Search

Water pollution remains a pressing global challenge, threatening human health and ecosystem stability. Ultrafiltration emerges as a vital technology in this contest, offering a powerful tool for contaminant removal and safeguarding clean water resources. Thus, the optimization of ultrafiltration processes holds paramount significance for efficient contaminant removal. This study revolutionizes wastewater treatment by introducing a hybrid machine learning approach that optimizes ultrafiltration processes for superior contaminant removal. Utilizing the powerful synergy between eXtreme Gradient Boosting (XGBoost) and Bayesian optimization, we developed predictive models with remarkable accuracy (R2 values exceeding 99%) for post-treatment concentrations of metal ions, organic pollutants, and salts. This translates to precise control over the ultrafiltration process, driven by 4 key input variables: metal ion concentration, organic pollutants, salts, and applied pressure. The findings not only demonstrate the effectiveness of this hybrid approach but also pave the way for significant advancements in wastewater treatment strategies, ultimately contributing to cleaner water. This research marks a significant leap in machine learning applications for environmental challenges, paving the way for further advancements in wastewater treatment technology. [ABSTRACT FROM AUTHOR]

The identification of the disturbance stress direction of rock mass is crucial for safe and efficient production in underground engineering. This paper proposes a novel disturbance stress direction identification method of rock mass using acoustic parameters, which can provide precise disaster protection in underground engineering. First, monitoring sensors are arranged in the target rock mass area for environmental preparation and data acquisition. Second, the acquired data are cleaned and the parameter weight is carefully balanced. Third, the objective function is constructed and the disturbance stress direction is identified. To verify the validity of the proposed method, the uniaxial and biaxial compression tests on granite specimens were carried out to simulate the engineering disturbances on the rock mass under equal and non-equal confining pressure conditions, respectively. The results showed that the average direction error of the novel method is 12.74°. The accuracy of the proposed method could be enhanced by dealing with rock mass in isobaric conditions and a larger dataset. This study not only achieves effective identification of disturbance stress direction but also contributes to preventing and controlling engineering disasters. Highlights: A novel disturbance stress direction identification method (DSDI) using acoustic parameters is proposed. The structural change of rock masse induced by disturbance stress is the inherent cause of anisotropy in acoustic parameter variations. The accuracy of DSDI was verified by the indoor experiment under equal confining pressure and non-equal confining pressure. [ABSTRACT FROM AUTHOR]

Pelophylax nigromaculatus is an important aquaculture economic animal in China, with an annual output of more than 80,000 tons in recent years. P. nigromaculatus demonstrates distinct sexual dimorphism, characterized by females being substantially larger than males. Consequently, the cultivation of female populations presents higher economic value. The RNA-Seq technology is harnessed in this study to delve into the sexual differentiation and reproductive mechanisms of these frogs. By analyzing the transcriptomes of mature testes and ovaries, 17.24 Gbp and 23.71 Gbp of clean data were obtained respectively. This analysis yielded 107,897 annotated genes, among which 8099 were differentially expressed; 4370 were upregulated and 3729 were downregulated in ovaries. GO and KEGG analyses highlighted 70 genes exhibiting sexual dimorphism. For validation, 13 differentially expressed genes were subjected to qRT-PCR, corroborating the results of transcriptome sequencing. Furthermore, the transcriptome analysis of P. nigromaculatus' sexual glands revealed 1,062,663 SNPs, with 676,172 (63.63%) and 386,491 (36.37%) being base conversion and base transversion sites, respectively. Utilizing excel for SNP site analysis, 35,406 SNPs were identified as significantly associated with the sex of P. nigromaculatus. In pursuit of sex-related markers, 100 SNP sites, aligning perfectly with male heterozygosity and female homozygosity, were randomly selected for amplification and sequencing. Through this approach, 134 SNPs were identified, 83 (61.94%) of which matched the transcriptome data. The findings of this study provide an invaluable resource for future functional research on sex-related genes of P. nigromaculatus and offer a foundation for the development of sex-related molecular markers. [ABSTRACT FROM AUTHOR]

Background & Objectives: In today’s world, many adolescents face many problems due to social media distractions, one of the most important problem is mental health and its related problems. India has adolescents, a quarter of the total population and only a few studies were done on mental well-being among adolescents studying in government and private schools. The objectives of the study were to assess mental well-being among adolescents and to find out the association between mental well-being with selected sociodemographic variables. Methodology: This was a cross-sectional self-administered questionnaire based study. A convenient sampling method was used. Totally 184 male and female adolescents studying 9th to 12th standards from rural areas of government and private school students had voluntarily participated. To assess the mental well-being among adolescents Warwick Mental Well-being scale was used. All the collected data were cleaned, coded, and entered in MS Excel version 2007. Frequencies, Chi-square test, and Fisher’s exact test were analyzed by using SPSS software version 22.0. Results and Conclusion: The results were found that 59 % of adolescents were studying in a government school and 41% were studying in a private school. There was an association between mental well-being and the mother’s education, siblings, staying, domicile and extracurricular activities (p<0.05). This research study showed that the majority of the adolescents were having average level of mental well-being in the Vellore district of Tamil Nadu. [ABSTRACT FROM AUTHOR]

Background & Objectives: In today’s world, many adolescents face many problems due to social media distractions, one of the most important problem is mental health and its related problems. India has adolescents, a quarter of the total population and only a few studies were done on mental well-being among adolescents studying in government and private schools. The objectives of the study were to assess mental well-being among adolescents and to find out the association between mental well-being with selected sociodemographic variables. Methodology: This was a cross-sectional self-administered questionnaire based study. A convenient sampling method was used. Totally 184 male and female adolescents studying 9th to 12th standards from rural areas of government and private school students had voluntarily participated. To assess the mental well-being among adolescents Warwick Mental Well-being scale was used. All the collected data were cleaned, coded, and entered in MS Excel version 2007. Frequencies, Chi-square test, and Fisher’s exact test were analyzed by using SPSS software version 22.0. Results and Conclusion: The results were found that 59 % of adolescents were studying in a government school and 41% were studying in a private school. There was an association between mental well-being and the mother’s education, siblings, staying, domicile and extracurricular activities (p<0.05). This research study showed that the majority of the adolescents were having average level of mental well-being in the Vellore district of Tamil Nadu. [ABSTRACT FROM AUTHOR]

Background: Platycodon grandiflorum (P. grandiflorum) is a commonly used medicinal plant in China. Transcriptome sequencing studies of different tissues of P. grandiflorum have been widely conducted. However, studies on transcriptome sequencing and expression patterns of key genes in the saponin synthesis pathway of Tongcheng P. grandiflorum, a high-quality medicinal resource of different years, are relatively limited. Results: This study involved transcriptome sequencing and bioinformatics analysis of the roots from annual, biennial, and triennial P. grandiflorum in the Tongcheng area. After data filtering and assembly, we obtained 111.44 Gb of clean base data, including 742,880616 clean reads. We identified 5,156 differential expression unigenes between at least two sample groups, with differences noted among annual, biennial, and triennial P. grandiflorum plants. GO enrichment analysis annotated 3509, 1819, and 1393 DEGs in comparison TC1vsTC2, TC1vsTC3, and TC2vsTC3, respectively. Furthermore, KEGG enrichment analysis identified 16 genes encoding key enzymes in the terpene skeleton biosynthesis, sesquiterpene and triterpene biosynthesis pathways, including SE, AACT, FPPS, DXR, HMGR, HMGS, and DXS. The results of qRT-PCR experiments showed that most of the genes were most highly expressed in annual P. grandiflorum. Conclusions: The present study provided transcriptomic data from the roots of Tongcheng P. grandiflorum of different years, which provides critical bioinformatics data on the growth and development of P. grandiflorum, laying a foundation for further research on saponins and identifying key enzymes involved in this process across different growth stages. [ABSTRACT FROM AUTHOR]

Introduction: Urgency has been defined as the tendency towards rash speech and behavior in the context of emotion. Measures of Urgency have been found to have robust predictive power for psychopathologies and problematic behaviors. In the current study, we question whether emotions are unique drivers of urgency, or if emotions are potent exemplars of contexts that lead to rash speech and behavior. The Emotion Specific model and the Broader Contexts model correspond with these two conceptualizations of urgency, and they frame our pre-registered hypotheses. Methods: Participants from two well-powered samples (n = 600,n = 588) completed 9 modified items from the Urgency and Positive Urgency scales to assess rash responses in each of four contexts - "Upset," "Excited," "Tired," and "Hungry" - and a fifth "In General" set. After data cleaning, we used principal components analysis to construct a unidimensional, 4-item set that was applied to capture impulsive behavior across the five contexts. Results: We found that this research tool, called the Contexts of Impulsive Behaviors (CIBS), replicated in the second dataset, and it had adequate internal reliability in both samples. Although the Emotion Specific model was supported by the fact that the Upset context had a greater mean and greater variance than the Tired and Hungry contexts, most results supported the Broader Contexts model. That is, CIBS contexts were highly intercorrelated, and bivariate correlations with psychopathology were not significantly different across contexts. In partial correlations, effects of the Upset and Excited contexts were partially or fully statistically mediated by the Tired and Hungry contexts. Discussion: These findings suggest that emotions are potent contexts for impulsive behaviors. At the same time, those with high urgency are vulnerable to impulsivity in other contexts, such as fatigue and hunger, that challenge the regulatory functions of the prefrontal cortex. Limitations, future directions, and clinical implications are discussed. [ABSTRACT FROM AUTHOR]

Social networks on the dark web are rich in data that provides valuable insight into the nature of the activities on the dark web and human behaviors related to these activities. It also encompasses a diversity of ideologies, interests, and thought patterns associated with illicit activities and businesses on the dark web. For this reason, social networks on the dark web constitute a powerful tool and a profuse data source for various investigative work. However, such investigations encounter considerable challenges related to the massive volumes of textual data, analyzing it effectively, and extracting knowledge from it. This knowledge can be used in various investigations and studies when representing it in ontologies as a unified and integrative data source. In this paper, we introduce a novel approach for extracting and representing knowledge hidden in dark web communities through topic modeling and ontology learning methods. We start from the conceptual design of the ontology and employ several stages of text processing and analysis to achieve the desired knowledge graph, DarkOnto. These stages include data cleaning and preprocessing, topic modeling using correlated topic model (CTM), class‐topic similarity estimation, ontology construction, ontology population, and ontology evaluation, where the proposed approach achieved high results. Furthermore, we discuss the results, limitations, challenges, and future work. This paper presents a promising approach for extracting hidden valuable knowledge from dark web communities where investigating and conceptualizing criminal communities can be conducted efficiently. [ABSTRACT FROM AUTHOR]

This research employs a comprehensive methodology to delve into public sentiments surrounding the Israeli–Palestinian conflict, incorporating unconventional data sources—YouTube comments and Mastodon. Departing from the traditional reliance on Twitter, our systematic approach involves keyword-driven content identification, leveraging Google Trends to establish five pivotal keywords. Mastodon searches employed straightforward hashtag strategies, while the intricacies of YouTube required an exclusive focus on official newspaper channels to mitigate polarization risks. Rigorous data cleaning ensued, retaining only English-language texts and eliminating extraneous elements. The resulting dataset was subjected to Sentiment Analysis and Emotion Detection, providing a nuanced understanding of public sentiments across platforms, totaling 253.3 K texts. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the second most commonly diagnosed cancer worldwide, with a high mortality rate in developing countries, including sub-Saharan Africa. Screening is one way to ensure early detection and management of breast cancer, and it is influenced by several factors. Education and socio-economic status may also affect the utilization of breast cancer screening services as these impact decision-making. This study aimed to investigate women's empowerment and its influence on the uptake of breast cancer screening among women in Tanzania. Methods: This study utilized the 2022 Tanzania Demographic and Health Survey data, and included 4216 women aged 20 to 49 years. Women empowerment variables used include social independence, decision-making, ownership of assets, and attitude towards violence. Statistical Package for Social Sciences version 26 was used for data cleaning and analysis. Descriptive statistics and bivariate analysis were done, including a multivariate logistic regression to assess the level of association between independent variables with breast cancer screening. Results: Findings indicate that the prevalence of breast cancer screening is 5.2%. Age, education level, literacy, ownership of assets, attitude towards violence, and decision making are associated with ever going for breast cancer screening. Women aged 45 to 49 years (AOR = 6.28, 95% CI = 6.27–6.28), those with secondary or higher education (AOR 1.1, 95% CI = 1.05–1.06), literate women (AOR = 1.13, 95% CI = 1.13–1.13), those who own a house (AOR = 3.08, 95% CI = 3.08–3.09), who jointly decide on their healthcare with partners on healthcare (AOR = 1.18, 95% CI = 1.18–1.19) had significantly higher odds of going for breast cancer screening. Conclusion: Women's empowerment is significantly associated with the likelihood of engaging in breast cancer screening. Empowered women are more likely to undergo screening. Focus should be on empowering women through education, businesses, and community involvement. Country-specific interventions and breast cancer screening awareness campaigns should include empowerment initiatives to promote screening uptake. [ABSTRACT FROM AUTHOR]

Career Guidance and Counseling Needs Scale (CGCNS) was developed to assess career related needs of students. Initially 143 items were generated from verbatim of university students who participated in qualitative exploratory interviews conducted earlier. Item pool was shortened to 90 items and 67 items respectively after two successive revisions. Content validity was established by expert review from 10 experts resulting in a 57-items scale. For standardization, CGCNS was administered to 480 (451 after data cleaning) public sector university students selected through stratified random sampling. Demographic Sheet, Perceived Stress Scale, and Career Decision-Making Indicators Scale were used for establishing validity. Through EFA, four factors were generated titled Career Foresight, Career Difficulties, Career Dissonance, and Career-Related Self-Awareness. Final CGCNS had 41 items. Alpha reliability and split-half reliability was 0.88 and 0.80 (p < .001) respectively. Convergent validity was 0.76 and concurrent validity was 0.70 (p < .001). Pakistan and other similar countries could benefit from CGCNS. [ABSTRACT FROM AUTHOR]

Garlic is an important vegetable and condiment that has good medical and health care effects. At present, the origin of Chinese garlic and its association with other types of quality are limited to the molecular marker level, and there are few reports at the genome level. Therefore, this study is based on the specific length amplified fragment sequencing (SLAF-seq) of 102 copies of garlic germplasm resources, the group structure, and further screening of the core germplasm. SLAF-seq of 102 garlic cultivars yielded 1949.85 Mb of clean data and 526,432,275 SNPs. Through principal component analysis, evolutionary tree, population structure, and genetic relationship analysis, all garlic cultivars were divided into 3 groups. Among them, Group 1 contains 45 Chinese cultivars and 1 Egyptian cultivar, which are distributed mainly in the coastal and central areas of China. Group 2 contains 36 Chinese cultivars and 1 U.S. cultivar, which are distributed mainly in Northwest China. Group 3 contains 19 Chinese cultivars, which are distributed mainly in Xinjiang, China. The genetic diversity results indicate that the fixation index (Fst) values of Group 1 and Group 2 are lower than those of Group 1 and Group 3 and that the diversity of nucleotides (π) of Group 3 is greater than those of Group 2 and Group 1. Finally, the 30 parts of the cultivars were used as the core germplasms, and there was no difference between the two cultivars in terms of core quality. In summary, this study provides tags for the determination of garlic molecular markers and genotypes and provides a theoretical basis for subsequent resource protection and utilization, genetic positioning of important agronomic traits, and molecular marking agglomeration breeding. [ABSTRACT FROM AUTHOR]

The idea of Transit Oriented Development (TOD) was to foster urban development around railway networks and has been strategically built and applied since the late 19th and early 20th centuries. Previous studies reported positive, negative, or irrelevant impact of TOD on prices of surrounding real estate. The study aims to evaluate the impact of TOD on property prices in Kuala Lumpur. It utilizes secondary data obtained from the National Property Information Centre (NAPIC), Malaysia, after data cleaning, Nine Thousand Five Hundred and Forty-Nine (9549) Housing Transactions between the periods 2009 and 2018 were used. The research design was quantitative, and the Hedonic Price Model (log-log model) was used for data analysis. The model revealed a multiple correlation coefficient (R) of 0.891 and an adjusted R2 value of 0.794, indicating that 79.4% of the house price variation is explained by the model. The F value of 996.921, which is statistically significant, indicates that the predictors significantly combine to predict the price of TOD areas in Kuala Lumpur. The coefficient for LnTrainsta is 0.056, indicating that there was a positive relationship between residential house prices and TOD in Kuala Lumpur; this explains that house prices increase by 5.6% for every 100 meter closer to the rail transit station. [ABSTRACT FROM AUTHOR]

Policymakers demonstrate a keen interest in understanding risky driving behaviors to formulate effective countermeasures aimed at reducing accidents and economic losses. With the increasing deployment of millimeter-wave (MMW) radars on roadways, there exists a viable opportunity to gather extensive vehicle information at big data levels from individual drivers traversing through the radar detection range. This study endeavors to analyze traffic flow characteristics and identify risky driving behaviors using the noisy raw vehicle position and speed profiles obtained from MMW radars installed on a highway in China. A series of data cleaning procedures are meticulously implemented to address several typical trajectory errors stemming from MMW radars. Subsequently, after data cleaning, the study identifies risky driving behaviors through established methods found in the literature and evaluates the prevalence of these behaviors across different times of day and days of the week. This research mitigates the gap between raw vehicle trajectories from MMW radar and popular existing risk analysis methods. In addition, this research analyzes the temporal pattern of different risks and pinpoints their inherent connections. The outcomes of this research endeavor hold the potential to furnish practical insights for the formulation of targeted safety enhancement policies by governmental bodies or relevant agencies. [ABSTRACT FROM AUTHOR]

Burn registers are an important source of surveillance data on injury intent. These data are considered essential to inform prevention activities. In South Asia, intentional burn injuries are thought to disproportionately affect women. Assessment of injury intent is difficult because it is influenced by personal, family, social, and legal sensitivities. This can introduce misclassification into data, and bias analyses. We conducted a descriptive, hypothesis generating study to explore misclassification of injury intent using data from a newly digitised single centre burn register in south India. Data from 1st February 2016 to 28th February 2022 were analysed. All patients in the data set were included in the study (n = 1930). Demographic and clinical characteristics for patients are described for each classification of injury intent. All data cleaning and analyses were completed using RStudio. Injury intent data were missing for 12.6% of cases. It was the most commonly missing variable in the data set. "Accidental" injuries had a similar distribution over time, age, and total body surface area (TBSA) for males and females. "Homicidal" injuries were more common in females. Injuries reported as "Suicidal" affected men and women equally. A decrease in reporting of "Suicidal" injuries in females corresponded to an increase in high TBSA injuries classified as 'Other' or with missing data. Overwriting of injury intent was present in 1.5% of cases. The overwritten group had a greater proportion of females (62.1% vs. 48.5%) and higher median TBSA (77.5% vs. 27.5%) compared to the group where intent was not overwritten. Our findings indicate that some subgroups, such as females with high TBSA burns, appear to be more likely to be misclassified and should be the focus of future research. They also highlight that quality of surveillance data could be improved by recording of clinical impression, change in patient reported intent, and use of a common data element for intent to standardise data collection. We also recommend that injury intent is recorded as a unique variable and should not be mixed with other elements of injury causation (e.g. mechanism). Although this is a single centre study, the methods will be of interest to those who utilise routinely collected data and wish to reduce misclassification of this important variable. • We explored misclassification of injury intent using burn register data from India. • Injury intent was the most commonly missing variable in the data set (12.6%). • Patients with overwriting of intent predominantly included females and large burns. • Females with burns categorised as 'other' or with missing data may be misclassified. • These findings highlight opportunities to improve quality of surveillance data. [ABSTRACT FROM AUTHOR]

Background: Developmental toxicity tests are extremely expensive, require a large number of animals, and are time-consuming. It is necessary to develop a new approach to simplify the analysis of developmental endpoints. One of these endpoints is malformation, and one group of ongoing methods for simplifying is in silico models. In this study, we aim to develop a quantitative structure–activity relationship (QSAR) model and identify the best algorithm for predicting malformations, as well as the most important and effective physicochemical properties associated with malformation. Methods: The dataset was extracted from a reliable database called COMPTOX. Physicochemical properties (descriptors) were calculated using Mordred and RDKit chemoinformatics software. The data were cleaned, preprocessed, and then split into training and testing sets. Machine learning algorithms, such as gradient boosting model (GBM) and logistic regression (LR), as well as deep learning models, including multilayer perceptron (MLP) and neural networks (NNs) trained with train set data and different sets of descriptors. The models were then validated with test set and various statistical parameters, such as Matthew's correlation coefficient (MCC) and balanced accuracy (BAC) score, were used to compare the models. Results: A set of descriptors containing with 78% AUC was identified as the best set of descriptors. Gradient boosting was determined to be the best algorithm with 78% predictive power. Conclusions: The descriptors that were the most effective for developing models directly impact the mechanism of malformation, and GBM is the best model due to its MCC and BAC. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance. [ABSTRACT FROM AUTHOR]

Copyright of Commerce & Management Quarterly is the property of Chinese Association of Business & Management Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite a voluminous literature on resource availability and the implications for organizational performance, little is known about how changes in government agencies' resources impact their policy implementation activities and goal prioritization. This article explores how changes in resources affect regulatory enforcement activities by types of resources and policy implementation activities, and whether resource cutbacks prompt a tradeoff of the effectiveness‐equity goals. Using the block‐group level data on the Clean Air Act (CAA) implementation from 2012 to 2019, we find that state environmental agencies prioritize regulatory effectiveness over environmental justice by concentrating their resources on communities where task demands correspond to organizations' core missions. They also promote social equity to some extent when facing spending cutbacks but not staffing cuts. Spending cutbacks had a less severe impact on compliance inspections for more socially vulnerable communities, while those exposed to more imminent environmental harms received more inspections. [ABSTRACT FROM AUTHOR]

The quality of data in a pavement management system (PMS) has a direct impact on pavement maintenance and rehabilitation (M&R) decisions and management. However, pavement performance data and M&R action data often suffer from problems such as omissions and anomalies. To solve these problems, this study proposes a data cleaning framework based on artificial neural networks (ANNs) that can clean pavement performance data and M&R action data simultaneously. First, data are classified and labeled by the framework using the percentile method and considering the nature of the PMS data itself. Then two ANNs are established, one to clean data from anomalous or omitted pavement performance, and another to fill in data from omitted M&R actions. Applying the framework to the PMS in Shanxi Province, China, the following conclusions can be drawn. In terms of filling in the omitted M&R action, ANN calculated less average loss and improved the average prediction accuracy by 7.88% compared to the logistic regression model, proving the superiority of ANN in filling in the omitted M&R action data. Compared to the framework of filling in the omitted M&R action data by ANN without cleaning the pavement performance data, the proposed framework resulted in less average loss values and 5.71% improvement in average accuracy, demonstrating the need for cleaning both types of data simultaneously. The framework can provide a higher-quality data set for pavement M&R decisions and management. [ABSTRACT FROM AUTHOR]

Among malignant diseases, lung cancer has one of the highest mortality and incidence. Most epidemiological studies conclude that Hungary faces the most severe burden in association with this disease. However, for various reasons estimates and population-based studies show discrepancies. In this study, an intense data cleansing was performed on lung cancer cases that were reported to the Hungarian National Cancer Registry in 2018, and the major clinico-pathological parameters as well as survival characteristics were described. Our population-based figures were compared to the European estimates. As a result of our thorough revision, the corrected incidence of lung cancer has fallen below the number of cases that were reported to the Registry from 11,746 to 9,519. We also demonstrate that Hungary did not show the highest incidence and mortality in Europe, but it is still among the ones with the worst raking countries, with 92.9 and 50.6 age standardized rate per 100 thousand capita among males and females, respectively. Analysis of the annually reported case numbers revealed a gender-specific difference in incidence trends: while from 2001 to 2019 it slightly decreased among males, it increased among females. The most dominant subtype was adenocarcinoma, which was more frequent among female patients. Unfortunately, most of the newly diagnosed cases were in advanced stage; thus, 5 year overall survival was 14.8%. We anticipate that in the longer term, a decrease in incidence and improvement in survival rates may be expected as a result of the development of primary and secondary prevention programs in the country. [ABSTRACT FROM AUTHOR]

This study aims to enhance maritime safety by focusing on risk analysis in tank cleaning operations, specifically targeting the risk of spillages. The Bow-Tie-based Fuzzy Bayesian Network (BT-FBN) methodology was employed to conduct an equipment-based dynamic risk analysis. A real chemical tanker was selected as the model vessel to ensure realistic calculations, and ‘Loss of Containment’ (LoC) was identified as the critical event for comprehensive risk analysis. The BT methodology was utilised to identify and systematically map potential hazards. Considering the temporal dynamics of risk, a Bayesian dynamic risk analysis was performed using four-month tank cleaning data from the model vessel, and fuzzy logic was applied to address uncertainties in the data. The findings indicated a steady increase in the risk of ‘LoC’ from 4.33% to 11.86% over four months, consistent with observed accident rates (8.57%). Furthermore, the likelihood of consequential events, such as ‘Delay in Operations & Near Miss’ (C-1), showed a similar trend, ranging from 3.69% to 10.09%. Notably, the analysis underscored the critical role of ‘Human Error & Management Failure’ (IE4) in contributing most significantly to ‘LoC’, ranging from 2.86% to 8.77%, emphasising the importance of addressing human factors to mitigate environmental pollution. [ABSTRACT FROM AUTHOR]

Background: Insomnia is a sleep disorder characterized by difficulty falling asleep, staying asleep, or experiencing poor-quality sleep. People with this problem often have trouble falling asleep at night, wake up frequently during the night, and may wake up too early in the morning and feel tired and not refreshed. This can lead to daytime fatigue, irritability, difficulty concentrating, and impaired functioning in their day-to-day activities. Study is scarce in resource-limited countries such as Ethiopia, particularly concerning the study setting. As a result, this study aimed to assess the prevalence of insomnia and its associated factors among undergraduate students at Hawassa University. Methods: An institution-based cross-sectional study design was used. A stratified simple random sampling method was used among 398 study participants. The data were collected using a structured self-administered questionnaire. The outcome variable was assessed by the insomnia severity index (ISI). The data were then gathered by using the Kobo toolbox online and then exported into the Statistical Package for Social Sciences version 27 to analyze the data. Data cleaning and screening were conducted exclusively by the investigators. Descriptive statistics like frequency, percentages, and mean were used. Bivariate and multivariate binary regression were performed. In multivariate binary logistic regression, a p-value of<0.05 was identified as a significantly associated factor with the dependent variable. Results: Among 398 students who participated in the study, 81 (20.4%) experienced insomnia. Being female [adjusted odds ratio (AOR) = 2.98; 95% confidence interval (CI) 1.56--5.69], age (AOR = 3.06; 95% CI 1.11--8.45), mild anxiety symptom (AOR = 4.89; 95% CI 1.56--15.37), and mobile device use ≥30 min just before going to sleep (AOR = 7.81; 95% CI 2.34--26.12) were significantly associated with insomnia. Conclusion: In this study, the prevalence of insomnia was one-fifth among university students, which is high when compared to another study at the national level. There were significant associations between being female, age, anxiety symptoms, and mobile device use before going to sleep with insomnia. This indicates that there is a need to treat and prevent insomnia in college students, emphasizing the necessity for mental healthcare and ethical technology use. [ABSTRACT FROM AUTHOR]

Semantic interoperability (SI) is defined as the capability of interpreting the nature of the information exchanged inside cloud computing (CC). For SI, ontology is selected as a solution. A hierarchical structure is offered by an ontology that comprises semantic relations between the application and the cloud server (CS). Even though different methods are introduced, ontology centered on semantic relation between the application and CS is not yet attained for achieving accurate interoperability. An ontology‐centered SI in CC utilizing a modified Shell Game optimization‐centered recurrent neural network (MSGO‐RNN) is presented in this work. Here, accessing the data from the data warehouse is initiated that experiences different standardization functions for improving the data's quality, namely, data extraction, data cleaning, data transformation, data loading, and refreshing. Then, the data are passed via the semantic layer (SL), which offers a constant way of interpreting data. Then, for enhancing the accuracy rate of interoperability along with offering a low computational time, the relevant feature is chosen utilizing genetic crossover mutation‐centered improved fertile field (GCM‐IFF) optimization. Ontology is created utilizing the Protégé tool centered on the data, and respective manipulation is executed utilizing HermiT ontology web language (OWL) reasoner. Lastly, to attain cloud interoperability, the features are trained and tested by employing the MSGO‐RNN. The experimental outcome exhibits that when compared with the top‐notch methods, an accuracy of 96.32% is acquired by the framework for choosing the CS, and less time of 22567 ms is acquired by the framework for training. [ABSTRACT FROM AUTHOR]

While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is timeconsuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research. [ABSTRACT FROM AUTHOR]

Maintaining a healthy diet is essential, particularly for diabetic patients who are affected by severe defects. Nutrition therapy is vital to avoid the effects of diabetes and maintain better health. However, high sugar, fatty foods are significantly avoided by the patients to find alternate food from the same group. Despite this, there are still variations in diet that affect diabetic patients. Therefore, this paper proposes an effective food recommendation system to suggest suitable dietary plans for patients affected by diabetes. The personal health information of patients and several food products gathered from the Internet of Medical Things (IoMT) dataset are taken as input data for the proposed integrated recommendation system. To boost unbiased detection, the data noise present in the IoMT dataset is eliminated and regularized using preprocessing steps, including data cleaning, data reduction, data transformation, data enrichment, and data validation. From the preprocessed data, significant feature characteristics that contribute more to diabetes detection and food product recommendation are selected using the Wingsuit Flying Search (WFS) algorithm. These selected features are then classified using the proposed Hybrid Ensemble Support Vector Deep Residual (HESDR) approach. This approach accurately classifies diabetes and non-diabetes patients and suggests top nutrient-dense food products for patients with diabetes. The analytical results show that the proposed HESDR approach achieves a high accuracy rate of about 98.3% compared to other methods. In conclusion, this paper proposes an effective food recommendation system that accurately classifies diabetic patients and suggests suitable dietary plans based on their health conditions. The approach effectively eliminates data noise and selects significant features using the WFS algorithm. The proposed HESDR approach accurately classifies diabetic patients and recommends nutrient-dense food products for them, achieving high accuracy rates. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis and prompt treatment of malaria in young children are crucial for preventing the serious stages of the disease. If delayed treatment-seeking habits are observed in certain areas, targeted campaigns and interventions can be implemented to improve the situation. Methods: This study applied multivariate binary logistic regression model diagnostics and geospatial logistic model to identify traditional authorities in Malawi where caregivers have unusual health-seeking behaviour for childhood malaria. The data from the 2021 Malawi Malaria Indicator Survey were analysed using R software version 4.3.0 for regressions and STATA version 17 for data cleaning. Results: Both models showed significant variability in treatment-seeking habits of caregivers between villages. The mixed-effects logit model residual identified Vuso Jere, Kampingo Sibande, Ngabu, and Dzoole as outliers in the model. Despite characteristics that promote late reporting of malaria at clinics, most mothers in these traditional authorities sought treatment within twenty-four hours of the onset of malaria symptoms in their children. On the other hand, the geospatial logit model showed that late seeking of malaria treatment was prevalent in most areas of the country, except a few traditional authorities such as Mwakaboko, Mwenemisuku, Mwabulambya, Mmbelwa, Mwadzama, Zulu, Amidu, Kasisi, and Mabuka. Conclusions: These findings suggest that using a combination of multivariate regression model residuals and geospatial statistics can help in identifying communities with distinct treatment-seeking patterns for childhood malaria within a population. Health policymakers could benefit from consulting traditional authorities who demonstrated early reporting for care in this study. This could help in understanding the best practices followed by mothers in those areas which can be replicated in regions where seeking care is delayed. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods. [ABSTRACT FROM AUTHOR]

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang's clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters. [ABSTRACT FROM AUTHOR]

Introduction: Hospitals across the country are facing increases in hospital length of stay ranging from 2% to 14%. This results in patients who stay in hospital for long periods of time being three times more likely to die in hospital. Therefore, identifying factors that contribute to longer hospital stays enhances the ability to improve services and quality of patient care. However, there is limited documented evidence on factors associated with longer hospital stays among surgical inpatients in Ethiopia and the study area. Objective: This study aimed to assess the length of hospital stay and associated factors among adult surgical patients admitted to surgical wards in Amhara Regional State Comprehensive Specialized Hospitals, Ethiopia, 2023. Methods: An institutional-based cross-sectional study was conducted among 452 adult surgical patients from April 17 to May 22, 2023. Data were collected based on a pretested, structured, interviewer-administered questionnaire, medical record review, and direct measurement of BMI. Study participants were selected using a systematic random sampling technique. The collected data were cleaned, entered into EpiData version 4.6.0 and exported to STATA version 14 for analysis. Binary logistic regression analysis was used. Variables with a p value <0.05 in the multivariable logistic regression analysis were considered statistically significant. Results: In the current study, the prevalence of prolonged hospital stay was 26.5% (95% CI: 22.7, 30.8). Patients referred from another public health facility (AOR = 2.65; 95% CI: 1.14, 6.14), hospital-acquired pneumonia (AOR = 3.64; 95% CI: 1.43, 9.23), duration of surgery ≥110 minutes (AOR = 2.54; 95% CI: 1.25, 5.16), being underweight (AOR = 5.21; 95%CI: 2.63, 10.33) and preoperative anemia (AOR = 3.22; 95% CI: 1.77, 5.86) were factors associated with prolonged hospital stays. Conclusion: This study found a significant proportion of prolonged hospital stays among patients admitted to surgical wards. Patients referred from another public health facility, preoperative anemia, underweight, duration of surgery ≥110 minutes, and hospital-acquired pneumonia were factors associated with prolonged hospital stay. Early screening and treatment of anemia and malnutrition before surgery can shorten the length of stay. [ABSTRACT FROM AUTHOR]

Objective: To investigate the differential expression genes (DEGs) in spinal tuberculosis using transcriptomics, with the aim of identifying novel therapeutic targets and prognostic indicators for the clinical management of spinal tuberculosis. Methods: Patients who visited the Department of Orthopedics at the Second Hospital, Lanzhou University from January 2021 to May 2023 were enrolled. Based on the inclusion and exclusion criteria, there were 5 patients in the test group and 5 patients in the control group. Total RNA was extracted and paired-end sequencing was conducted on the sequencing platform. After processing the sequencing data with clean reads and annotating the reference genome, FPKM normalization and differential expression analysis were performed. The DEGs and long non-coding RNAs (LncRNAs) were analyzed for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment. The cis-regulation of differentially expressed mRNAs (DE mRNAs) by LncRNAs was predicted and analyzed to establish a co-expression network. Results: This study identified 2366 DEGs, with 974 genes significantly upregulated and 1392 genes significantly downregulated. The upregulated genes are associated with cytokine-cytokine receptor interactions, tuberculosis, and TNF-α signaling pathways, primarily enriched in biological processes such as immunity and inflammation. The downregulated genes are related to muscle development, contraction, fungal defense response, and collagen metabolism processes. Analysis of LncRNAs from bone tuberculosis RNA-seq data detected a total of 3652 LncRNAs, with 356 significantly upregulated and 184 significantly downregulated. Further analysis identified 311 significantly different LncRNAs that could cis-regulate 777 target genes, enriched in pathways such as muscle contraction, inflammatory response, and immune response, closely related to bone tuberculosis. There are 51 genes enriched in the immune response pathway regulated by cis-acting LncRNAs. LncRNAs that regulate immune response-related genes, such as upregulated RP11-451G4.2, RP11-701P16.5, AC079767.4, AC017002.1, LINC01094, CTA-384D8.35, and AC092484.1, as well as downregulated RP11-2C24.7, may serve as potential prognostic and therapeutic targets. Conclusion: The DE mRNAs and LncRNAs in spinal tuberculosis are both associated with immune regulatory pathways. These pathways promote or inhibit the tuberculosis infection and development at the mechanistic level and play an important role in the process of tuberculosis transferring to bone tissue. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms--Random Forest, Multilayer Perceptron, and Convolutional Neural Network--are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930-0.957 and an AUC value of 0.955. The CNNbased warning model demonstrated an accuracy of 0.945-0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930-0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on 5 August 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multi-classification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this study Frontiers in Earth Science 01 frontiersin.org Liu et al. 10.3389/feart.2024.1419421 contribute to valuable attempts in landslide disaster warning model research, with anticipated further improvements through the gradual accumulation of samples and practical application verification. [ABSTRACT FROM AUTHOR]

Reducing the radiation dose leads to the x-ray computed tomography (CT) images suffering from heavy noise and artifacts, which inevitably interferes with the subsequent clinic diagnostic and analysis. Leading works have explored diffusion models for low-dose CT imaging to avoid the structure degeneration and blurring effects of previous deep denoising models. However, most of them always begin their generative processes with Gaussian noise, which has little or no structure priors of the clean data distribution, thereby leading to long-time inference and unpleasant reconstruction quality. To alleviate these problems, this paper presents a Structure-Aware Diffusion model (SAD), an end-to-end self-guided learning framework for high-fidelity CT image reconstruction. First, SAD builds a nonlinear diffusion bridge between clean and degraded data distributions, which could directly learn the implicit physical degradation prior from observed measurements. Second, SAD integrates the prompt learning mechanism and implicit neural representation into the diffusion process, where rich and diverse structure representations extracted by degraded inputs are exploited as prompts, which provides global and local structure priors, to guide CT image reconstruction. Finally, we devise an efficient self-guided diffusion architecture using an iterative updated strategy, which further refines structural prompts during each generative step to drive finer image reconstruction. Extensive experiments on AAPM-Mayo and LoDoPaB-CT datasets demonstrate that our SAD could achieve superior performance in terms of noise removal, structure preservation, and blind-dose generalization, with few generative steps, even one step only. [ABSTRACT FROM AUTHOR]

Eye tracking has taken hold in second language acquisition (SLA) and bilingualism as a valuable technique for researching cognitive processes, yet a comprehensive picture of reporting practices is still lacking. Our systematic review addressed this gap. We synthesized 145 empirical eye‐tracking studies, coding for 58 reporting features and applying a gap analysis to the codings. Although certain aspects of reporting, such as descriptions of auxiliary assumptions, equipment, and setup, were consistently implemented, we found significant variation and sometimes complete omission of crucial details surrounding aspects of data quality and of data preprocessing and cleaning. This lack of information hinders the evaluation of methodological rigor and overall study quality of eye‐tracking research. We propose a set of field‐specific reporting guidelines in the form of a checklist to improve the quality of data collected and contribute to the larger goal of advancing the replication and reproducibility of eye‐tracking research in SLA and bilingualism. [ABSTRACT FROM AUTHOR]

Summary The advent of spatial transcriptomics has revolutionized our understanding of the spatial heterogeneity in tissues, providing unprecedented insights into the cellular and molecular mechanisms underlying biological processes. Although quality control (QC) critical for downstream data analyses, there is currently a lack of specialized tools for one-stop spatial transcriptome QC. Here, we introduce SpatialQC, a one-stop QC pipeline, which generates comprehensive QC reports and produces clean data in an interactive fashion. SpatialQC is widely applicable to spatial transcriptomic techniques. Availability and implementation source code and user manuals are available via https://github.com/mgy520/spatialQC , and deposited on Zenodo (https://doi.org/10.5281/zenodo.12634669). [ABSTRACT FROM AUTHOR]

Since the measurement of oil quantity in aviation engine lubrication systems is susceptible to multiple parameters, it is difficult to calculate and predict oil consumption rates. We propose an improved method for extracting oil quantity data and predicting oil consumption rates. Engine data are cleansed using density-based clustering algorithms, including density based spatial clustering of applications with noise (DBSCAN), to obtain stable oil quantity data during steady flight conditions. By the least squares method, oil consumption rates are derived with an average fitting goodness of 0.86. Subsequently, a multi-layer perception (MLP) is employed to establish the relationship between oil consumption rates and flight status parameters, resulting in a predicted average absolute percentage error of 1.15%, compared to actual values. The proposed method meets practical engineering requirements, providing a reliable reference for assessing the health status of aviation engine lubrication systems. [ABSTRACT FROM AUTHOR]

The P300 ERP component, related to the onset of task-relevant or infrequent stimuli, has been widely used in the Mobile Brain/Body Imaging (MoBI) literature. This systematic review evaluates the quality and breadth of P300 MoBI studies, revealing a maturing field with well-designed research yet grappling with standardization and global representation challenges. While affirming the reliability of measuring P300 ERP components in mobile settings, the review identifies significant hurdles in standardizing data cleaning and processing techniques, impacting comparability and reproducibility. Geographical disparities emerge, with studies predominantly in the Global North and a dearth of research from the Global South, emphasizing the need for broader inclusivity to counter the WEIRD bias in psychology. Collaborative projects and mobile EEG systems showcase the feasibility of reaching diverse populations, which is essential to advance precision psychiatry and to integrate varied data streams. Methodologically, a trend toward ecological validity is noted, shifting from lab-based to real-world settings with portable EEG system advancements. Future hardware developments are expected to balance signal quality and sensor intrusiveness, enriching data collection in everyday contexts. Innovative methodologies reflect a move toward more natural experimental settings, prompting critical questions about the applicability of traditional ERP markers, such as the P300 outside structured paradigms. The review concludes by highlighting the crucial role of integrating mobile technologies, physiological sensors, and machine learning to advance cognitive neuroscience. It advocates for an operational definition of ecological validity to bridge the gap between controlled experiments and the complexity of embodied cognitive experiences, enhancing both theoretical understanding and practical application in study design. [ABSTRACT FROM AUTHOR]

Developing technologies over digitalization have become more popular and become a threat to society and cybersecurity. Generally, the phishing method is used by hackers to access the data without the influence of users whose data was stolen. Several techniques are used to detect whether the data is phished or non-phished. Some anti-phishing software is used to identify the phishing data. However, few of these techniques did not provide efficient performance. Hence, the proposed model is introduced to overcome the issues obtained and improve the efficiency of detecting whether the data is phished or non-phished. The data is gathered from the Phishstorm dataset, which is pre-processed using the Z score normalization method and data cleaning. Data balancing is done by the Advanced synthetic sampling approach (Adv-SyN) to balance the dataset, and the features are extracted using a Double self-sparse autoencoder (DSelSa). The Opposition Gazelle optimization algorithm (OpGoA) model is used for optimal feature selection, and finally, the data is classified using Multi Head Depth wise Tern integrated long short term memory (MDepthNet). The sooty tern optimization is used to evaluate the loss function of the network model. The performance of the proposed model is analyzed based on some evaluation metrics and compared with other models, which describes the efficiency of the proposed model. The main objective of proposed technique used to detect the phishing attack and phishing or non-phishing. An automated DL methodology introduced for effective detection of phishing attacks for enhancing the cyber security. The accuracy of the proposed model is obtained as 99.45%, and Precision is 99.45%. RMSE and MSE rate of the proposed model is reduced to 0.73 and 0.05 for better performance. [ABSTRACT FROM AUTHOR]

Chlorine dioxide (ClO2) is widely used for the quality preservation of postharvest horticultural plants. However, the molecular mechanism of how ClO2 works is not clear. The purpose of this study was to understand ethylene-related molecular signaling in ClO2-treated fresh-cut cauliflower florets. Transcriptome analysis was used to investigate ethylene-related gene regulation. A total of 182.83 Gb clean data were acquired, and the reads of each sample to the unique mapped position of the reference genome could reach more than 85.51%. A sum of 2875, 3500, 4582 and 1906 differential expressed genes (DEGs) were identified at 0 d, 4 d, 8 d and 16 d between the control group and ClO2-treated group, respectively. DEGs were enriched in functions such as 'response to oxygen-containing compounds' and 'phosphorylation', as well as MAPK signaling pathway, plant hormone transduction pathway and so on. Genes, including OXI1, MPK3, WRKY22 and ERF1, which are located at the junction of wounding, pathogen attack, pathogen infection or ethylene signal transduction pathways, were up-regulated in response to stress. ETR and CTR1 (both up-regulated), as well as three down-regulated genes, including BolC5t34953H (a probable NAC), BolC1t05767H (a probable NAC) and BolC2t06548H (a probable ERF13), might work as negative regulators for ethylene signal transduction. In conclusion, ethylene-related genes and pathways are involved in ClO2 treatment, which might enhance stress resistance and have a negative feedback mechanism. [ABSTRACT FROM AUTHOR]

When sampling data of specific classes (i.e., known classes) for a scientific task, collectors may encounter unknown classes (i.e., novel classes). Since these novel classes might be valuable for future research, collectors will also sample them and assign them to several clusters with the help of known-class data. This assigning process is known as novel class discovery (NCD). However, category confusion is common in the sampling process and may make the NCD unreliable. To tackle this problem, this paper introduces a new and more realistic setting, where collectors may misidentify known classes and even confuse known classes with novel classes—we name it NCD under unreliable sampling (NUSA). We find that NUSA will empirically degrade existing NCD methods if taking no care of sampling errors. To handle NUSA, we propose an effective solution, named hidden-prototype-based discovery network (HPDN): (1) we try to obtain relatively clean data representations even with the confusedly sampled data; (2) we propose a mini-batch K-means variant for robust clustering, alleviating the negative impact of residual errors embedded in the representations by detaching the noisy supervision timely. Experiments demonstrate that, under NUSA, HPDN significantly outperforms competitive baselines (e.g., 6 % more than the best baseline on CIFAR-10) and remains robust when encountering serious sampling errors. [ABSTRACT FROM AUTHOR]

Damage to pipeline welds during service can result in substantial human and economic losses. Hence, early and accurate quality diagnosis of in-service natural gas pipeline welds is of utmost importance. However, the current diagnostic methods are plagued by low accuracy and excessive resource consumption. Therefore, a fusion model for diagnosing the quality of in-service natural gas pipeline welds is constructed based on gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), and random forest classifier (RFC). Initially, heterogeneous raw data sets undergo data cleaning and quality enhancement processes. Subsequently, the predictions from GBDT, KNN, and RFC are fused using voting as the fusion strategy. The processed data set is then utilized to train and test the fusion model (MIX). After verifying the reliability of the model through cross-validation, the performance of the model's prediction results is assessed, and a comprehensive analysis of the model's characteristics is conducted by combining theory and empirical evidence. By analyzing the experimental results, the fusion model performed better than the single model, with an accuracy rate of 96%. It can effectively diagnose the quality of natural gas pipeline welds in service, providing a new reference for existing diagnostic methods. Practical Applications: The fusion modeling method proposed in this study can be applied to the diagnosis of welding quality of in-service natural gas pipelines in order to solve the problem of early quality diagnosis of natural gas pipelines and improve the diagnosis of welding quality. The method can effectively overcome the problem of small size and poor quality of engineering data sets. First, the original data set is cleaned and downscaled, and then the synthetic minority oversampling technique (SMOTE) combined with edited nearest neighborhood (ENN) (i.e., SMOTEENN) is balanced to improve the quality of the data set. Then, the data set is predicted by three models, and the three predictions are fused using a voting fusion strategy to effectively improve the diagnostic ability of the models. Finally, the importance of the output features of the fused models is utilized for empirical and theoretical information mining. The experimental results show that the diagnostic accuracy of the method can reach 96% with high reliability and stability, which can provide technical support and theoretical basis for the diagnosis of the welding quality of in-service natural gas pipelines. Mining feature information can also provide more reference information for the pipeline welding construction stage, improve the initial welding quality, and reduce welding defects. [ABSTRACT FROM AUTHOR]

With the progressive enhancement of social media, several people prefer posting their opinions on various social media instead of posting on radios, television or newspapers. The postings differ in dimensions and include various titles and comments. Nowadays, the formation of plagiarism is increasing tremendously which occurs by rewriting or repeating one’s work. There are many ways to detect plagiarism by browsing through the internet. The significant intention of this paper involves the detection of plagiarism in social media using four different phases, namely the data pre-processing phase, n-gram evaluation, similarity/distance computation analysis and the plagiarism detection phase. The pre-processing includes data cleaning processes, such as the removal of redundant data, upper case letters, noise, irrelevant punctuations and characterizing into a vector form. After pre-processing the data are fed for n-gram evaluation to develop a post- ing attribution system. Then finally, an ensemble support vector machine-based African vulture optimization (ESVM-AVO) approach is employed to detect plagiarism which signifies that the performance based on detection is enhanced and the execution time in obtaining a high rate of detection accuracy is very low. Finally, the performance evaluation and the comparative analysis are carried out to determine the performance of the proposed system. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is to conduct a comprehensive bibliometric analysis of uveal melanoma (UM) research with a focus on Turkish contributions in both national and international literature. Methods: A search, including Web of Science (WoS) Core Collection, Scopus, Turkish Database, and gray literature, including national thesis and TUBITAK project databases, was conducted without time limitation. Documents focused on UM research and had at least one author with a Turkish institution affiliation were included. Data were cleaned and analyzed using bibliometric tools, including Open Refine and VOSviewer. Bibliometric data such as the number of publications, journals, authors, h-index, collaboration patterns, co-occurrence of keywords, citations, and the growth trends of publications were analyzed. Results: The oldest and newest documents found were between 1987 and 2024. A total of 113 international (97 publications from WoS and 16 from Scopus) and 26 national publications (Turkish index) were included. The most common document type was the original research article (n=89, 78.76%) in international literature. The most represented journal was the Turkish Journal of Ophthalmology with (n=12, 10.62%) publications. A total of 16 theses with a publication rate of 56.5% were noted. Hacettepe University, Ankara University, and Istanbul University were the leading affiliations in UM research. Keyword analysis showed that Turkish UM research is predominantly focused on treatment modalities, and the genetic aspect of research is less represented. Conclusion: Our results highlight the dominance of a few academic centers and researchers on UM research, modest contribution to international literature, and potential research progression areas such as basic science and genetics research. [ABSTRACT FROM AUTHOR]

Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth labels. However, in practice, it is not always feasible to obtain clean point clouds. In this paper, we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as groundtruth labels during training. We demonstrate that it is feasible for neural networks to only take noisy point clouds as input, and learn to approximate and restore their clean versions. In particular, we generate two noise levels for the original point clouds, requiring the second noise level to be twice the amount of the first noise level. With this, we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise, and thus learn the displacement of each noisy point in order to recover the corresponding clean point. Comprehensive experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise, obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods. [ABSTRACT FROM AUTHOR]

Existing learning-based dehazing algorithms struggle to deal with real world hazy images for lack of paired clean data. Moreover, most dehazing methods require significant computation and memory. To address the above problems, we propose a joint dual-teacher knowledge distillation and unsupervised fusion framework for single image dehazing in this paper. First, considering the complex degradation factors in real-world hazy images, two synthetic-to-real dehazing networks are explored to generate two preliminary dehazing results with the heterogeneous distillation strategy. Second, to get more qualified ground truth, an unsupervised adversarial fusion network is proposed to refine the preliminary outputs of teachers with unpaired clean images. In particular, the unpaired clean images are enhanced to deal with the dim artifacts. Furthermore, to alleviate the structure distortion in the unsupervised adversarial training, we constructed an intermediate image to constrain the output of the fusion network. Finally, considering the memory storage and computation overhead, an end-to-end lightweight student network is trained to learn the mapping from the original hazy image to the output of the fusion network. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on real-world hazy images in terms of no-reference image quality assessment and the parameters. [ABSTRACT FROM AUTHOR]

Mitigating the impact of class imbalance datasets on classifiers poses a challenge to the machine learning community. Conventional classifiers do not perform well as they are habitually biased toward the majority class. Among existing solutions, the synthetic minority oversampling technique (SMOTE) has shown great potential, aiming to improve the dataset rather than the classifier. However, SMOTE still needs improvement because of its equal oversampling to each minority instance. Based on the consensus that instances far from the borderline contribute less to classification, a refined method for oversampling borderline minority instances (OBMI) is proposed in this paper using a two-stage Tomek link-finding procedure. In the oversampling stage, the pairs of between-class instances nearest to each other are first found to form Tomek links. Then, these minority instances in Tomek links are extracted as base instances. Finally, new minority instances are generated, each of which is linearly interpolated between a base instance and one minority neighbor of the base instance. To address the overlap caused by oversampling, in the cleaning stage, Tomek links are employed again to remove the borderline instances from both classes. The OBMI is compared with ten baseline methods on 17 benchmark datasets. The results show that it performs better on most of the selected datasets in terms of the F1-score and G-mean. Statistical analysis also indicates its higher-level Friedman ranking. [ABSTRACT FROM AUTHOR]

Online cross-modal hashing has received increasing attention due to its efficiency and effectiveness in handling cross-modal streaming data retrieval. Despite the promising performance, these methods mainly focus on the supervised learning paradigm, demanding expensive and laborious work to obtain clean annotated data. Existing unsupervised online hashing methods mostly struggle to construct instructive semantic correlations among data chunks, resulting in the forgetting of accumulated data distribution. To this end, we propose a Dynamic Prototype-based Online Cross-modal Hashing method, called DPOCH. Based on the pre-learned reliable common representations, DPOCH generates prototypes incrementally as sketches of accumulated data and updates them dynamically for adapting streaming data. Thereafter, the prototype-based semantic embedding and similarity graphs are designed to promote stability and generalization of the hashing process, thereby obtaining globally adaptive hash codes and hash functions. Experimental results on benchmarked datasets demonstrate that the proposed DPOCH outperforms state-of-the-art unsupervised online cross-modal hashing methods. [ABSTRACT FROM AUTHOR]

With the growth of global trade, port shipping is becoming more and more important. In this paper, an analysis of a ship's inbound and outbound track characteristics is conducted using the OD–Hausdorff distance. The accuracy and efficiency of trajectory data analysis have been enhanced through clustering analysis. Trajectories are arranged in a time sequence, and representative port segments are selected. An improved OD–Hausdorff distance method is employed to capture the dynamic characteristics of a ship's movements, such as speed and heading. Additionally, the DBSCAN algorithm is utilized for clustering, allowing for the processing of multidimensional AIS data. Data cleaning and preprocessing have ensured the reliability of the AIS data, and the Douglas–Peucker algorithm is used for trajectory simplification. Significant improvements in the accuracy and efficiency of trajectory clustering have been observed. Therefore, the main channel of the Guan River and the right side of Yanwei Port are usually followed by ships greater than 60 m in length, with a lateral Relative Mean Deviation (RMD) of 7.06%. Vessels shorter than 60 m have been shown to have greater path variability, with a lateral RMD of 7.94%. Additionally, a crossing pattern at Xiangshui Port is exhibited by ships shorter than 60 m due to the extension of berths and their positions at turns. Enhanced clustering accuracy has provided more precise trajectory patterns, which aids in better channel management. [ABSTRACT FROM AUTHOR]