Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective online large-group teaching in health professions education

Online large-group teaching (OLGT), employed to reach a large group of learners in separate physical locations, allows asynchronous learning and facilitates social distancing. While online large-groups can be a powerful and resource-lean means of health professions education, it has challenges and potential pitfalls that may affect the learning process and outcomes. Through a sociomateriality framework, this article describes strategies for effective online large-group teaching in health professions education in three key strands. Firstly, to optimize learning, OLGT sessions should match learning needs with appropriate OLGT platforms, incorporate strategies to sustain learner attention, and accommodate learners of different abilities. Secondly, to develop a learning culture, OLGT must not only focus on cognitive aspects of learning but also build a community of practice, nurture digital professionalism and professional identity. Thirdly, we discuss the avoidance of pitfalls such as cognitive overload of both tutors and learners, technical issues and security risks, mitigating inequities in access to online learning, and the use of program evaluation to plan for sustained improvements. We conclude with a case vignette that discusses the challenges of OLGT and the application of the above strategies in a teaching scenario.
Realtime visual-inertial SLAM integrated with radar data to triangulate targets

This paper presents research concerning the use of visual-inertial Simultaneous Localization And Mapping (SLAM) algorithms to aid in Continuous Wave (CW) radar target mapping. SLAM is an established field in which radar has been used to internally contribute to the localization algorithms. Instead, the application in this case is to use SLAM outputs to localize radar data and construct three-dimensional target maps which can be viewed live in augmented reality. These methods are transferable to other types of radar units and sensors, but this paper presents the research showing how the methods can be applied to calculate depth efficiently with CW radar through triangulation using a Boolean intersection algorithm. Localization of the radar target is achieved through quaternion algebra. Due to the compact nature of the SLAM and CW devices, the radar unit can be operated entirely handheld. Targets are scanned in a free-form manner where there is no need to have a gridded scanning layout. The main advantage to this method is eliminating many hours of usage training and expertise, thereby eliminating ambiguity in the location, size and depth of buried or hidden targets. Additionally, this method grants the user the additional power, penetration and sensitivity of CW radar without the lack of range finding. Applications include pipe and buried structure location, avalanche rescue, structural health monitoring and historical site research.
Understanding the foremost challenges in the transition to online teaching and learning during COVID’19 Pandemic: A systematic literature review

The study intends to understand the foremost challenges in the transition to online teaching and learning during the COVID'19 pandemic. The PRISMA approach reduces the selection of journal articles and review papers according to the research aims and the inclusion criteria. The journal articles and review papers were extracted and stored in M.S. Excel and Google Scholar, Academic. Microsoft, Semantic Scholar, Elsevier, and Emerald Insight databases searched relevant documents using formulated keywords. A statistical technique was applied using M.S. Excel to analyze data, including an independent t-Test to determine the differences among teachers and students. The review revealed the evidence that the majority of the studies were primarily focused on the individual developing countries and result from other developing countries not considered. In addition, the foremost challenges in the transition to online teaching and learning during the covid-19 pandemic were inadequate skills and training, inadequate Internet/Infrastructure, lack of supporting resources and lack of online student engagement and feedback. Finally, the independent t-test reveals no statistically significant difference in the transition to online teaching and learning challenges during the covid-19 pandemic. Both teachers and students encounter similar challenges. The systematic review raised concerns that higher learning needs to effectively implement strategies and support teachers and students in getting into online teaching and learning.
Pollen and trichome morphology of tribe Stachydeae (Lamiaceae) and itsphylogenetic significance

: Tribe Stachydeae with 12 genera were divided into two major clades: Melitis and Eurystachys clades consisting of all genera attributed to Stachydeae except Melittis . Recently, a broadly defined Eurystachys clade falling into 12 well-supported clades have been formally named in a phylogenetic nomenclature. In the present study, trichomes of 70 spp. from nine genera and pollens of 53 spp. from six genera were investigated using scanning electron microscopy (SEM) in order to draw the taxonomic conclusion of tribe Stachydeae. Trichome characters that can provide adequate variation in delimitation of taxa were: presence or absence of glandular or nonglandular trichomes, number of cells (uni-/bi-/multicellular), presence of extremely long trichomes (i.e. more than 1000 μm), presence of branched trichomes as well as type of glandular trichomes (peltate or capitate with three subtypes: sessile to subsessile, short-stalked and long-stalked trichomes). Among the investigated characters, extremely long simple trichomes and branched trichomes were apomorphic and were found in Eriostomum and Marrubiastrum clades, respectively. In addition, number of colpi, type of sculpturing as well as shape of lumina as the main features of the investigated pollen grains afforded valuable characters especially in delimitation of several natural groups, i.e. Eriostomum clade, Empedoclea clade, Sideritis clade, Marrubiastrum clade, and Swainsoniana clade. Moreover, a close relationship among two annual clades of Sideritis each of represents a section, i.e. Hesiodia and Burgsdorfia , is suggested by our results. However, in both Stachys and Olisia clades which are morphologically heterogeneous, type of sculpturing shows variation and mostly useful in species discrimination. Overall, trichome and pollen characters can be useful in describing the natural groups within tribe Stachydeae along with other reliable morphological characters.
Internet without wires

African communities without access to the internet are being trained to build their own wireless networks, thanks to a project launched by the South-Africa-based NGO, the Association for Progressive Communications (APC). To date, four regional African workshops run by the The APC s Community Wireless Connectivity project have trained about 100 people and produced training materials in English and French. In most cases, internet access relies on the availability of reliable, fixed telephone lines, which can be hard to find in many parts of rural Africa. Wireless technology, which is based on using radio waves to carry data, can by-pass the fixed-line problem. It is hoped that wireless networking can provide some of the benefits to internet access that mobile phone technology gave to telephone access in Africa. Participants in the workshops learned how to configure access points, climb towers safely, calculate radio links, survey their sites, source appropriate equipment, budget for projects and secure their networks. At the first workshop, held in Mtoni, Zanzibar, trainees were able to build antennas out of recycled tin cans, and later use them to make a wireless connection to Grave island an atoll 2 km across the sea from the workshop venue. Ann Tothill PO Box 29755 Melville 2109 South Africa Fax: +27 (0)11 726 16 92 Email: aatothill@apc.org Website: www.apc.org/wireless
Isotopic Characterization of Italian Industrial Hemp (Cannabis sativa L.) Intended for Food Use: A First Exploratory Study

In this study, Italian industrial hemp (Cannabis sativa L.) intended for food use was isotopically characterized for the first time. The stable isotope ratios of five bioelements were analyzed in different parts of the plant (i.e., roots, stems, inflorescences, and seeds) sampled in eight different regions of Italy, and in five hemp seed oils. The values of δ2H, δ13C, δ18O, and δ34S differed according to the latitude and, therefore, to the geographical origin of the samples and the climate conditions of plant growth, while the δ15N values allowed us to distinguish between crops grown under conventional and organic fertilization. The findings from this preliminary study corroborate the reliability of using light stable isotope ratios to characterize hemp and its derived food products and contribute to the creation of a first isotopic database for this plant, paving the way for future studies on authentication, traceability, and verification of organic labeling.
DEEP LEARNING TRAINING WITH UNBALANCE SAMPLE DISTRIBUTION FOR REMOTE SENSING IMAGE SEGMENTATION

Abstract. The intelligent interpretation of remote sensing images based on deep learning has become a hot spot with the increasing satellite images acquired due to the rapid development of aerospace technology. Sufficient and reasonable distributed samples are essential for the accuracy of deep learning. The spatial distribution of natural features is inhomogeneous in the real world. When people create sample dataset, they often collect within a certain local range, which may bring problems of unbalanced distribution of samples, including the unbalance between training dataset and validation dataset, and the unbalance among different sample categories. This long-tail distribution of samples (i.e., a few classes account for most of the data, while most classes are under-represented) can lead to bias in the training model and make it difficult to ensure accuracy.In this paper we tried to solved the above-mentioned problem in landcover classification with high spatial and spectral resolution (HSSR) remote sensing images. We first adopted an iterative stratification method for multi-label data classification to ensure that both training dataset and validation dataset contain reasonable proportion of landcover classes. Then we proposed a weighted loss algorithm to further strengthen the learning ability of the model for rare categories. Experiments on a large volume HSSR dataset shows that with our methods the accuracy of landcover classification increased by 2%.
The Effect of Nutritional Support Based on the Dietary Anti-Inflammatory Index on Cancer-Related Fatigue in Lung Cancer Patients Undergoing Chemotherapy

Background Cancer-related fatigue is one of the most common symptoms reported by cancer patients and is considered to be related to inflammation. Objective This study aimed to explore the effects of nutritional support based on the dietary anti-inflammatory index on cancer-related fatigue in lung cancer patients undergoing chemotherapy. Methods This was a randomized controlled trial with 106 lung cancer patients who were divided into either the anti-inflammatory diet group (n = 53) or the usual diet group (n = 53) for 3 months. The primary outcome was cancer-related fatigue. Secondary outcomes included high sensitivity C-reactive protein (hs-CRP) concentrations, nutritional status, and quality of life. Repeated-measures analysis of variance was used to examine the effectiveness of this intervention. Results The anti-inflammatory diet improved fatigue (−1.99 ± 1.78, P < .001), hs-CRP levels (−4.15 [−11.87, −0.58], P < .001), Patient-Generated Subjective Global Assessment (−2.53 ± 3.11, P = .030), and albumin concentrations (2.83 ± 0.59, P < .001) compared with the usual diet after 3 months. Simultaneously, in the repeated-measures analysis of variance, the differences in fatigue (F = 5.536, P < .001), hs-CRP levels (F = 6.918, P < .001), and albumin concentrations (F = 2.727, P = .048) were statistically significant for the group-by-time interaction. Conclusion The study provided evidence for the positive effect of nutritional support based on the dietary anti-inflammatory index on cancer-related fatigue, hs-CRP levels, nutritional status, and quality of life in lung cancer patients undergoing chemotherapy. Implication for Practice With an anti-inflammatory diet, nurses can help these patients improve their overall quality of life.
Transition metal dichalcogenide heterojunction PN diode toward ultimate photovoltaic benefits

Recently, two-dimensional (2D) transition metal dichalcogenide (TMDC) semiconductors as van der Waals (vdW) materials have attracted much attention from researchers. Among many 2D TMDC materials, a few layer-thin molybdenum disulfide (MoS2) and tungsten diselenide (WSe2) have been most intensively studied respectively as 2D n- and p-type semiconductors. Here, we have fabricated vertical vdW heterojunction n-MoS2/p-WSe2 diode with a few tens nm-thick layers by using vertically-sandwiched ohmic terminals, so that no quasi neutral region may exist between two terminals. As a result, we obtained high photo responsivity at zero volt without any electric power, and it appears comparable to those of commercially-optimized Si PN diode. Photo-voltage output of 0.3 V was easily obtained from our vdW PN diode as open circuit voltage, and can be doubled up to 0.6 V by using two PN diodes. These beneficial photovoltaic results from vdW PN diode were directly applied to PV switching dynamics and transistor photo gating, for the first time. We regard that our vdW n-MoS2/p-WSe2 heterojunction diode could maximize its photovoltaic energy benefits with optimized TMDC thicknesses.
Využití fuzzy logiky pro studii bezpečnostních hrozeb a rizik fyzické ochrany

Hadacek L. , Využiti fuzzy logiky pro studii bezpecnostnich hrozeb a rizik fyzicke ochrany, Disertacni prace, Ostrava: VSB-Technicka univerzita, 2015, 95 s. V disertacni praci se zabývam odhadem bezpecnostniho rizika s využitim pravidel fuzzy logiky. Teorie fuzzy logiky nabizi možnost matematicky modelovat význam podminěných vagnich výroků. Přibližnou logickou dedukci je možne vyhodnotit hodnotu rizika. Pravidla typu IF-THEN jsou svoji strukturou blizka vyjadřovani clověka. Hodnoty rizika zjistěne přibližnou logickou dedukci umožni optimalizovat opatřeni fyzicke ochrany objektu. Bezpecnostni charakteristiky aktiva jsou urcujici pro stanoveni bezpecnostniho rizika. Stanoveni bezpecnostniho rizika je provedeno na třech vzajemně provazaných urovnich. V nejnižsi urovni vstupuji do logicke struktury rizikový profil, zranitelnost a hodnota aktiva. Na středni urovni je odhadnuta pravděpodobnost ztraty a velikost nasledku. Výsledne bezpecnostni riziko je odhadnuto ze vztahu mezi pravděpodobnosti ztraty a nasledkem na nejvyssi urovni. Navržena logicka struktura neni uzavřena. Ke stanoveni vstupnich hodnot je možne využit dalsi kvantitativni nebo kvalitativni analyticke metody, jejichž výsledky je možne ohodnotit jazykovými výrazy.
Research on the Power Sharing Control and Stability of VSGs

Aiming at the deficiencies of power sharing control performances when a traditional droop control is adopted for microgrid inverters, this paper proposes a microgrid inverter power sharing control strategy based on a virtual synchronous generator. This control method simulates the electromechanical transient characteristics of a synchronous generator in a power system by an ontology algorithm and the control laws of a synchronous generator by control over the speed governor and excitation regulator. As a result, that the microgrid system is able to effectively retain the stability of the voltage and frequency, and the power sharing precision of the microgrid inverter is improved. Based on an analysis of stability of a microgrid system controlled by a virtual synchronous generator, design thoughts are provided for further improvement of the power sharing precision of inverters. The simulation results shows that when the virtual synchronous generator based control strategy was adopted, the power sharing performances of microgrid inverters are improved more obviously than those using the droop control strategy.
Ultra-Short-Term Dual Antiplatelet Therapy in Treating Unruptured Brain Aneurysm With the Pipeline Flex-Shield Embolization Device

Ruptured cerebral aneurysms can cause significant morbidity and mortality. Endoluminal devices to treat aneurysms such as the Pipeline™ Flex Embolization Device with Shield Technology (PFES) (Medtronic, Dublin, Ireland) integrate phosphorylcholine on the surface of the device in order to reduce platelet adherence that causes periprocedural thromboembolic events and subsequent long-term intrastent stenosis. In addition to the Shield Technology, patients are commonly placed on dual antiplatelet therapy (DAPT) for six months to reduce thromboembolic events and subsequent long-term intrastent stenosis. There is a strong positive correlation between the length of DAPT use and bleeding. Here, we present a case of a 66-year-old female with a right supraclinoid internal carotid artery (ICA) aneurysm treated with a PFES who was placed on dual antiplatelet therapy for the first 31 days postoperative and subsequently maintained on aspirin (ASA) 81 mg monotherapy. At two months, a follow-up diagnostic cerebral angiogram showed complete occlusion of the aneurysm with a patent stent. Our case sets the stage for further research into the optimal length of dual antiplatelet therapy required in PFES to prevent short and long-term thromboembolic events. This report indicates that it may be safe for patients with PFES to intermittently halt the use of DAPT to manage bleeding complications or perform surgery.
Automated Video Classification System Driven by Characteristics of Emotional Human Brainwaves Caused by Audiovisual Stimuli

This article proposes a method of automatically classifying videos into human emotional categories by imitating human neural processes, in which emotion-specific electroencephalography (EEG) characteristics are generated by audiovisual stimulation. In the proposed method, sophisticated emotional features are first extracted from EEG signals that are generated while a subject watches a video, using a sample-attention-based deep neural network encoder. Next, the direct mapping relationship between the extracted emotional EEG features and audiovisual features extracted from the contents of the corresponding videos are learned by a deep belief network. For practical application, the EEG features corresponding to the input video are automatically generated based on the machine’s learned ability without measuring human EEG signals and subsequently applied to a segment-attention-based deep neural network decoder for emotion classification of the video. The experimental results demonstrated that the proposed method significantly outperforms the existing methods with an average accuracy of about 95% for classifying four emotional classes of the video. As for automated emotional video classification, our artificial emotional EEG features-based approach obtains competitive performance, comparable to models that directly measure EEG, and can be generalized to various audiovisual data sets.
Early Successful Eye Movement Desensitization and Reprocessing (EMDR) Therapy for Verbal Memory Impairment in an Adjustment Disorder: A Case Report in a Newly-Diagnosed Multiple Sclerosis Patient

Multiple sclerosis (MS) is a chronic inflammatory disease of the immune system affecting the central nervous system. Several phenotypes are possible, and cases usually present with a relapsing-remitting (RR) course with disease onset at a young age. MS diagnosis can represent a traumatic event for the patient, possibly evolving into adjustment disorder (AD). AD is defined by the presence of emotional or behavioral symptoms in response to identifiable stress occurring within the prior three months and similarly to post-traumatic stress disorder (PTSD) can significantly affect quality of life. Usually, neuropsychological disorders are not associated with AD. Several treatments are available for AD, and among them, eye movement desensitization and reprocessing (EMDR) is one of the most effective in relieving depression and anxiety. However, little is known about AD and PTSD in the MS population and no data are available on the effectiveness of EMDR for cognitive impairment associated with AD. We describe a 25-year-old patient with RR MS developing an AD with a verbal memory deficit after being diagnosed. Both the psychological and cognitive deficits were diagnosed using an extensive neuropsychological battery. Considering the high impact of the verbal memory deficit, on the patient’s quality of life, an EMDR intervention was planned. After a six-month EMDR intervention performed by two trained neuropsychologists, the patient was retested. There was an improvement in verbal memory tests and depression anxiety scales and the Dissociative Experiences Scale. It is recognized that emotional changes and psychiatric disorders, frequently affect MS patients at diagnosis. It is imperative to recognize this and promptly set a neuropsychological treatment. Moreover, we suggest checking cognition along with depression and anxiety. Finally, to our knowledge, this is the first report of AD with an isolated neuropsychological deficit (verbal memory) developed after the MS diagnosis and treated beneficially with e EMDR. More studies are needed to confirm the efficacy of EMDR in treating cognitive impairment associated with AD in MS patients.
X-Ray Quiz: A 2-Month-Old Baby with a Painful Ecchymotic upper Extremity

This 2-month-old female baby without previous systemic disease was brought to the emergency department (ED) because she was found to have a painful, swelling and ecchymotic right upper limb for 1 day. According to her mother's statement, she didn't have a trauma history recently. Orthopaedics was consulted and close reduction with Velpeau sling immobilisation was performed at the ED.
Obstetric outcome in patients with rheumatic heart disease: experience in a tertiary hospital

Aims: To determine the clinical outcome of rheumatic heart disease in pregnancy. Methods: Retrospective cross-sectional descriptive study from April 2019 to April 2021 in Nobel Medical College, Biratnagar, Nepal. Feto-maternal variables were taken for their health status. Data presented in table with frequency. Results: Out of 13013 deliveries in a year, 49 had cardiac disease (0.37%) and 38 had rheumatic heart disease (0.29%) over 28 weeks of gestation; 95% (n=36) had mitral valve involvement; 12 were primigravida and 7 preterm at the time of delivery. Half of them underwent caesarean section for various indications. Most common maternal complications were cardiac failure, cardiac arrythmia, admission to ICU, obstetric complications, including maternal mortality in 5.2% (n=2) cases. Low birth was in 29% (n=11) of cases, and 34% (n=13) of them needed NICU care at the time of delivery. There was history of rheumatic fever in 9 cases (24%). Conclusions: Rheumatic heart disease is the commonest diagnosis among heart disease in pregnancy and adverse event can be minimized by multidisciplinary intervention