Miracle-dz/UniFAR-FTUs huggingface dataset:
A review on essential oil analyses and biological activities of the traditionally used medicinal plant Thymus vulgaris L

Since the old times, seeds producing plants have played a vital role in the progress of human culture to treat diseases. Medicinal plants are used traditionally by the local communities to treat diseases. Recently, a report has shown that more than 250,000 flowering plant species are available globally. Scientists are continuously working on higher plants to help and understand plant poisonousness and to defend humans and animals from natural toxins. A plant`s toxicity and its medical use are dependent on the plant’s volatile phytochemicals. Thymus vulgaris L is a common aromatic plant used widely as a folk medicine to treat various diseases by different ethnic communities around the globe including the Sultanate of Oman. Previous studies in Oman showed that the selected plant species contains several groups of phytochemicals such as essential oils and secondary metabolic compounds they can enhance their biological and toxicological activities. Therefore, the aim of the present review is to explore the volatile phytochemicals, biological and toxicological features of Thymus vulgaris grown in Oman. The results can be helpful for discovering new drugs to treat asthma, cough, chronic bronchitis and other infectious diseases. In conclusion, this review provides information on the volatile phytochemicals, pharmacological and toxicological aspects of the selected plant species. ARTICLE HISTORY Received: Nov. 27, 2021 Revised: Jan. 19, 2022 Accepted: Feb. 27, 2022
Nurturing self-compassionate performers

ABSTRACT Performers, such as athletes, actors, dancers, and musicians, function within high pressure competitive and often hostile or critical environments. These individuals can be prone to a range of self-critical cognitions and behaviours which may make them susceptible to mental ill-health and psychological distress. Fostering and nurturing self-compassion is one approach to motivating behaviour and health which may be relevant within training and support settings. Self-compassion has been associated with a range of positive outcomes in the general community, as well as in those functioning in performance spaces, primarily sport. In this commentary, we outline why cultivation of self-compassion may be an increasingly valuable method for psychologists working with individuals from these environments, particularly within the context of a decimated industry during the global pandemic. We provide a brief overview of relevant research, before exploring practical and applied approaches (including transcripts) that can be used within therapeutic settings. KEY POINTS What is already known about this topic: Performers such as athletes, dancers, actors, and musicians are exposed to a range of stressors which can increase susceptibility to mental ill-health. Self-compassion can be cultivated in therapy and is associated with better mental health outcomes. Due to a range of hostile self-critical tendencies that are commonly seen in performers, some individuals may be resistant to concepts of self-compassion. What this topic adds: We outline a rationale for why psychologists working with performers should aim to nurture self-compassionate tendencies and practices. We outline methods and practical approaches to doing this in session, as well as describe potential hurdles and barriers for implementation.
Implicit and Explicit Attitudes toward Foods Derived from Genome Editing and Genetic Modification Technologies under Different Information Treatments

ABSTRACT Genome editing technologies represent a potential revolution in plant breeding methods. However, there is a limitation in understanding the consumers’ attitudes toward foods derived from this novel technology. Especially, the lack of knowledge and confusion in differentiating with genetically modified (GM) products could potentially restrict further development of genome-edited (GE) foods. Therefore, a survey of 407 Vietnamese consumers was conducted to analyze the implicit and explicit attitudes toward GE foods compared to GM foods under different information treatments. The implicit association test (IAT) was used for implicit evaluation, and semantic differential scales were used for explicit evaluation. As our important results, under the context of condensed information, the implicit measure shows a high correlation between attitudes toward GE and GM foods. This result is one of the initial pieces of evidence that has been shown about the relationship among consumers’ attitudes toward GE and GM foods. And as our result has been pointed out that providing in-depth information help to reduce the correlation between attitudes toward GE and GM foods. This evidence is important to suggest effective risk-benefits communication to help reduce negative attitudes toward GE food and increase consumers’ acceptance.
Efficacy of automated electronic medical records (EMR) notification to promote provider intervention for severe depression: a retrospective record review

Background: Depressive symptoms as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS) Depression (D; PROMIS-D) score is a patient-specific independent predictor of decreased improvement after orthopaedic surgery. Despite evidence that depression predicts worse surgical outcomes, data show orthopaedic surgeons rarely address mental health with their patients. Methods: The authors conducted a retrospective chart review of all patient visit data from October 1, 2018 through December 31, 2020, at Cedars-Sinai Orthopaedic Center outpatient clinics to determine if a best practices alert built into the electronic medical record (EMR) notifying surgeons of their patients who had PROMIS-D scores of 70+ (severe depression) would be impetus enough for providers to intervene. Records with PROMIS-D scores of 70+ (N=361) were selected and reviewed; the authors examined how often the surgeons documented an action in the record in response to the depression notification. Results: Surgeons were found to take action in only 28.5% (N=103) of the visits with qualifying scores. Conclusions: Notification of patient depression status alone was insufficient to persuade most surgeons to discuss mental health with their severely depressed patients. To empower surgeons to have these important conversations, extant barriers need to be identified and addressed. The association with rates of intervention and individual-level factors of both patients and providers could be explored. Level of Evidence: Level IV.
MRI Brain Tumor Detection Using Boosted Crossbred Random Forests and Chimp Optimization Algorithm Based Convolutional Neural Networks

: Early detection of the brain tumors can help in providing sufficient treatment to completely cure the disease. Machine Learning (ML) and Deep Learning (DL) algorithms are utilized predominantly in recent years for the tumor detection process from the brain Magnetic Resonance Imaging (MRI). In this paper, an effective tumor detection framework is proposed by utilizing a hybrid segmentation algorithm and a new hyper-parameter optimized Convolutional Neural Networks (CNN) based classifier using Chimp Optimization Algorithm (COA). The framework includes pre-processing, segmentation, feature extraction and classification stages. The pre-processing stage performs the denoising using wavelet filter and image enhancement using adaptive histogram equalization. Then the Boosted Crossbred Random Forests (BCRF) is used to segment the regions of interest to identify skull, tumor and lesions. The features are extracted using the Grey-Level Co-occurrence Matrix (GLCM) and Gabor filter. Finally, the tumor classification is achieved using the COA-CNN. The COA-CNN is developed by utilizing the COA to tune the hyper-parameters of CNN to obtain optimal CNN architecture. Experimental evaluation was performed on a dataset of 3264 MRI brain images containing normal and tumor images. No tumor, Glioma, Meningioma, and Pituitary tumor are classes of the MRI images which are correctly classified by the proposed BCRF and COA-CNN based detection model with a testing accuracy of 95.18% and reduced processing time. The results indicate the efficacy of the proposed method in classifying brain tumors for assisting physicians in early tumor diagnosis and treatments.
Experimental investigation of the effect of weave type on the mechanical properties of woven hemp fabric/epoxy composites

Natural fiber composites have a growing popularity as alternative materials to petroleum-based plastics and glass fiber reinforced composites due to various economic and environmental reasons. Textile preforms such as woven, knitted, braided, nonwoven, and multiaxial fabrics are commonly used in composite industry due to their high mechanical properties, tailorability, ease of handling during production, and versatility in material design. A range of properties can be obtained by varying the weave pattern and fabric architecture in woven fabric composites. In this study, epoxy composites reinforced with hemp woven fabrics with four different weave types were produced. The weave types chosen were quasi-unidirectional (UD), plain, basket 2/2, and twill 2/2 which are commonly used in the composite industry. Fabric properties such as yarn angle, yarn density, and crimp ratio were determined to evaluate their possible effect on composite properties. Tensile and three-point flexural tests were conducted in order to determine the effect of weave type on the mechanical properties of the resulting composites. It was found that UD composites show the highest tensile strength, tensile modulus, flexural strength, and flexural modulus in the 0° (yarn) direction. Plain weave fabric composites showed the second highest tensile and flexural strength and moduli followed by basket 2/2 weave composites. Composites reinforced with twill 2/2 woven fabrics showed the lowest mechanical properties due to high yarn crimp and yarn angle, less balanced structure, and resulting shear forces involved during various loading conditions.
Structure design and simulation of micromechanical silicon oscillating accelerometer

Silicon MEMS resonant accelerometer takes quasi-digital signal as its output.The structure of a micromechanical silicon oscillating accelerometer was designed based on a micro leverage institution and DETF(double-ended tuning fork) resonator.The design essentials are presented based on its working principle and the error sources.The whole structure was designed combining with the present processing technology.The structure parameters' influences on the accelerometer performance were discussed by using MATLAB,and the parameters are optimized.Simulation of the overall structure was carried out by ANSYS.The designed accelerometer has a normal frequency of 29 kHz,a scale factor of 95 Hz/g,a measuring range of ±50g,and its differential output frequency has good linearity,with correlation coefficient of 0.099%. The study shows that,the resonator error comes from the principle is the restrictive factor of the measuring range.Resonator amplitude should not be too big.Considering the existing process condition,the width of the vibrating beam produces the biggest error,which has a great influence on the performance of resonator.
Health and Well-being at the Transition to Adulthood: Examining Unmet Social and Health Needs Among Young Women

The present study examined the assets and unmet social and health needs of young women at the transition to adulthood in a particular area of a small city in North Carolina. We conducted in-depth semi-structured interviews with 15 participants who identified as women (age 18–29). Using a deductive thematic analysis approach, we identified three themes and 13 sub-themes across the topic areas of interest in our interview: First, childhood experiences and familial histories created disparate physical and mental health needs in emerging adulthood. Second, new responsibilities and/or traumatic experiences led to recognition of mental health as a critical factor. Third, young women found support for their health and well-being from family and friends, with room for improvement among community institutions and structures. Implications are discussed for young women, health care providers, and social systems in which young women are embedded.
MENULIS HIKAYAT DENGAN MENGGUNAKAN METODE KEARIFAN LOKAL DAERAH BALARAJA

Abstrak. Kekayaan Indonesia tidak hanya berupa sumber daya alam.Indonesia memiliki kekayaan berupa karya sastra yaitu naskah kuno.Metode pelaksanaan yang dilakukan dengan berbagai metode sebagai berikut.Pertama, metode ceramah dan tanya jawab. Tim abdimas memberikan materi tentang hikayat dan memperkenalkan beberapa hikayat yang ada.Kedua, praktek. Dalam kegiatan abdimas ini akan dilaksanakan ptraktek menulis hikayat. Ketiga, Publikasi. Kegiatan abdimas ini akan mempublikasikan berupa buku hikayat yang ada. Pengabdian kepada masyarakat ini dilaksanakan kepada Ikatan Mahasiswa Kabupaten Tangerang (IMKT) di Kabupaten Tangerang. Beberapa tujuan yang mendasar yang diharapkan oleh tim abdimas terhadap pelaksanaan kegiatan ini. Pertama, menjelaskan bahwa kekayaan kabupaten Tangerang dapat dilihat dari cerita rakyat yang dimiliki.Kedua, mendiskusikan secara bersama mengenai cerita dari orang tua terdahulu, beberapa cerita yang ada dikumpulkan dan didiskusikan secara bersama.Ketiga, menulis salah satu cerita tersebut menjadi sebuah hikayat bergambar, cerita ini disepakati secara bersama-sama yaitu hikayat Nyimas.Kata Kunci: Pelatihan, Menulis Hikayat, Kearifan Lokal Abstract. Indonesia’s wealth is not only in the form natural resource. Indonesia has a wealth of literary work, namely ancient manuscripts. The method of implementation is carried out by various methods as follows. First, the lecture and question and answer method. The Abdimas activity, a Saga writing practice will be carried out. Third, second, practice. In this Abdimas activity, a Hikayat writing pactice will be carried out. Third, publication. This community service activity will publish existing Hikayat books. This community service is carried out to the Tangerang Regency Student Association (IMKT) ini Tangerang Regency. Some of the basic goals expected by the Community teams for the implementation of this a activity. First, it explains that the wealth of Tangerang district can be seen from the folklore it has. Second, discussing stories from previous parents together, some of the stories are collected and discusses together. Third, writing one of these stories into a pictorial hikayat, this story is mutually agree, namely the hikayat Nyimas.Keywords: Training, Story Writing, Local Wisdom
Coordinated Optimal Placement of Energy Storage System and Capacitor Bank Considering Optimal Energy Storage Scheduling for Distribution System Using Mixed-Integer Particle Swarm Optimization

This paper proposes a mixed-integer particle swarm optimization (MIPSO) for coordinated optimal placement of energy storage system (ESS) and capacitor bank (CB). In the propose method, optimal ESS scheduling (OESSS) is solved by particle swarm optimization (PSO), as a subproblem, the optimal coordinated placement (COP) for ESS and CB, simultaneously. The distribution system annual loss minimization (DSALM) is used as the objectives of COP problem. The proposed method was tested with the IEEE 33-bus radial distribution test system. The results demonstrated that the proposed method is successful and robust in minimizing system losses, which loss saving of 35.12% when compare to the based case, which is the best solution among other existing methods.
Improved SMOTE and Optimized Siamese Neural Networks for Class Imbalanced Heterogeneous Cross Project Defect Prediction

: Heterogeneous Cross-Project Defect Prediction (HCPDP) predicts the defects by incorporating the project datasets with different distribution and metrics. However, the class imbalance problem and shallow feature learning of classifiers reduce the overall performance. This paper presents an HCPDP using Improved Synthetic Minority Oversampling Technique (ISMOTE) and hybrid deep learning classifier of Golden Eagle Optimized Siamese Neural Networks (GEO-SNN). Initially, Peters filter removes irrelevant instances in source-target dataset based on heterogeneous metrics. ISMOTE, enhanced through fuzzy mutual information based KNN attribute weights assignment, is applied solve the class imbalance problem. Features are scaled using Z-Score standardization and metric matching by Spearman's Rank Correlation (SRC). Finally, GEO-SNN, developed by SNN parameter optimization using GEO, performs deep semantic feature learning for defect prediction. Experiments using benchmark datasets showed that the proposed ISMOTE and GEO-SNN based HCPDP technique has provided 99% defect prediction accuracy and reduced processing time by 20%than the state-of-the-art methods.
A Fault Location Method Based on Natural Frequencies and Empirical Mode Decomposition for Mixed Overhead-cable Lines

A fault location method based on natural frequencies can work accurately without identifying the wave front.The key factor of this approach is to correctly extract principal component of the natural frequencies of fault travelling wave.Considering the discontinuous wave impedance,the natural frequencies spectra of mixed overhead-cable lines may be aliased and cause difficulties in identifying and extracting the principal component of natural frequencies.In order to handle this more effectively,a fault location method based on natural frequencies and empirical mode decomposition(EMD)for mixed overhead-cable lines is proposed.By using EMD,a signal can be decomposed to obtain the required part of the travelling wave before extracting the principal component of natural frequencies spectra of travelling wave.The natural frequencies spectra can be analysed subsequently to extract the principal component of natural frequencies spectra of the faulty travelling wave to finally obtain the fault location(s). Simulation on a 110 kV mixed overhead-cable line system shows that the proposed method can satisfactorily solve the spectra aliasing problem in fault location studies for mixed overhead-cable lines in frequencies domain.It has potential values for practical usage.
The effect of ductility on the seismic collapse risk of residential steel moment-resisting frames at Alborz and Zagros Seismic zones, Iran

ABSTRACT In this paper, seismic hazard curves are selected for four different sites at Alborz and Zagros seismic zones with underlying conditions of soft and hard soil. Three residential steel moment-resisting frames (SMRFs) with 3, 5, and 7 stories are designed and modeled for each site to perform cloud analysis, implementing more than 300 far-field as-recorded ground motions. Using the results of regression-based cloud analysis, robust fragility curves are developed for each SMRF, and by combining the fragility curve with the hazard curve, the annual rate of collapse (i.e., collapse risk) is calculated for each SMRF. Furthermore, the important levels of peak ground acceleration (PGA) that greatly contribute to the collapse risk of each SMRF are obtained by exploiting the collapse deaggregation curves, which are composed up of fragility curves, derivative of hazard curves, and the annual rates of collapse. It was found that a strong correlation exists between ductility and the collapse risk of SMRFs at both seismic zones and underlying soil conditions, where increasing the ductility of SMRFs results in a decreased risk of collapse. It was also found that the important levels of the PGA contributing to the collapse risk of the SMRFs are inclined towards greater values as the ductility increases. The 5-storey SMRFs exhibited the least ductility and the highest collapse risk at both seismic zones and soil conditions, while the 3-storey SMRFs were the most ductile ones with the least risk of collapse.
Optimization of Human Resource Performance Management System Based on Improved R-Means Clustering Algorithm

With the rapid development of network technology and database technology, computers have been able to store large-scale and massive data. On the other hand, traditional data analysis and processing tools such as management information system can only process these data on the surface, but the deeper data analysis ability is not satisfactory. The contradiction between data supply ability and data analysis ability is becoming more and more prominent, so there is an urgent need for an automation technology that can deeply process data. Data mining technology came into being. Cluster analysis, as an important topic in data mining, is a data mining method that divides data into natural groups and gives the description of the characteristics of each group. It is a basic method of data mining and knowledge discovery. Cluster analysis is a data mining technology for unsupervised classification of data without prior knowledge and guidance. Through the appropriate use of advanced algorithms, it can explore the hidden valuable information, improve the quality of data analysis and interpretation, and provide a scientific judgment basis for the reprocessing or understanding of data by other data analysis and sorting tools. First, this paper briefly introduces the principle, development, and methods of cluster analysis and expounds the application of cluster analysis. Then it expounds the principle of R-means clustering algorithm, analyzes the advantages and disadvantages of basic R-means clustering algorithm, and expounds several existing improvement methods. An improved R-means clustering algorithm and a clustering analysis model based on R-means clustering algorithm are proposed, and the corresponding algorithm flow and implementation are given.
Cochrane Effective Practice and Organisation of Care (EPOC) Qualitative Evidence Syntheses, Differences From Reviews of Intervention Effectiveness and Implications for Guidance

Systematic reviews of qualitative research (‘qualitative evidence syntheses’) are increasingly popular and represent a potentially important source of information about people’s views, needs and experiences. Since 2013, Cochrane has published qualitative evidence syntheses, and the Cochrane Effective Practice and Organisation of Care group has been involved in the majority of these reviews. But more guidance is needed on how to prepare these reviews in an environment that is more familiar with reviews of quantitative research. In this paper, we describe and reflect on how Cochrane qualitative evidence syntheses differ from reviews of intervention effectiveness and how these differences have influenced the guidance developed by the EPOC group. In particular, we discuss how it has been important to display to end users, firstly, that qualitative evidence syntheses are carried out with rigour and transparency, and secondly, that these quality standards need to reflect qualitative research traditions. We also discuss lessons that reviews of effectiveness might learn from qualitative research.