel coordinate analysis, self-organizing maps, and total variation analysis—are then applied to identify and cross-validate the most influential variables. A second-round optimization is subsequently performed, restricted to these key variables. The methodology is demonstrated on a four-stage axial turbine, with performance improvements validated by CFD simulations. Results show a 1.34% efficiency gain from the first-round optimization and an additional 0.89% from the second, yielding a total improvement of 2.24% over the baseline design. Remarkably, the entire optimization process is completed within 2 days, compared with the months required by traditional CFD-based methods. These findings highlight the novelty and effectiveness of integrating data mining into meanline-based optimization, offering a robust, efficient, and generalizable tool for turbine design.

IntroductionSalivary gland carcinomas (SGC) are rare tumours. The term SGC is not more than an umbrella for a variety of histogenetically, morphologically and biologically distinct entities. Accordingly, SGCs have not been sufficiently investigated to date. Their rarity makes it difficult to reach high patient numbers for individual entities in clinical studies, leading to pooling patients with different histological subtypes to attain sufficient participants. The different histological subtypes of SGC differ significantly in their clinicopathological features, such as their grading, their occurrence and their outcome. SGCs are usually stratified into low-grade, intermediate-grade or high-grade tumours. In most kinds of SGC, specific targetable molecular markers are lacking. The inclusion of immunotherapy (IT), however, might improve the outcome of patients suffering from high-grade SGCs. In order to integrate IT as a therapeutic option for SGC and to facilitate therapeutic decisions based on tumour (immune) biology, predictive and prognostic immunological biomarkers are indispensable.Methods and analysisIn this prospective study, 500 patients will be enrolled, who are distributed in three arms. The observational cohort includes patients with malignant salivary gland tumours, whereas patients with benign tumours of a salivary gland are grouped in the control group 1. In the control cohort, 2 patients do not have a salivary gland tumour but have a planned functional surgery of the nose or ear or a maxillofacial surgery. The local immune status from the tumour tissue and the microbiome will be sampled before treatment. In addition, the systemic immune status from peripheral blood will be analysed before and after surgery and after the adjuvant and definitive chemoradiotherapy, if applicable. Clinical baseline characteristics and outcome parameters will additionally be collected. Data mining and modelling approaches will finally be applied to identify interactions of local and systemic immune parameters and to define predictive and prognostic immune signatures based on the evaluated immune markers.Ethics and disseminationApproval from the institutional review board of the Friedrich-Alexander-Universität Erlangen-Nürnberg was granted in September 2023 (application number 23-292-B). The results will be disseminated to the scientific audience and the general public via presentations at conferences and publication in peer-reviewed journals.Trial registration numberNCT06047236.

This study developed and evaluated data mining models for age estimation and legal age thresholds classification based on elbow MRI in a contemporary Chinese population. A total of 867 patients (614 males and 253 females) aged 30 years or younger were retrospectively included, and T1-weighted coronal elbow MRI images were assessed by experts using five age-related indicators. Multiple data mining models were constructed, with mean absolute error (MAE) as the primary performance metric. Both intra- and inter-observer agreements demonstrated good reliability. In external validation, the optimal model achieved the lowest MAE of 2.9845 years in males and 2.9767 years in females, compared to 2.0471 years and 3.0477 years, respectively, in internal validation. For classification at the 12-, 14-, 16-, and 18-year threshold, the area under the receiver operating characteristic curve (AUC) exceeded 0.90 across all models. Notably, these models also attained the highest specificity (defined as the proportion of individuals truly below the threshold correctly identified) reaching 1.000, 1.000, 0.970, and 0.800 in males, and 1.000, 1.000, 0.857, and 0.750 in females at the respective thresholds. In conclusion, the proposed models show promise in estimating age and classifying legal thresholds, with performance that is generally comparable to that of other MRI-based approaches in certain contexts. These preliminary findings suggest that elbow MRI may serve as a useful tool for age assessment in the Chinese population, though further validation is warranted to confirm its generalizability.

This paper presents IB-DARP (Iteration Balancing—Divide Areas Routing Problem), an enhanced multi-vessel cooperative mission and path planning method designed to address the limitations of traditional approaches, including uneven task allocation, workload imbalance, and path conflicts. The proposed method integrates four key mechanisms to improve planning robustness and computational efficiency. A historical data mining mechanism is first employed to extract stable navigation patterns from accumulated vessel trajectories and construct a high-confidence maritime route network. Based on this network, a precomputation mechanism significantly reduces planning-stage computational complexity by calculating essential inter-node distances in advance. A heading-aware partitioning mechanism further decomposes the multi-vessel planning problem into tractable single-vessel subproblems, while an iterative auction–equilibrium mechanism dynamically adjusts task assignments to enhance global load balance and suppress conflicts. To evaluate the effectiveness of IB-DARP, comprehensive ablation studies and large-scale scenario experiments were conducted, demonstrating its advantages in mission allocation, conflict mitigation, and cooperative path optimization. The results confirm that IB-DARP provides a scalable and efficient solution for multi-vessel cooperative mission planning in complex maritime environments.

Trastuzumab, the first anti-human epidermal growth factor receptor 2 (HER2)-targeted drug, is limited by adverse drug events (ADEs). The next-generation antibody-drug conjugates trastuzumab deruxtecan (T-DXd) and trastuzumab emtansine (T-DM1) exhibit enhanced efficacy and safety profiles compared with trastuzumab. In this study, we utilized US Food and Drug Administration Adverse Event Reporting System (FAERS) data to compare the ADEs of all three drugs, to facilitate personalized clinical decision-making and targeted monitoring. ADE reports for patients with breast cancer using trastuzumab, T-DXd, or T-DM1 were retrieved. ADEs were classified using preferred terms (PT), standardized MedDRA queries (SMQs), and system organ classes (SOCs). Data mining using reported odds ratio (ROR), proportional reporting ratio, Bayesian confidence propagation neural network (BCPNN), and multi-item gamma-Poisson shrinker was conducted. Overall, 20,829 cases of trastuzumab, 4565 cases of T-DXd, and 2975 cases of T-DM1 were included. With regard to SMQ terms, trastuzumab had a higher signal intensity for cardiac toxicity, and the RORs for "cardiomyopathy" of trastuzumab, T-DXd and T-DM1 were 8.59, 1.33, and 1.84, respectively. Meanwhile, T-DXd showed stronger signals for lung toxicity and T-DM1 showed prominent hepatotoxicity signals. Based on the differences between trastuzumab, T-DXd and T-DM1, this study established an individualized medication selection flowchart. This study applied four algorithms to analyze and compare ADEs associated with trastuzumab, T-DXd, or T-DM1. By integrating multi-level analysis including PT, SMQ, and SOC, this study provides a comprehensive safety perspective to guide clinical decision-making and medication monitoring for patients with breast cancer receiving HER2-targeted therapy.