bluuebunny/crossref_metadata_2025_split huggingface dataset:
EEG topographic features for assessing skill levels during laparoscopic surgical training
Authors: Manabe Takahiro , Walia Pushpinder , Fu Yaoyu , Intes Xavier , De Suvranu , Schwaitzberg Steven , Cavuoto Lora , Dutta Anirban 
Published: August 2022

Abstract
Objective: The objective of this study was to evaluate the differences in brain activity between expert surgeons and novice medical residents based on electroencephalography (EEG). The first sub-goal was to assess the Microstate EEGlab toolbox and BCIlab toolboxes for data analysis and classification of the topographical features for microstate-based Common Spatial Pattern (CSP) analysis. Then, the second sub-goal was to compare microstate-based CSP with the conventional regularized CSP approach.Methods: After IRB approval, ten expert surgeons and 13 novice medical residents were recruited at the University at Buffalo. After informed consent, the subjects performed three trials of laparoscopic suturing and knot tying with rest periods in-between the task trials. 32-channel EEG was performed during the task performance that was used to analyze spatial patterns of brain activity in 8 expert surgeons (2 dropouts due to data quality) and 13 novice medical residents. Microstate analysis was applied as preprocessing to improve the signal-to-noise ratio before CSP analysis, distinguishing expert surgeons' brain activity from novice medical residents.Results: Microstate-based CSP analysis identified the significant channels based on the maximum spatial pattern vectors at the scalp. While novices had primarily the frontal cortex involved for a maximum of the spatial pattern vectors at the scalp, the experts had the hotspot of the spatial pattern vectors over the frontal and parietal cortices. Simple linear discriminant analysis with 10-fold cross-validation achieved more than 90% classification accuracy with microstate-based CSP, while the conventional regularized CSP could reach around 80% classification accuracy.Conclusion and Discussion: Microstate-based CSP analysis can identify an optimal set of channels for evaluating the differences in brain activity between expert surgeons and novice medical residents. Future studies can apply microstate-based monitoring of the temporal dynamics of the brain behavior for an individualized adaptive VR-based training paradigm.
Distributed Reinforcement Learning-based Memory Allocation for Edge-PLCs in Industrial IoT
Authors: Fu Tingting , Peng Yanjun , Liu Peng , Lao Haksrun , Wan Shaohua 
Published: August 2022

Abstract
The rapid growth of the number of devices in the industrial Internet of things (IIoT) has a huge influence in the amount of data involved. In order to alleviate the computing load of cloud servers and reduce the delay of data processing, edge-cloud computing cooperation has been introduced to the IIoT. General programmable logic controllers (PLCs), which have been playing important roles in industrial control systems, start to gain the ability of processing large amount of industrial data and sharing the workload of cloud servers. This transforms them into edge-PLCs. However, continuous influx of multiple types of concurrent production data stream against the limited capacity of built-in memory in PLCs bring a huge challenge. Therefore, ability to reasonably allocate memory resources in edge-PLCs to ensure data utilization and real-time processing has become one of the core means in improving the efficiency of industrial processes. In this paper, to tackle dynamic changes of arrival data rate over time at each edge-PLC, we propose to optimize memory allocation with Q-learning distributedly. The simulation experiments verify that the method can effectively reduce the data loss probability while improve the system performance.
An Aseptic Approach Towards Skin Lesion Localization and Grading using Deep Learning and Harris Hawks Optimization
Authors: Balaha Hossam Magdy , Hassan Asmaa El-Sayed , El-Gendy Eman M. , ZainEldin Hanaa , Saafan Mahmoud M. 
Published: August 2022

Abstract
Skin cancer is the most common form of cancer. Hence, the lives of millions of people are affected by this cancer every year. Approximately, it is predicted that the total number of cases of cancer will double in the next fifty years. It is an expensive procedure to discover skin cancer types in the early stages. Additionally, the survival rate reduces as cancer progresses. The current study proposes an aseptic approach toward skin lesion detection, classification, and segmentation using deep learning and a meta-heuristic optimizer called Harris Hawks Optimization Algorithm (HHO). The current study utilized the manual and automatic segmentation approaches. The manual segmentation is used when the dataset has no masks to use while the automatic segmentation approach is used, using U-Net models, to build an adaptive segmentation model. Additionally, the meta-heuristic HHO optimizer is utilized to achieve the optimization of the hyperparameters using 5 pre-trained CNN models (i.e., VGG16, VGG19, DenseNet169, DenseNet201, and MobileNet). Two are collected (i.e., "Melanoma Skin Cancer Dataset of 10000 Images" and "Skin Cancer ISIC" dataset) from two publically available sources. For the segmentation, the best-reported scores are 0.15908, 91.95%, 0.08864, 0.04313, 0.02072, 0.20767 in terms of loss, accuracy, Mean Absolute Error, Mean Squared Error, Mean Squared Logarithmic Error, and Root Mean Squared Error, respectively. For the "Melanoma Skin Cancer Dataset of 10000 Images" dataset, from the applied experiments, the best reported overall accuracy is 97.08% by the DenseNet169 pre-trained model. For the "Skin Cancer ISIC" dataset, the best reported overall accuracy is 96.06% by the MobileNet pre-trained model. After computing the results, the suggested approach is compared with 9 related studies.
Low serum sodium level and prediction of febrile seizure recurrence
Authors: farah Monzer azhari mahgoub , Mahari Rahel Gebreab 
Published: August 2022

Abstract
Background: Febrile seizure (FS) is one of the most common neurologic disorders in children. Electrolyte imbalance especially hyponatremia may have an important role in triggering the febrile seizure. The measure of serum electrolytes will be useful in predicting further seizures. This study aimed to investigate the effect of relative hyponatremia on the risk of recurrent febrile seizures.Methods: This prospective study was performed on 115 children (6-60 months) with convulsions, at the pediatric department of Ibrahim malik teaching hospital. the serum sodium was measured immediately after abortion of seizures and each patient was followed for 24 hours for assessment of recurrence.Of the 115 children, 47 (40.9 %) develop recurrent febrile seizures. Of those the majority were males 36 (76.6%) (P=0.01), 52(45.2%) age less than 12 months(P=0.001) , 26 (68.4%) have parents consanguinity(P=0.000) , and were first born child25 (53.2%). The mean serum sodium levels in patients with one and recurrent attacks , were 132.1, 136.1 meq/l, respectively (P=0.00).Conclusions: The serum sodium level was ominously lower in the recurrence group compared to the non-recurrent group. Measurements of serum sodium levels and hyponatremia diagnosis have a key role in predicting the FC recurrence. Therefore, doctors should be careful during fluid administration to febrile seizure patients.
Development of Bigels Containing Antifungal Agent for Vaginal Infection
Authors: NNensi  
Published: August 2022

Abstract
Objective
Bigels are developed and are unique semi-solid formulations that have captivated the interest of many researchers due to their significant benefits over ordinary gels. The goal of this research work was to create and characterize new bigels for drug delivery applications by combining Hydroxypropyl methylcellulose hydrogel with sorbitan monostearate oil (coconut and olive) based organogel. The microscopy revealed the presence of both aqueous & oil phases as bigel.
Methods
The hydrogels and organogels were prepared individually and then the bigels were prepared by mixing hydrogel and organogel in a defined ratio and then they were evaluated using physiochemical tests, in-vitro drug release, microscopy, etc. The microstructures and physicochemical characteristics of the bigel were tested using microscopy, viscosity measurement, mechanical analysis, and differential scanning calorimetry analysis.
Results
Tube inversion tests reveal that the bigel doesn’t flow under its own weight till 167 mins. The microscopy suggested that the gels exhibited fiber-like structures due to the trapping of the organogel inside hydrogel molecules; this entrapment was demonstrated to be uniformly accomplished, resulting in formulation stability, and the DSC study reveals that the terbinafine is not decomposed also after formulating in bigel, and the terbinafine bigel was also found to be stable. The drug-loaded gels demonstrated effective antibacterial activity against Candida species. The formulated bigel shows initial release in 2 hours and slowly release later in 4 hours. The formed bigel is found to be stable after 3 months with a pH range of 7.07 ± 0.04, showing good spreadability and drug content was 99.99 ± 0.75.
Conclusions
Terbinafine, the drug of preference for the treatment of bacterial vaginosis, demonstrated diffusion-mediated drug release when placed into bigels. In general, the produced bigels might be employed as delivery vehicles for drugs delivered vaginally.
Gene Expression Analysis of RNA-Seq Data from Human Osteoarthritis Knee
Authors: R Nandhakumar , Elumalai Elakkiya , Gupta Krishna Kant 
Published: August 2022

Abstract
Osteoarthritis has always been one of the most researched degenerative disorders pervasively. Moreover, this is been classified under complex disorders with an emphasis on the clinical analysis for the older people. However, a genomic understanding of the hereditary impact of this disease seems lacking. The basic genomic analysis has identified various biomarkers, but lacks prognosis. In this direction, we have retrieved publicly available RNA- seq data from the NCBI Sequence Read Archive (SRA). Utilising the Galaxy browser, the data analysis was performed to identify the differentially expressed genes from SRP IDs {ERP105501, SRP322624}. The overlaps of these studies revealed 105 upregulated and 127 downregulated genes. These sets were further processed for multi-omic analysis through the databases. We identified specific process involvement through the Gene Ontology followed by hub genes through Protein -protein interactions. In addition to this, the transcription factors were identified from the list through BART search and miRNAs interacting with the identified hub genes through miRNet. This study aims to perform genomic analysis of osteoarthritis by identifying the differentially expressed genes among multiple tissues from human osteoarthritis knee joint and further analyse to find the key genes, biological pathways, transcription factors and miRNAs that can be considered as hub genes of the Osteoarthritis disease in humans.
Postural orthostatic tachycardia syndrome and post-acute COVID-19
Authors: Amekran Youssra , Damoun Narjisse , El Hangouche Abdelkader Jalil 
Published: August 2022

While the acute illness of COVID-19 was the initial focus of concern, there are increasing reports of patients with chronic symptoms, known as long-COVID. Dysautonomia may be a possible post-acute neurological complication explaining the persistent symptoms observed in long COVID. Postural tachycardia syndrome (POTS), a form of dysautonomia characterized by sustained tachycardia and orthostatic intolerance, has been increasingly reported in patients after SARS-CoV-2 infection. In this context, this review aimed to report and discuss the available literature pertaining to post COVID-19 POTS.
Global longitudinal strain and echocardiographic parameters of left ventricular geometry and systolic function in healthy adult Angolans: Effect of age and gender
Authors: Morais Humberto , Feijão Ana , Pereira Savarino de Victória 
Published: August 2022

Introduction: Studies on the normal echocardiographic reference values in Africans are limited.Objectives: This study aims to establish the normal left ventricular echocardiographic parameters for adult Angolans, stratified by gender and age.Methods: A cross-sectional study was performed involving healthy adults attending a diagnostic center in Luanda, Angola. The two-dimensional transthoracic echocardiography was performed according to the Guidelines of the American Society of Echocardiography and the European Association of Cardiovascular Imaging.Results: A total of 103 men (47.5%) (mean age: 39.5±10,8) and 114 women (52.5%) (mean age: 43.0±12,5 years) were included. Compared to men, women were older (p=0.025) and had a significantly smaller body surface area (BSA) (p<0.001). Left ventricular wall thickness, left ventricular end-diastolic dimension and volume (LVEDV), left ventricular mass (LVM) and LVM indexed to BSA (LVMi) were significantly lower in women (p<0.005). LVEDD indexed to BSA and left ventricular ejection fraction (LVEF) were significantly higher in women than in men (p=0.007), (p=0.01), respectively. Mitral annular plane systolic excursion, LVEF by strain, and global longitudinal strain showed no gender differences. Posterior wall thickness showed a statistically significant increase in the older groups (p=0.043). The VST, relative wall thickness, LVM, and LVMi showed no significant differences between age categories. In turn, the shortening fraction and the ejection fraction increase with age.Conclusion: For a more sensitive morphological and functional assessment of the left ventricle, it is necessary to take into account the gender and age of the individual.
Household behavioral intention, environmental habit and attitude related to efficient water management: an empirical analysis on pro-environmental behavior among urban residents
Authors: Sarpong Kwabena Agyarko , Amankwaa Gordon 
Published: August 2022

Abstract
The management of water utilities in urban contexts, which are particularly prone to water shortages and where future water security is of the utmost importance, has become a challenging task for the majority of municipal and regional planners. The present study attempts to evaluate how environmental knowledge and values influence possible pro-environmental behavior with respect to household water consumption behavior, attitudes, and habits among 500 (n = 313) urban dwellers in an urban setting in Jiangsu Province, China. We adapted the Theory of Planned Behavior (TPB) to elucidate pro-environmental behaviors while exploratory factor and Spearman correlation analysis were applied to datasets elicited from respondents. The study establishes important positive correlations using Spearman's rank (r = 0.50) based on the findings of our empirical analysis, which suggest that individuals with strong environmental values and rich environmental knowledge are more likely to exhibit behaviors, attitudes and habits that result in effective household water use and management. Additionally, based on the magnitude of the factor loadings, the key results from principal factor analysis were as follows: efficient household water consumption is contingent on fixing household leaks (0.73) and replacing old shower fixtures and water-using appliances (0.66). Nevertheless, China's water resources are insufficient and require efficient management (0.66), the quality of tap water supplies is compromised and cannot be trusted (0.66) and there has been little to no improvement in water quality and availability in the past 10 years in China (0.69). This research reinforces the role of knowledge in shaping behavior, and has policy effects and could serve as a benchmark for measures to reduce household water use.
PENENTUAN SISTEM PENGUMPULAN SAMPAH DAN TEMPAT PENAMPUNGAN SEMENTARA DESA SAWAHMULYA, SANGKAPURA
Authors: Fikriyah Nuril , Meidiana Christia , Sari Kartika Eka 
Published: August 2022

Pengelolaan sampah menjadi tantangan serius bagi pengelola dan pembuat kebijakan di perkotaan maupun pedesaan dan oleh karena itu, solusi masalah sampah harus terintegrasi dan berkelanjutan. Desa Sawahmulya merupakan Desa yang ditetapkan sebagai zona prioritas pelayanan sampah di Pulau Bawean. Namun, penanganan sampahnya belum optimal. Pelayanan pengumpulan Desa Sawahmulya belum tersedia di setiap dusun. Keberadan 3 TPS seharusnya dapat melayani seluruh dusun. Tetapi, masih ada dusun yang belum terlayani sehingga menimbulkan TPS ilegal yang berada di tepi pantai dan permasalahan lainnya. Oleh karena itu, penelitian ini bertujuan untuk mengidentifikasi sistem pengelolaan sampah, menghitung efektivitas pengumpulan sampah, mengevaluasi kinerja TPS, menentukan lokasi dan lingkup pelayanan TPS, serta sistem pengumpulan sampah di Desa Sawahmulya.Â Hasil penelitian menunjukkan pengumpulan sampah di Desa Sawahmulya belum efektif, hanya 36% sampah yang terkumpul oleh petugas di TPS, kondisi TPS Sawahlaut sudah melebihi kapasitasnya yakni 143%, dari segi lokasi keberadaan lokasi TPS masih belum memenuhi kriteria kesesuaian lokasi TPS sehingga direkomendasikan untuk membangun TPS Baru di Kebundaya yang mampu melayani 500 KK dan melakukan pemerataan daerah lingkup pelayanan di setiap TPS. Penentuan sistem pengumpulan meliputi pola pengumpulan komunal dan individual tidak langsung dengan memerlukan 7 rute pengumpulan, dan penambahan moda bermotor dengan ritasi sebanyak 2-3 sesuai area pelayanannya.Kata Kunci: Pengelolaan Sampah, Sistem-Pengumpulan-Sampah, TPS.
Influence of 2 Digital Exercise Modules of a Multimodular System on Balance and Leg Strength Under Consideration of Use Adherence: Prospective Cohort Study
Authors: Venek Verena , Kranzinger Christina , Jungreitmayr Sonja , Ring-Dimitriou Susanne , Schwameder Hermann , Stöggl Thomas 
Published: August 2022

Background
To empower healthy aging, digital solutions embed multiple modules for physical activity, cognitive health promotion, and social engagement. Integrating new empowering technologies such as digital exercise monitoring requires assessment measures and analysis procedures, considering variable compliance of users with different modules.

Objective
This study aims to assess the influence of a tablet-based and a feedback system–based exercise module on balance and leg strength by considering use adherence instead of the use of the entire multimodular system.

Methods
In the prospective cohort study within the fit4AAL project, 83 users (n=67, 81% women; n=16, 19% men; mean age 66.2, SD 2.3 years) used the 2 digital exercise modules of a multimodular physical activity promotion system for >18 weeks. A data-driven clustering method based on the average use frequency of the exercise modules determined the number of user types that met the World Health Organization–recommended training frequency of at least twice per week. On the basis of this use adherence, statistical analysis was performed with features of functional performance tests (unipedal stance, 30-second chair rise, Y-balance, and hurdle step tests). The tests were conducted 6 months before the intervention, immediately before the intervention, and after the intervention, comparing the baseline phase with the 3 feedback use groups of the study (using only the tablet, the tablet and the feedback system, or only the feedback system).

Results
Of the 83 users, 43 (52%) met the World Health Organization–recommended frequency of muscle-strengthening activities. Overall, the feedback use groups achieved, on average, more chair rises in 30 seconds than the baseline group (P=.01; moderate effect size of 0.07). Of the 43 users, 26 (60%) additionally used the feedback system–based exercise module. They improved in balance compared with the users using either the tablet or the feedback system (P=.02). In addition, they improved their leg strength within the group (P=.04) and compared with the baseline (P=.01).

Conclusions
The additional use of a feedback system showed a tendency to positively maintain and influence the already exceptionally high functional performance of older adults. Considering use adherence in future multimodular system studies is crucial to assess the influence of single and combined use of exercise modules on functional performance.
Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach
Authors: Rastpour Amir , McGregor Carolyn 
Published: August 2022

Background
Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers.

Objective
The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers).

Methods
We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments.

Results
The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system’s knowledge increased the utility of our highly deidentified data and improved the predictive power of the models.

Conclusions
The random forest method, enhanced with the system’s knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.