Updating Training in the Medical Field

This chapter briefly reviews the literature that explores the training technique of deliberate practice and the related constructs, training outcomes of achievement goal orientation, self-efficacy, perceived instrumentality, and reflective practice. This work explains how educators can use and measure these variables to enhance current training methodologies. As part of creating more effective training, the TARGET model, developed by Ames, will be utilized to discuss potential ways to enhance training outcomes in a post-COVID-19 world. Specifically, suggestions are offered for enhancing online training using deliberate practice combined with the TARGET model within a medical setting where there are limited resources.

Models of injury and practical tips for using imagery in rehabilitation

Athletes’ reactions to injuries are varied and involve not only physical, but also mental responses. The perception of injury and individual diff erences can infl uence the results of rehabilitation. This article presents four models that show these relationships. Special attention is paid to the diffi culties faced by athletes when returning to sp Imagery is a mental training technique recommended in the rehabilitation process. Correct application of this method is thought to be important in recovery. There are several main factors that are considered to aff ect the eff ectiveness of imagery training. Real examples of the use of the technique by injured athletes are useful for understanding what to pay attention to. Our purpose is to show that imagery training can help in an injury situation. ort after injury.

Smart Visualization for Online Aids Image Retrieval

Visual aids can be considered as a motivational tool in enhancing students’ attention and create positive perceptions. The use of new technologies has opened new possibilities to integrate online visual aids in the teaching process, which produce positive learning effects. In this paper, a novel technique employed to retrieve specific images based on the kind of query classification. The semantic dictionary built based on the specific classification correlate with the query intention. Singular Value Decomposition SVD training technique have been used to select the effective key templates in order to link the query with the web annotation directly. The present method can be considered as a strategic tool in the E-learning technique, which can provide variety of clustered images to help the students in creative and critical thinking skills and prevent the indoctrination method in learning the students. The qualitative results achieved high True Positive (TP) retrieved images that respect to the effectiveness of the E-learning task. Also, it provides a good 92% of learning reaction and superior learning behavior level.

New seedless table grape variety ‘Albina’ of the Institute Magarach breeding

Селекционерами Института «Магарач» создан новый бессемянный сорт винограда столового направления использования Альбина. Элитная форма, оформленная как новый сорт винограда, выделена из популяции сеянцев комбинации скрещивания Мускат Джим × Ромулус в 1996 году. В статье представлены основные ампелографические и биолого-хозяйственные параметры, которыми характеризуется новый перспективный сорт: средний срок созревания (25.08), продукционный период - 132 дня. Рекомендуемая форма куста - кордон на среднем штамбе. Нагрузка 6 глазков на рожке (4 рожка). Схема посадки - 3 х 1,5 м. Профилактические обработки против грибных болезней - 3-4 раза за сезон. Содержание в ягодах при технологической зрелости: сахаров - 20,3 г/100см, титруемых кислот - 6,3 г/дм. Урожай рекомендуется использовать для потребления в свежем виде. Дегустационная оценка свежего винограда - 8,47 балла. Selection breeders of the Institute Magarach have created a new seedless table grape variety ‘Albina’. The elite form, registered as a new grape variety, was isolated in 1996 from seedling population of the ‘Muscat Jim × Romulus’ cross combination. The article presents main ampelographic, biological and economic parameters typical for new promising cultivar: mid-ripening date (25.08), production period - 132 days. The recommended bush training technique is a medium trunk cordon. Loading is 6 eyes on a cane (4 canes). Planting scheme is 3 x 1.5 m. Preventive treatment against fungal diseases - 3-4 times per season. The content of sugars in technologically ripe berries is 20.3g/100cm, of titratable acids - 6.3g/dm. The crop yield is recommended for fresh consumption. Tasting evaluation of fresh grapes is 8.47 points.

Effects of Voice Therapy Using Gliding and Humming in Dysphonic Patients With Glottal Gap

Background and Objectives Therapies have been reported to treat the glottal gap previously. However, these voice therapies showed the limits because many techniques focused only on one among breathing, resonance and phonation. In addition patients often have difficulties visiting hospital frequently. ‘Gliding and humming’ is vocal training technique that readjusts total vocal patterns such as breathing, resonance and phonation. This technique can be easily applied during short term sessions. The purpose of this study is to evaluate the efficiency of voice therapy with ‘gliding and humming’ for patients with glottic gap during short-term treatment sessions.Materials and Method Twenty-three patients with glottal gap were selected. Of all patients, 14 patients had sulcus vocalis and 12 patients had muscle tension dysphonia (MTD). Voice therapies were performed 1.9 sessions in average. GRBAS, jitter, shimmer, noise to harmonic ratio, semitone range, closed quotient_vowel and maximum phonation time were compared before and after the therapies. In addition, changes of glottal gap and MTD severity were evaluated.Results Statistically significant improvement was observed. MTD improvement was observed only among the patients with glottal gap improvement. Also sulcus vocalis group showed the statistically significant improvement.Conclusion ‘Gliding and humming’ was effective to the patients with glottic gap and sulcus vocalis. Also, among patients who have both glottic gap and MTD, the data suggests that voice therapy for glottic gap also makes improvement in MTD.

Management of small perforated corneal ulcers: Training model in mammalian eye to enhance surgical skills of residents

Purpose: To demonstrate a training technique on the mammalian eye for optimum Cyanoacrylate Tissue adhesive application in cases of perforated corneal ulcers. Methods: A full-thickness defect simulating a perforation was created on the goat’s eye cornea to teach the technique of cyanoacrylate glue application in cases of corneal perforations to novice surgeons. Results: This training model on the mammalian eye was tested by 10 residents at our centre. All the 10 candidates involved in our series were newly joined Cornea fellows with proficient skill in cataract surgeries and minor ophthalmic procedures such as suture removal, chalazion excision, pterygium removal and administration of an intravitreal injection. None of the candidates had prior experience of corneal surgeries. Each resident made an average of 4.4 attempts to seal the corneal defect, obtain a regular corneal surface and form the anterior chamber. Conclusion: This training model helps in mastering one of the skills of corneal surgeries.

Assessment of Gradient Descent Trained Rule-Fact Network Expert System Multi-Path Training Technique Performance

The use of gradient descent training to optimize the performance of a rule-fact network expert system via updating the network’s rule weightings was previously demonstrated. Along with this, four training techniques were proposed: two used a single path for optimization and two use multiple paths. The performance of the single path techniques was previously evaluated under a variety of experimental conditions. The multiple path techniques, when compared, outperformed the single path ones; however, these techniques were not evaluated with different network types, training velocities or training levels. This paper considers the multi-path techniques under a similar variety of experimental conditions to the prior assessment of the single-path techniques and demonstrates their effectiveness under multiple operating conditions.

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.

UNBERT: User-News Matching BERT for News Recommendation

Nowadays, news recommendation has become a popular channel for users to access news of their interests. How to represent rich textual contents of news and precisely match users' interests and candidate news lies in the core of news recommendation. However, existing recommendation methods merely learn textual representations from in-domain news data, which limits their generalization ability to new news that are common in cold-start scenarios. Meanwhile, many of these methods represent each user by aggregating the historically browsed news into a single vector and then compute the matching score with the candidate news vector, which may lose the low-level matching signals. In this paper, we explore the use of the successful BERT pre-training technique in NLP for news recommendation and propose a BERT-based user-news matching model, called UNBERT. In contrast to existing research, our UNBERT model not only leverages the pre-trained model with rich language knowledge to enhance textual representation, but also captures multi-grained user-news matching signals at both word-level and news-level. Extensive experiments on the Microsoft News Dataset (MIND) demonstrate that our approach constantly outperforms the state-of-the-art methods.