ALLaM: Large Language Models for Arabic and English

We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models

A novel solution for finding postpartum haemorrhage using fuzzy neural techniques

Postpartum haemorrhage (PPH) is the loss of blood above 500 ml during vaginal or caesarean deliveries. It is difficult to find a PPH in an earlier stage, so pregnant women are exposed to excess blood loss that makes them suffer and die. Antenatal practices help in identifying risk factors, and modern technology is used to overcome the risk. Still, the morbidity rate and the mortality arise due to the unpredicted and unexpected cause. PPH is still a significant cause of maternal morbidity and mortality worldwide. The novelty of this research work is to alert medical practitioner about excessive bleeding of pregnant women during childbirth. We are proposing an automation system using wearable devices to prevent pregnant women from the PPH. These devices measure parameters like temperature, pulse rate, blood pressure, and sweat rate of pregnant women. Fuzzy neural technique-based rules are used for each parameter to predict the risk in developing PPH and to evaluate the performance of proposed system for reducing mortality and morbidity rates. Our findings of experiment are carried on metrics of HPPH (high-level postpartum haemorrhage), NPPH (normal-level postpartum haemorrhage), and MPPH (medium-level postpartum haemorrhage) for 15 patients. Fuzzy output value of 1 indicates patient state with NPPH, 0 indicates patient state with HPPH, and values in between 0 and 1 indicate MPPH. Based on the sensitivity of the predicted values, medical attention is taken from doctors or nurses in nearby locations using Internet of Things infrastructure

Pilot Evaluation of a Novel, Low-Cost, Simulation Model for Training and Assessment of Laparoscopic Intracorporeal Continuous Suturing

Background: Laparoscopic intracorporeal continuous suturing is being employed in a growing number of minimally invasive procedures. However, there is a lack of adequate bench models for gaining proficiency in this complex task. The purpose of this study was to assess a novel simulation model for running suture. Methods: Participants were grouped as novice (LSN) or expert (LSE) at laparoscopic suturing based on prior experience and training level. A novel low-cost bench model was developed to simulate laparoscopic intracorporeal continuous closure of a defect. The primary outcome measured was time taken to complete the task. Videos were scored by independent raters for Global Operative Assessment of Laparoscopic Skills (GOALS). Results: Sixteen subjects (7 LSE and 9 LSN) participated in this study. LSE completed the task significantly faster than LSN (430 ± 107 vs 637 ± 164 seconds, P ≤ .05). LSN scored higher on accuracy penalties than LSE (Median 30 vs 0, P ≤ .05). Mean GOALS score was significantly different between the 2 groups (LSE 20.64 ± 2.64 vs LSN 14.28 ± 1.94, P &lt; .001) with good inter-rater reliability (ICC ≥ .823). An aggregate score using the formula: Performance Score = 1200-time(sec)-(accuracy penalties x 10) was significantly different between groups with a mean score of 741 ± 141 for LSE vs 285 ± 167 for LSN ( P &lt; .001). Conclusion A novel bench model for laparoscopic continuous suturing was able to significantly discriminate between laparoscopic experts and novices. This low-cost model may be useful for both training and assessment of laparoscopic continuous suturing proficiency. </jats:p