Miracle-dz/UniFAR-FTUs huggingface dataset:
Recreational Uses And Motivations Of Visitors In Seaside Wetlands Of Costa Brava (spain)

Seaside wetlands bring together a huge quantity of services and functions, especially ecological and social. All of them depend on the ecological quality of ecosystems and on the equipment and service that allow them to be enjoyed. Tourism is an economic segment, carrying an important weight in most coastal regions around the world. Hence a complete environmental management of seaside wetlands should include the touristic perspective. In this communication, we analyse usage and motivation in three seaside wetlands of the Costa Brava (Spain) through a survey realized in the months of the highest occurrence of visitors (from June to September 2015). From the results, we highlight the high number of visitors who use the sites for recreational purposes (49%), such as running or cycling, in comparison with visitors who stated aesthetic motivations (16%). Many visitors also stated no motivation for visiting the sites (31%); they use them as car parks to go to the beach or as a byway to other sites. On the other hand, most visitors stressed the landscape (30%) or the degree of naturalness (29%) as a positive element of seaside wetlands, while the majority of negative elements are linked to bad management of the site (36%). When we requested a landscape valuation in a five-point scale, a significant number of high values were shown. Furthermore, we found a link between evaluation and tourism typology (local, national or foreign, lodged or excursionist) and motivation for visit (recreational, aesthetic or without). The principal conclusion is that, despite the fact that the main uses of the seaside wetlands are recreational, tourists appreciate landscape quality and degree of naturalness in sites where they develop their activities. This assigns to tourism, especially seaside tourism, an active role in conservation of seaside wetlands.
NIST Special Publication 800-88 Guidelines for Media Sanitization

This is a Hard copy of the NIST Special Publication 800-88, Guidelines for Media Sanitization is a setup of Recommendations of the National Institute of Standards and Technology. The information security concern regarding information disposal and media sanitization resides not in the media but in the recorded information. The issue of media disposal and sanitization is driven by the information placed intentionally or unintentionally on the media. With the advanced features of todays operating systems, electronic media used on a system should be assumed to contain information commensurate with the security categorization of the systems confidentiality. If not handled properly, release of these media could lead to an occurrence of unauthorized disclosure of information. Categorization of an information technology (IT) system in accordance with Federal Information Processing Standard (FIPS) 199, Standards for Security Categorization of Federal Information and Information Systems, is the critical first step in understanding and managing system information and media. Based on the results of categorization, the system owner should refer to NIST Special Publication (SP) 800-53, Recommended Security Controls for Federal Information Systems, which specifies that, the organization sanitizes information system digital media using approved equipment, techniques, and procedures. The organization tracks, documents, and verifies media sanitization and destruction actions and periodically tests sanitization equipment/procedures to ensure correct performance. The organization sanitizes or destroys information system digital media before its disposal or release for reuse outside the organization, to prevent unauthorized individuals from gaining access to and using the information contained on the media. Disclaimer This hardcopy is not published by National Institute of Standards and Technology (NIST), the US Government or US Department of Commerce. The publication of this document should not in any way imply any relationship or affiliation to the above named organizations and Government.
GC/MS Profiling, Anti-Collagenase, Anti-Elastase, Anti-Tyrosinase and Anti-Hyaluronidase Activities of a Stenocarpus sinuatus Leaves Extract

Today, skin care products and cosmetic preparations containing natural ingredients are widely preferred by consumers. Therefore, many cosmetic brands are encouraged to offer more natural products to the market, such as plant extracts that can be used for their antiaging, antiwrinkle, and depigmentation properties and other cosmetic purposes. In the current study, the volatile constituents of the hexane-soluble fraction of a Stenocarpus sinuatus (family Proteaceae) leaf methanol extract (SSHF) were analyzed using GC/MS analysis. Moreover, the antiaging activity of SSHF was evaluated through in vitro studies of anti-collagenase, anti-elastase, anti-tyrosinase, and anti-hyaluronidase activities. In addition, an in silico docking study was carried out to identify the interaction mechanisms of the major compounds in SSHF with the active sites of the target enzymes. Furthermore, an in silico toxicity study of the identified compounds in SSHF was performed. It was revealed that vitamin E (α-tocopherol) was the major constituent of SSHF, representing 52.59% of the extract, followed by γ-sitosterol (8.65%), neophytadiene (8.19%), β-tocopherol (6.07%), and others. The in vitro studies showed a significant inhibition by SSHF of collagenase, elastase, tyrosinase, and hyaluronidase, with IC50 values of 60.03, 177.5, 67.5, and 38.8 µg/mL, respectively, comparable to those of the positive controls epigallocatechin gallate (ECGC, for collagenase, elastase, hyaluronidase) and kojic acid (for tyrosinase). Additionally, the molecular docking study revealed good acceptable binding scores of the four major compounds, comparable to those of ECGC and kojic acid. Besides, the SSHF identified phytoconstituents showed no predicted potential toxicity nor skin toxicity, as determined in silico. In conclusion, the antiaging potential of SSHF may be attributed to its high content of vitamin E in addition to the synergetic effect of other volatile constituents. Thus, SSHF could be incorporated in pharmaceutical skin care products and cosmetics after further studies.
Society's Perspectives on Contemporary Islamic Law in Indonesia through Social Media Analysis Technology: A Preliminary Study

The social, cultural, and technological developments of society are unavoidable. This has an impact on the development of Islamic Law, which keeps all Muslim activities in the right corridor. Contemporary Islamic law, known as Contemporary Islamic Law, has also developed to answer new societal problems. Various views on Contemporary Islamic Law in solving multiple issues certainly reap various responses from the community and scholars. These views are often conveyed through social media such as Youtube, Instagram, Facebook, and Twitter. Therefore, this article aims to discuss a preliminary study of text analysis techniques used to find out the views of the community and Ulama on Contemporary Islamic Law issues computationally and automatically. This initial study reviews the methods and techniques that will be used, namely the Indonesian National Work Competency Standards (SKKNI) methodology for data science. This study will also use a sentiment analysis approach, topic modeling, and pattern analysis to find out people's views on issues of Contemporary Islamic Law through social media. The algorithm used for sentiment analysis is the Multinomial Naïve Bayes Classifier (MNBC), for topic modeling is Latent Dirichlet Allocation (LDA), while for pattern analysis using the Prefix-projected Sequential Pattern Mining (PrefixSpan) algorithm. The model generated from sentiment analysis, topic modeling, and pattern analysis will be evaluated by measuring the confusion matrix, coherence value, and silhouette coefficient value. In addition, analysis and interpretation of the model results will be carried out in-depth qualitatively by involving the views and thoughts of Islamic Law experts.
EVALUATION OF THE IMPACTS OF TRAINING ON EMPLOYEE PERFORMANCE IN THE UK HOTEL INDUSTRY: AN ANALYSIS OF EMPLOYEES’ PERSPECTIVES AT MARRIOTT INTERNATIONAL

The purpose of this paper is to evaluate the impact of training on employee performance in the UK hotel industry. It has also analysed the effectiveness of employee performance, impacts of on-the-job training on employee performance, the impact of off-the-job training on employee performance, and the impact of online training on the performance of the employees of Marriott International hotels. This study was conducted based on the deductive approach and positivism philosophy. Three hypotheses were developed based on theory and literature. Primary data was collected from the employees who were participated in different types of training provided by Marriott International hotels. An online questionnaire survey through the Kwik Survey platform was used to collect primary data from the respondents. A sample of 172 employees was chosen through a voluntary response sampling technique. The data were analysed through SPSS using standard deviation, mean, correlation, regression, and charts. This study found that on-the-job training and off-the-job training have significant impacts on employee performance, while online training has no significant impact on employee performance. It is found that on-the-job training has a significant impact on employee performance as β = 0.846, and p = 0.000. Similarly, off-the-job training has a significant impact on employee performance as β = 0.572 and p = 0.26. And online training has no significant impact on employee performance as β = 0.301 and p = 0.276, which is more than 0.05. Similarly, evaluation of the mean of the scores for employee performance variables suggests that every variable of performance has a mean score of more than 3, and the mean score for overall employee performance is 4.03, which equals 80.6%. This means employee performance at Marriott International hotels based on employees' own ratings is highly effective.
Repetitive transcranial magnetic stimulation for prophylactive treatment of chronic migraine: A randomised, single-blind, parallel-group, sham-controlled trial

Background: Pilot studies suggest that repetitive transcranial magnetic stimulation could be effective in migraine. We studied its efficacy for prophylaxis of chronic migraine. Methods: We undertook a randomised, single-blind, sham-controlled study in patients with chronic migraine. Subjects were randomly allocated to sham, high-frequency repetitive transcranial magnetic stimulation (HF-rTMS) or low-frequency repetitive transcranial magnetic stimulation (LF-rTMS). The primary outcome was the reduction of attacks at four weeks. In addition, reduction of severity of headache based on the Visual Analogue Scale score at two & four weeks and a functional disability score at four weeks was recorded. Results: One hundred and eight patients were allocated to sham stimulation (n=36), HF-rTMS (n=36) and LF-rTMS (n=36) groups. The number of attacks per month decreased to 7.05+/-1.45 at four weeks post-rTMS in the HF-rTMS (p<0.001) as compared to the LF-rTMS (10.61+/-1.69) and sham (10.83+/-3.37) groups. The VAS score in the HF-rTMS group was significantly lowered (p < 0.001) from 8.61+/-1.15 at baseline to 6.63+/-0.96, and 7.19+/-0.98 at two and four weeks respectively. The duration of headache in hours over 4 weeks reduced from 32.4+/-14.9 to 11.6+/-6.14 in the HF-rTMS group (p<0.001), but did not change significantly in the LF-rTMS (30.4+/-11.9 to 27.8+/-14, p>0.05) and sham (26.8+/-11.06 to 37+/-15, p>0.05) in sham groups. There was no significant difference in functional disability between all the study groups at four weeks. Conclusion: High-frequency, but not low-frequency, repetitive transcranial magnetic stimulation may be useful in management of chronic migraine.
A deep learning-based clinical target volume segmentation in female pelvic MRI for radiation therapy planning

Brachytherapy (BT) combined with external beam radiotherapy (EBRT) is the standard treatment for cervical cancer. Accurate segmentation of the tumor and nearby organs at risk (OAR) is necessary for accurate radiotherapy (RT) planning. While OAR segmentation has been widely studied, showing promising performance, accurate tumor and/or corresponding clinical target volume (CTV) segmentation has been less explored. In cervical cancer RT, magnetic resonance (MR) imaging is used as the standard imaging modality to define the CTV, which is very challenging as the microscopic spread of tumor cells is not clearly visible even in MRI. We propose a two-step convolutional neural network (CNN) approach to delineate CTV from T2-weighted (T2W) MR images. First, a human expert needs to select a seed point inside the CTV region, from which the MR volume is cropped to produce a region of interest (ROI) volume. The ROI volume is then fed to an attention U-Net to produce CTV segmentation. A total of 213 MR datasets from 125 patients was used to develop and evaluate the proposed methodology. The network was trained using 2-dimensional (2-D) slices extracted in the axial direction from 183 MR datasets and augmented using translation operation. The proposed method was tested on the remaining 30 MR datasets and yielded Mean±SD dice similarity coefficient (DSC) of 0.80±0.06 and Hausdorff distance (95th percentile) of 3.30±0.58 mm. The performance of our method is superior to the standard U-Net-based method (pvalue< 0.005). Although the proposed method is semi-automatic, the observer variability coefficient of variation (CV) was reported as 2.86% that demonstrated the high reproducibility of the algorithm.
A perspective on the ecological and evolutionary consequences of phenological variability in lake ice on north-temperate lakes

Climate change is leading to shifts in not only the average timing of phenological events, but also their variance and predictability. Increasing phenological variability creates a stochastic environment that is critically understudied, particularly in aquatic ecosystems. We provide a perspective on the possible implications for increasingly unpredictable aquatic habitats, including more frequent trophic asynchronies and altered hydrologic regimes, focusing on ice-off phenology in lakes. Increasingly frequent phenological extremes may limit the ability of organisms to optimize traits required to adapt to a warming environment. Using a unique, long-term ecological dataset on Escanaba Lake, WI, USA as a case study, we show that the average date of ice-off is shifting earlier and becoming more variable, thus altering limnological conditions and yielding uncoupled food web responses with ramifications for fish spawn timing and recruitment success. A genes-to-ecosystems understanding of the responses of aquatic communities to increasingly variable phenology is needed. Our perspective suggests that management for diversity, at the intra- and inter-specific levels, will become paramount for conserving resilient aquatic ecosystems.
Learning Discriminant Spatial Features with Deep Graph-based Convolutions for Occluded Face Detection

The use of face masks has become a widespread non-pharmaceutical practice to mitigate the transmission of COVID-19. However, achieving accurate facial detection while people wear masks or similar face occlusions is a major challenge. This paper introduces a model to detect occluded or masked faces based on fused convolutional graphs. This model includes a deep neural architecture with two spatial-based graphs that rely on a set of key facial features. First, a distance graph is used to identify geographical similarity between the facial nodes that represent certain key face parts. Second, a correlation graph is formulated to compute the correlations between every two nodes that represent two different augmented facial modalities. Transfer learning is then performed using a pretrained deep architecture as a baseline to map the abstract semantic information into multiple feature filters. Then, discriminant graph convolutions are constructed based on the fusion of distance and correlation graphs. This model evaluates two tasks of facial detection, which are the binary detection of masked or unmasked faces, and multi-category detection of masked, unmasked, or occluded face with no mask. The experimental results on two benchmarking real-world datasets show that the proposed deep learning model is highly effective with an accuracy of 98% achieved in binary detection. Even with high variance in image occlusions, our proposed model has great promise in detecting and distinguishing between types of facial occlusion with an accuracy of 86% reported in multi-category detection.
Now I understand! Success factors among high-achieving undergraduate Hispanic students majoring in engineering at a research university

Now I Understand! Success Factors among High-Achieving Undergraduate Hispanic Students Majoring in Engineering at a Research University. (August 2012) Bari L. Brookins, B.M. E. , Oklahoma Baptist University; M.A., Florida Atlantic University Chair of Advisory Committee: Dr. Fred A. Bonner, II This dissertation examined the perceptions of high-achieving undergraduate Hispanic students majoring in engineering with regard to their academic success. As the largest and fastest growing minority group in the US, Hispanics are underrepresented among the racial and ethnic compositions of students enrolled in undergraduate engineering programs. Factoring in the overall decline in the number of graduates in engineering, as well, enhances the challenges this will bring to need for a racially and ethnically representative US workforce. While engineering is an academically demanding discipline, some students not only succeed, but excel. To understand the factors that contributed to their academic success, seven high-achieving undergraduate engineering students were interviewed to examine their undergraduate experiences at Texas
Students’ Perception on the Effectiveness of Online Classes During Pandemic

The present study aimed at finding out students' perception on the effectiveness of online classes conducted during the pandemic situation of COVID -19. It attempted to find the students' perceptions for effective classes. I applied cross sectional survey design. For this, survey questionnaires were prepared in google form. Those students who were taking online classes at Nilkantha Multiple Campus in bachelor and master levels and education, arts and commerce faculties were selected as respondents for my research through convenience sampling procedure. Students were requested to reply the questions posted in their Facebook group of each classes. The data were collected, analyzed and interpreted by the help pie-charts and diagrams and descriptions. The result of this study indicated that respondents were not satisfied with their online classes due to low participation, lack of enough interaction, unattractive content delivery and the problems of poor internet and frequent power cut. Even though online classes were reported to be convenient in term of saving time, still the students perceived it to be less effective.
Data-Driven Risk-Sensitive Control for Personalized Lane Change Maneuvers

Most current research in the field of autonomous vehicle control assumes that all vehicles will follow the same patterns of automated driving behavior, resulting in systems with “conservative” or “average” driving styles. These systems may not be acceptable to drivers who prefer a more aggressive style of driving, however, while extremely cautious drivers may consider the standard outputs to be too aggressive. To address this problem, in this paper, we introduce Risk Sensitive Control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporates them into a receding-horizon controller. RSC uses a meta-learning algorithm to update the parameters of the cost function, continuously improving the controller online as more and more driving data is gathered from the user and subjective risk feedback. An estimator takes into account individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and its constraints. We test this approach using five lane change scenarios, some safe and some risky, with thirty real drivers in a CARLA simulation environment. Our quantitative and qualitative evaluations demonstrate that the proposed framework is able to generate a user’s preferred driving maneuvers during lane changes, i.e., control commands the user associates with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user’s own driving behavior.
Peer Power! Secure Peer Attachment Mediates the Effect of Parental Attachment on Depressive Withdrawal of Teenagers

Adolescents’ depressive social withdrawal is a relevant concern for mental health professionals, and it is widespread among community teenagers in form of subclinical symptoms. Different studies suggest that insecure attachment representations increase the adolescents’ likelihood to show symptoms of withdrawal (e.g., loneliness). This study explored the effect of the general attachment internal working model (IWM) and the independent and cumulative effects of the specific attachment representations of parents—in terms of secure base/safe haven—and peers on adolescents’ withdrawal. Additionally, the mediation of peer attachment on the effect of parental attachment on symptoms was explored. All analyses were conducted controlling for the difference between teenagers living with parents together or divorced/separated, as children of divorcees are considered more exposed to stressors. Ninety-one adolescents aged 12–17 years old were assessed online during the COVID pandemic period, employing the Youth Self-Report to assess withdrawal and the Friends and Family Interview to assess attachment-general IWM and attachment-specific representations. Results show no influence of parents together/separated or of the general IWM on withdrawal, but higher parent secure base/safe haven and peer attachment cumulatively predicted 10–21% less withdrawal. Moreover, more positive peer attachment mediated 61% of the effect of the parental secure attachment on withdrawal, revealing an indirect effect of parental attachment on withdrawal through peer attachment. In conclusion, both parents and peers are influential on adolescent mental health, and fostering positive peer relationships can buffer the effect of dysfunctional family relationships on teenagers’ withdrawal.
Evaluation of Genetic Structure of Pistachio Through Whole Genome Resequencing

Pistachio ( Pistacia vera L.) is the only edible and cultivated species. Pistachio is the only economically importance and dioecious species in the genus Pistacia . There are basic problems in pistachio breeding such as dioecious flower structure, long juvenile period and alternate bearing. These problems can be overcome not with classical breeding methods, but with modern molecular breeding methods. In this study, very high numbers of single nucleotide polymorphism (SNP), insertion/deletion (InDel), structural variants (SV) and copy number variation (CNV) were determined by using the next generation sequencing data of the pistachio genotype obtained with 15x sequencing coverage. A total of 1,785,235 SNP, 260,683 InDel, 5,227 SV and 1,914 CNV variants identified in PvF217 pistachio genotype. The variant density was calculated as one variant per 292 base. The distribution of the obtained variants to the Siirt reference genome was obtained. In addition, all variants were annotated to the reference genome and exonic and genomic variants were described using Annovar. These data will be used to consist of a molecular database in pistachio breeding for DNA fingerprinting, discovering unique cultivar specific alleles and to identify quantitative trait loci related to important nut traits.