Complete Data Set Research Articles

Structural connectivity at term equivalent age and language in preterm children at 2 years corrected.

We previously reported interhemispheric structural hyperconnectivity bypassing the corpus callosum in children born extremely preterm (<28 weeks) versus term children. This increased connectivity was positively associated with language performance at 4-6 years of age in our prior work. In the present study, we aim to investigate whether this extracallosal connectivity develops in extremely preterm infants at term equivalent age by leveraging a prospective cohort study of 350 very and extremely preterm infants followed longitudinally in the Cincinnati Infant Neurodevelopment Early Prediction Study. For this secondary analysis, we included only children born extremely preterm and without significant brain injury (n = 95). We use higher-order diffusion modelling to assess the degree to which extracallosal pathways are present in extremely preterm infants and predictive of later language scores at 22-26 months corrected age. We compare results obtained from two higher-order diffusion models: generalized q-sampling imaging and constrained spherical deconvolution. Advanced MRI was obtained at term equivalent age (39-44 weeks post-menstrual age). For structural connectometry analysis, we assessed the level of correlation between white matter connectivity at the whole-brain level at term equivalent age and language scores at 2 years corrected age, controlling for post-menstrual age, sex, brain abnormality score and social risk. For our constrained spherical deconvolution analyses, we performed connectivity-based fixel enhancement, using probabilistic tractography to inform statistical testing of the hypothesis that fibre metrics at term equivalent age relate to language scores at 2 years corrected age after adjusting for covariates. Ninety-five infants were extremely preterm with no significant brain injury. Of these, 53 had complete neurodevelopmental and imaging data sets that passed quality control. In the connectometry analyses adjusted for covariates and multiple comparisons (P < 0.05), the following tracks were inversely correlated with language: bilateral cerebellar white matter and middle cerebellar peduncles, bilateral corticospinal tracks, posterior commissure and the posterior inferior fronto-occipital fasciculus. No tracks from the constrained spherical deconvolution/connectivity-based fixel enhancement analyses remained significant after correction for multiple comparisons. Our findings provide critical information about the ontogeny of structural brain networks supporting language in extremely preterm children. Greater connectivity in more posterior tracks that include the cerebellum and connections to the regions of the temporal lobes at term equivalent age appears to be disadvantageous for language development.

Spatio-temporal patterns and trends in MODIS-retrieved radiative forcing by snow impurities over the Western US from 2001 to 2022

The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March–30th June) in all 22 years. We tested for long-term trend significance with the Mann–Kendall test and trend magnitude with Theil–Sen’s slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%–8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Medical school origins of award-winning psychiatrists; analysis of a complete national dataset

BackgroundBritain attracts doctors from all over the world to work in the National Health Service. Elucidating the educational backgrounds of award-winning doctors working in the country is potentially an important medical education issue and a merit award audit. Using the British clinical merit award schemes as outcome measures, we identify medical school origins of award-winning doctors who have been identified as having achieved national or international prominence.MethodsThe Clinical Excellence Awards/Distinction Awards schemes select doctors in Britain who are classified as high achievers, with categories for national prominence and above. We used this outcome measure in a quantitative observational analysis of the 2019–20 dataset of all 901 award-winning doctors. Pearson's Chi-Square test was used where appropriate.ResultsFive university medical schools (London university medical schools, Glasgow, Edinburgh, Aberdeen and Cambridge) accounted for 59.1% of the psychiatrist award-winning doctors in the 2019 round, despite the dataset representing 85 medical schools. 84.1% of the psychiatrist award-winners were from European medical schools, compared to 92.1% of the non-psychiatrist award-winners. International medical graduates accounted for 22.7% of the award-winning psychiatrists. Psychiatrists with the lower grade national awards came from a more diverse educational background of 17 medical schools. IMGs represented diverse medical schools from five continents and were most represented in the lowest grade of national merit awards at 24.2%.ConclusionsThe majority of the award-winning psychiatrists originated from only five medical schools. A greater diversity of medical school origin existed for the lowest grade national psychiatrist award-winners. International medical graduates contributed substantially to these award-winners; psychiatrist award-winners were more likely to be international medical graduates (22.7%) than non-psychiatrist award-winners (10.8%). This study not only indicates educational centres associated with the production of award-winners but also provides students with a roadmap for rational decision making when selecting medical schools.

Network Analysis of Active and Passive Radar Based on the Extended Kalman Filter

In this current work, a sequential extended Kalman filter (EKF) algorithm is proposed to solve the quandary of regional accuracy disparity for time difference of arrival (TDOA) positioning in passive radar. The proposed approach entails merging the complete set of active radar observation data with the TDOA data of passive radar in a serial fashion, followed by the deployment of the EKF algorithm for the purpose of filtering. The simulation results show that the proposed data fusion method effectively solves the regional accuracy imbalance problem caused by the fuzzy area and no solution area of the TDOA positioning solution through the conflation of the precision merits of active and passive radar, and improves the overall target detection accuracy.

Using Diverse Data Sources to Impute Missing Air Quality Data Collected in a Resource-Limited Setting

The sustainable operation of ambient air quality monitoring stations in developing countries is not always possible. Intermittent failures and breakdowns at air quality monitoring stations often affect the continuous measurement of data as required. These failures and breakdowns result in missing data. This study aimed to impute NO2, SO2, O3, and PM 10 to produce complete data sets of daily average exposures from 2010 to 2017. Models were built for (a) an individual pollutant at a monitoring station, (b) a combined model for the same pollutant from different stations, and (c) a data set with all the pollutants from all the monitoring stations. This study sought to evaluate the efficacy of the Multiple Imputation by Chain Equations (MICE) algorithm in successfully imputing air quality data that are missing at random. The application of classification and regression trees (CART) analysis using the MICE package in the R statistical programming language was compared with the predictive mean matching (PMM) method. The CART method performed better, with the pooled R-squared statistics of the imputed data ranging from 0.3 to 0.7, compared to a range of 0.02 to 0.25 for PMM. The MICE algorithm successfully resolved the incompleteness of the data. It was concluded that the CART method produced better reliable data than the PMM method. However, in this study, the pooled R2 values were accurate for NO2, but not so much for other pollutants.

Longitudinal Speech Outcome at 5 and 10 Years in UCLP: Influence of Speech Therapy and Secondary Velopharyngeal Surgery.

To investigate speech development of children aged 5 and 10 years with repaired unilateral cleft lip and palate (UCLP) and identify speech characteristics when speech proficiency is not at 'peer level' at 10 years. Estimate how the number of speech therapy visits are related to speech proficiency at 10 years, and what factors are predictive of whether a child's speech proficiency at 10 years is at 'peer level' or not. Longitudinal complete datasets from the Scandcleft project. 320 children from nine cleft palate teams in five countries, operated on with one out of four surgical methods. Secondary velopharyngeal surgery (VP-surgery) and number of speech therapy visits (ST-visits), a proxy for speech intervention. 'Peer level' of percentage of consonants correct (PCC, > 91%) and the composite score of velopharyngeal competence (VPC-Sum, 0-1). Speech proficiency improved, with only 23% of the participants at 'peer level' at 5 years, compared to 56% at 10 years. A poorer PCC score was the most sensitive marker for the 44% below 'peer level' at 10-year-of-age. The best predictor of 'peer level' speech proficiency at 10 years was speech proficiency at 5 years. A high number of ST-visits received did not improve the probability of achieving 'peer level' speech, and many children seemed to have received excessive amounts of ST-visits without substantial improvement. It is important to strive for speech at 'peer level' before age 5. Criteria for speech therapy intervention and for methods used needs to be evidence-based.

Enhancing Central Model Performance: Leveraging Federated Learning Across Virtual Machine Networks for Distributed Training and Synchronization

This project takes a closer look at federated learning as a way of achieving superior machine learning models in a distributed manner while preserving privacy in the datasets that contribute. We have modelled a network of cooperating virtual machines working collectively without explicit sharing of data. Rather than distributing the complete big dataset to each system, we have split it into chunks of 10,000, 5,000, 40,000, 5,000 entries. These systems would then work on their data with learning rates of their model's making and in the decision-making processes to modify their settings, so that the data that systems would work on could allow for building their respective models by them. What this means is that the high point in the project is the combination of these models into one overarching model. The overarching model then gets better because of the small models learning from it without having to access the data associated with the models in a direct sense. This way, a better model can be built, which will intimately understand the data and thereby predict more accurately. Taken as a whole, we have shown how federated learning can improve the models of machine learning in a significantly private manner, and thus the methodology is positively postured with respect to future related work

Expressway traffic flow prediction based on MF-TAN and STSA.

Highly accurate traffic flow prediction is essential for effectively managing traffic congestion, providing real-time travel advice, and reducing travel costs. However, traditional traffic flow prediction models often fail to fully consider the correlation and periodicity among traffic state data and rely on static network topology graphs. To solve this problem, this paper proposes a expressway traffic flow prediction model based on multi-feature spatial-temporal adaptive periodic fused graph convolutional network (MFSTAPFGCN). First, we make fine preprocessing of the raw data to construct a complete and accurate dataset. Second, by deeply investigating the correlation properties among section speed, traffic flow, and section saturation rate, we incorporate these features into a multi-feature temporal attention mechanism in order to dynamically model the correlation of traffic flow in different time periods. Next, we adopt a spatial-temporal adaptive fusion graph convolutional network to capture the daily cycle similarity and potential spatial-temporal dependence of traffic flow data. Finally, the superiority of the proposed MFSTAPFGCN model over the traditional baseline model is verified through comparative experiments on real Electronic Toll Collection (ETC) gantry transaction data, and the effectiveness of each module is demonstrated through ablation experiments.

CycleGAN-Based Data Augmentation for Subgrade Disease Detection in GPR Images with YOLOv5

Vehicle-mounted ground-penetrating radar (GPR) technology is an effective means of detecting railway subgrade diseases. However, existing methods of GPR data interpretation largely rely on manual identification, which is not only inefficient but also highly subjective. This paper proposes a semi-supervised deep learning method to identify railway subgrade diseases. This method addresses the sample imbalance problem in the defect dataset by utilizing a data augmentation method based on a generative adversarial network model. An initial network model for disease identification is obtained by training the YOLOv5 network with a small number of existing samples. The intelligently extended samples are then labeled to achieve a balance in the disease samples. The network is trained to improve the recognition accuracy of the intelligent model using a more complete dataset. The experimental results show that the accuracy of the proposed method can reach up to 94.53%, which is 23.85% higher than that of the supervised learning model without an extended dataset. This has strong industrial application value for railway subgrade disease detection as the potential learning ability of the model can be explored to a greater extent, thereby improving the recognition accuracy of subgrade diseases.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

Situational factors affecting abstinence from drugs: Panel data analysis of patients with drug use disorders in residential drug use treatment

AbstractAimTo identify situational factors that can predict drug abstention in patients with drug use disorders undergoing residential drug use treatment.MethodsPatients with drug use disorders admitted to drug addiction rehabilitation centers (DARCs) in 2016 were involved in this study. Longitudinal panel data were used, with eight follow‐up surveys over 6 years, approximately every 6 months. Of the 2752 samples from the eight follow‐up surveys, 2293 were analyzed as the complete panel data set. The primary outcome was drug abstention for approximately 6 months. The influences of situational factors during this period on the primary outcome were also assessed using a generalized linear mixed model in which inter‐individual differences were controlled as variable effects.ResultsThe use of residential DARCs positively influenced the primary outcome (adjusted odds ratio [AOR] 3.33, 95% confidence interval [CI] 1.79–6.21) when compared to no DARC usage. The cessation of drinking also positively affected the primary outcome (AOR 3.10, 95% CI 1.79–4.62), while employment status (AOR 2.22, 95% CI 1.12–4.41) and the cessation of drinking (AOR 4.92, 95% CI 2.77–8.72) positively impacted the primary outcomes of patients not using DARCs.ConclusionThe use of residential DARCs and the cessation of drinking positively affected drug abstention rates. Employment and the cessation of drinking for patients who were not using the DARCs also had a positive effect. This information will aid in the development of social recovery strategies for people with drug use disorders.

Database of aquatic macrophytes in Japanese lakes

AbstractMacrophytes, comprising aquatic vascular plants, bryophytes, and macroalgae, including charophytes, are crucial components of lake ecosystems. Their diversity affects ecosystem functions and services, and they are sensitive to environmental changes, making them effective indicators of lake health. Historical records of macrophyte species composition are essential for evaluating environmental changes and conservation goals. In this study, we compiled a comprehensive database of macrophytes, along with lake and survey information from the literature, providing a historical record of macrophyte biodiversity for lakes in Japan. This database enables the analysis of changes in the flora of Japanese lakes and fluctuations in species distribution, facilitating future monitoring. The complete dataset for this abstract published in the Data Article section of the journal is available in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2024-02.1/jalter-en.

A lower crust shear zone facilitates delamination and continental subduction under the Apennines

Physical properties and structure of the lithosphere are the first step to constrain the evolution of mountain belts. Here we show detailed shear wave velocity profiles of the lithosphere in the Apennines that clarify a controversial aspect of continental subduction: the intricate mechanism of crust delamination from the downgoing plate. From the analysis of complete and dense teleseismic Receiver Function data set, we find that the delamination of the continental lithosphere is favored by the development of a low seismic shear wave velocity zone in the middle-lower crust. We observe a double Moho below the external portions of the present mountain range, suggesting the progressive formation of the shallow interface. The delamination edge is located in the forearc, far eastward than expected, implying that the re-equilibration of the thermal unbalance, generated by the mantle substitution, may last 10-7 Myr.

Addressing the challenges of reconstructing systematic reviews datasets: a case study and a noisy label filter procedure

Systematic reviews and meta-analyses typically require significant time and effort. Machine learning models have the potential to enhance screening efficiency in these processes. To effectively evaluate such models, fully labeled datasets—detailing all records screened by humans and their labeling decisions—are imperative. This paper presents the creation of a comprehensive dataset for a systematic review of treatments for Borderline Personality Disorder, as reported by Oud et al. (2018) for running a simulation study. The authors adhered to the PRISMA guidelines and published both the search query and the list of included records, but the complete dataset with all labels was not disclosed. We replicated their search and, facing the absence of initial screening data, introduced a Noisy Label Filter (NLF) procedure using active learning to validate noisy labels. Following the NLF application, no further relevant records were found. A simulation study employing the reconstructed dataset demonstrated that active learning could reduce screening time by 82.30% compared to random reading. The paper discusses potential causes for discrepancies, provides recommendations, and introduces a decision tree to assist in reconstructing datasets for the purpose of running simulation studies.

From Posts to Knowledge: Annotating a Pandemic-Era Reddit Dataset to Navigate Mental Health Narratives

Mental illness is increasingly recognized as a substantial public health challenge worldwide. With the advent of social media, these platforms have become pivotal for individuals to express their emotions, thoughts, and experiences, thereby serving as a rich resource for mental health research. This paper is devoted to the creation of a comprehensive dataset and an innovative data annotation methodology to explore the underlying causes of these mental health issues. Our approach included the extraction of over one million Reddit posts from five different subreddits, spanning the pre-pandemic, during-pandemic, and post-pandemic periods. These posts were methodically annotated using a set of specific criteria, aimed at identifying various root causes. This rigorous process produced a richly categorized dataset, invaluable for detailed analysis. The complete unlabelled dataset, along with a subset that has been expertly annotated, is prepared for public release, as outlined in the data availability section. This dataset is a critical resource for training and fine-tuning machine learning models to identify the foundational triggers of individual mental health issues, offering valuable insights for practical interventions and future research in this domain.

Carbon dioxide and cardiac output as major contributors to cerebral oxygenation during apnoeic oxygenation

Apnoeic oxygenation has experienced a resurgence in interest in critical care and perioperative medicine. However, its effect on cerebral oxygenation and factors influencing it, have not yet been investigated in detail. By using near-infrared spectroscopy, we intended to provide further evidence for the safety of apnoeic oxygenation and to increase our understanding of the association between cerebral perfusion, haemodynamic, respiratory and demographic factors. In this secondary analysis of a prospective randomized controlled noninferiority trial, we recruited 125 patients, who underwent surgery under general anaesthesia with neuromuscular blockade. Arterial blood samples were taken every 2 min for a total of 15 min under apnoeic oxygenation with 100% oxygen. Near-infrared spectroscopy and cardiac output were continuously measured. Statistical analysis was performed using uni- and multivariable statistics. Ninety-one complete data sets were analysed. In six patients the SpO2 fell below 92% (predefined study termination criterion). The significant average increase of cerebral oxygenation was 0.5%/min and 2.1 mmHg/min for the arterial pressure of carbon dioxide (paCO2). The median cardiac output increased significantly from 5.0 l/min (IQR 4.5–6.0) to 6.5 l/min (IQR 5.7–7.5). The most significant effect on cerebral oxygenation was exhibited by the variable paCO2 and non-specific patient factors, followed by cardiac output and paO2. Apnoeic oxygenation proves to have a high safety profile while significantly increasing cerebral oxygenation, paCO2 and cardiac output. In reverse, NIRS might act as a reliable clinical surrogate of paCO2 and cardiac output during stable arterial oxygenation.

Toward Predicting Interfacial Tension of Impure and Pure CO2-Brine Systems Using Robust Correlative Approaches.

In the context of global climate change, significant attention is being directed toward renewable energy and the pivotal role of carbon capture and storage (CCS) technologies. These innovations involve secure CO2 storage in deep saline aquifers through structural and capillary processes, with the interfacial tension (IFT) of the CO2-brine system influencing the storage capacity of formations. In this study, an extensive data set of 2811 experimental data points was compiled to model the IFT of impure and pure CO2-brine systems. Three white-box machine learning (ML) methods, namely, genetic programming (GP), gene expression programming (GEP), and group method of data handling (GMDH) were employed to establish accurate mathematical correlations. Notably, the study utilized two distinct modeling approaches: one focused on impurity compositions and the other incorporating a pseudocritical temperature variable (Tcm) offering a versatile predictive tool suitable for various gas mixtures. Among the correlation methods explored, GMDH, employing five inputs, exhibited exceptional accuracy and reliability across all metrics. Its mean absolute percentage error (MAPE) values for testing, training, and complete data sets stood at 7.63, 7.31, and 7.38%, respectively. In the case of six-input models, the GEP correlation displayed the highest precision, with MAPE values of 9.30, 8.06, and 8.31% for the testing, training, and total data sets, respectively. The sensitivity and trend analyses revealed that pressure exerted the most significant impact on the IFT of CO2-brine, showcasing an adverse effect. Moreover, an impurity possessing a critical temperature below that of CO2 resulted in an elevated IFT. Consequently, this relationship leads to higher impurity concentrations aligning with lower Tcm values and subsequently elevated IFT. Also, monovalent and divalent cation molalities exhibited a growing influence on the IFT, with divalent cations exerting approximately double the influence of monovalent cations. Finally, the Leverage approach confirmed both the reliability of the experimental data and the robust statistical validity of the best correlations established in this study.

Effect of reducing electrical stimulation rate on hearing performance of Nucleus® cochlear implant recipients

Objective To evaluate whether a 500 pulses per second per channel (pps/ch) rate would provide non-inferior hearing performance compared to the 900 pps/ch rate in the Advanced Combination Encoder (ACE™) sound coding strategy. Design A repeated measures single-subject design was employed, wherein each subject served as their own control. All except one subject used 900 pps/ch at enrolment. After three weeks of using the alternative rate program, both programs were loaded into the sound processor for two more weeks of take-home use. Subjective performance, preference, words in quiet, sentences in babble, music quality, and fundamental frequency (F0) discrimination were assessed using a balanced design. Study sample Data from 18 subjects were analysed, with complete datasets available for 17 subjects. Results Non-inferior performance on all clinical measures was shown for the lower rate program. Subjects’ preference ratings were comparable for the programs, with 53% reporting no difference overall. When a preference was expressed, the 900 pps/ch condition was preferred more often. Conclusion Reducing the stimulation rate from 900 pps/ch to 500 pps/ch did not compromise the hearing outcomes evaluated in this study. A lower pulse rate in future cochlear implants could reduce power consumption, allowing for smaller batteries and processors.

Phase Selection Rules of Multi-Principal Element Alloys.

Computational prediction of phase stability of multi-principal element alloys (MPEAs) holds a lot of promise for rapid exploration of the enormous design space and autonomous discovery of superior structural and functional properties. Regardless of many plausible works that rely on phenomenological theory and machine learning, precise prediction is still limited by insufficient data and the lack of interpretability of some machine learning algorithms, e.g., convolutional neural network. In this work, a comprehensive approach is presented, encompassing the development of a complete dataset that contains 72387 density functional theory calculations, as well as a predictive global phenomenological descriptor. The phase selection descriptor, based on atomic electronegativity and valence electron concentration, significantly outperforms the widely used valence electron concentration, excelling in both accuracy (with an f1 score of 63% compared to 47%) and its ability to predict the HCP phase (0.48 recall compared to 0). The comprehensive data mining on the global design space of 61425 quaternary MPEAs made from 28 possible metals, together with the phenomenological theory and physical interpretation, will set up a solid computational science foundation for data-driven exploration of MPEAs.

Deep Learning-Based Cause-Related Marketing and the Impact of the Internet on MICE Events in the Context of the Epidemic

Since 2019, novel coronavirus pneumonia has been rampant around the world, and when outbreaks occur, Meetings, Incentives, Conferences and Exhibitions (MICE) events are often affected to varying degrees. In addition, in the context of the epidemic, consumers have increasingly taken the participation of MICE in charitable activities as a measure of their social responsibility and judged MICE events as good or bad accordingly. Therefore, the impact of deep learning-based good cause marketing and the Internet on MICE events in the context of the epidemic has attracted much attention. Based on the CiteSpace analysis, this study systematically reviewed the impact system of cause-related marketing on exhibition activities and fitted the neural network model with a single-factor inter-group experiment. The results show that when the complete data set is divided into 70% training set and 30% test set, the model with the training function of Train lm and seven hidden layers performs best in all models. This shows that in the process of charity marketing, the fit between consumers and charity activities determines the attitude and willingness of consumers to participate in charity marketing.