The study aimed to provide the procedural proposals of developing economic awareness at students of technical secondary schools in Egypt in the light of some contemporary international trends .The study used a descriptive approach through which it is
possible to describe and analyze modern the international trends .The study concluded a group of procedural proposals represented in : Involving the students in managing some of the school financial affairs (such as ,managing the school cafeteria ) .Forming student social contact groups through social networks to discuss the current economic matters ,providing the school with books and references and scientific resources concerned about developing the economic awareness at students of technical education , provision of the financial corporeal support required for teachers, increasing the number of teachers, , paying attention to train the teachers and raising their efficiency, skills through holding training courses in the field of financial and economic awareness, designing school curricula in a manner coping with the current economic situation and reinforcing the economic skills of the students', including the subject of the economic education so that it can be binding to the students of technical secondary schools throughout the three –year system or the five-year system , Including the financial culture subject in all various subjects of the Egyptian Curricula, allocating one day in the school year to be the Saving Day, making plays for some wrong economic behaviors and habits spread in the Egyptian community, Organizing trips and field visits to factories and economic institutions.
Diabetes is one of the most important health challenges in the world in the twenty-first century, and a healthy diet and changing bad eating habits can help prevent and treat this disease. The information of the nursing staff about the diet for type
II diabetics is important to the treatment plan, especially in patients who suffer from advanced complications. To assessment this information, the current descriptive study conducted on an convenient sample of 50 working nurses in Tishreen University Hospital, which showed that (80%) of the nurses have a good level of information related to the diet of type II diabetics patients, (14%) of them have a moderate level of information, and (6%) of them have a poor level of information, and the researcher recommended working on regular and continuous updating of the evidence-based guidelines for the diabetes diet to help the health care providers concerned with providing health services to patients with diabetes.
Objective
This research aimed to describe several areas in which AI could play a role in the development of Personalized Medicine and Drug Screening, and the transformations it has created in the field of biology and therapy. It also addressed the l
imitations faced by the application of artificial intelligence techniques and make suggestions for further research.
Methods
We have conducted a comprehensive review of research and papers related to the role of AI in personalized medicine and drug screening, and filtered the list of works for those relevant to this review.
Results
Artificial Intelligence can play an important role in the development of personalized medicines and drug screening at all clinical phases related to development and implementation of new customized health products, starting with finding the appropriate medicines to testing their usefulness. In addition, expertise in the use of artificial intelligence techniques can play a special role in this regard.
Discussion
The capacity of AI to enhance decision-making in personalized medicine and drug screening will largely depend on the accuracy of the relevant tests and the ways in which the data produced is stored, aggregated, accessed, and ultimately integrated.
Conclusion
The review of the relevant literature has revealed that AI techniques can enhance the decision-making process in the field of personalized medicine and drug screening by improving the ways in which produced data is aggregated, accessed, and ultimately integrated. One of the major obstacles in this field is that most hospitals and healthcare centers do not employ AI solutions, due to healthcare professionals lacking the expertise to build successful models using AI techniques and integrating them with clinical workflows.
The installation and care of urinary catheters is one of the tasks entrusted to nursing personnel, as the urinary tract is a passageway for the excretion of many hazardous wastes that are excreted with urine. Unfortunately, in some cases, the surviva
l of this duct may require the installation of a urinary catheter. This procedure usually weakens the defenses of the natural urethra, and in some cases this may cause dangerous infections that may reach the kidneys; Accordingly, the nursing staff must work to relieve the discomfort associated with the urinary catheter; By performing this procedure according to the approved steps and protocols, this study aimed to identify the level of knowledge and performance of the nursing staff about the nursing care of patients with urinary catheterization among 46 nurses, who were selected by the convenient sample method. The data were collected using a questionnaire developed by the researcher himself. Where the study showed that 93.5% of the participants had a good level of correct information, and that 6.5% of them had a moderate level of correct information. The performance of 95.7% of them was good before starting the catheter installation, and during the installation the performance of 82.6% of them was good, while after the completion of the installation 89.1% of them performed well. The study recommended the development of a written protocol related to urinary catheter insertion and its indications to be implemented and patient education about it, and regular and continuous updating of evidence-based guidelines for nursing procedures.
Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word
orders significantly differ. This is unlike human simultaneous interpreters who produce largely monotonic translations at the expense of the grammaticality of a sentence being translated. In this paper, we thus propose an algorithm to reorder and refine the target side of a full sentence translation corpus, so that the words/phrases between the source and target sentences are aligned largely monotonically, using word alignment and non-autoregressive neural machine translation. We then train a widely used wait-k simultaneous translation model on this reordered-and-refined corpus. The proposed approach improves BLEU scores and resulting translations exhibit enhanced monotonicity with source sentences.
This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation. The framework aims to jointly optimize the policy and translation models. To effectively consider all possible READ-WRITE simultaneou
s translation action paths, we adapt the online automatic speech recognition (ASR) model, RNN-T, but remove the strong monotonic constraint, which is critical for the translation task to consider reordering. To make CAAT work, we introduce a novel latency loss whose expectation can be optimized by a forward-backward algorithm. We implement CAAT with Transformer while the general CAAT architecture can also be implemented with other attention-based encoder-decoder frameworks. Experiments on both speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks show that CAAT achieves significantly better latency-quality trade-offs compared to the state-of-the-art simultaneous translation approaches.
In this paper, we study the possibility of unsupervised Multiple Choices Question Answering (MCQA). From very basic knowledge, the MCQA model knows that some choices have higher probabilities of being correct than others. The information, though very
noisy, guides the training of an MCQA model. The proposed method is shown to outperform the baseline approaches on RACE and is even comparable with some supervised learning approaches on MC500.
Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology. To remedy for this, a wide
range of debiasing techniques have recently been introduced to remove such stereotypical biases from PLMs. Existing debiasing methods, however, directly modify all of the PLMs parameters, which -- besides being computationally expensive -- comes with the inherent risk of (catastrophic) forgetting of useful language knowledge acquired in pretraining. In this work, we propose a more sustainable modular debiasing approach based on dedicated debiasing adapters, dubbed ADELE. Concretely, we (1) inject adapter modules into the original PLM layers and (2) update only the adapters (i.e., we keep the original PLM parameters frozen) via language modeling training on a counterfactually augmented corpus. We showcase ADELE, in gender debiasing of BERT: our extensive evaluation, encompassing three intrinsic and two extrinsic bias measures, renders ADELE, very effective in bias mitigation. We further show that -- due to its modular nature -- ADELE, coupled with task adapters, retains fairness even after large-scale downstream training. Finally, by means of multilingual BERT, we successfully transfer ADELE, to six target languages.
Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine's reasoning process. We propose Relation Extractor-Reader and Comparator (RERC), a
three-stage framework based on complex question decomposition. The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset, our RERC model has achieved the state-of-the-art performance, with a winning joint F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact. At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.
Style is an integral part of natural language. However, evaluation methods for style measures are rare, often task-specific and usually do not control for content. We propose the modular, fine-grained and content-controlled similarity-based STyle Eva
Luation framework (STEL) to test the performance of any model that can compare two sentences on style. We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac'tion and numb3r substitution). We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches. We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.
