Arabic dialect identification in the context of bivalency and code-switching

In this paper we use a novel approach towards Arabic dialect identification using language bivalency and written code-switching. Bivalency between languages or dialects is where a word or element is treated by language users as having a fundamentally similar semantic content in more than one language or dialect. Arabic dialect identification in writing is a difficult task even for humans due to the fact that words are used interchangeably between dialects. The task of automatically identifying dialect is harder and classifiers trained using only n-grams will perform poorly when tested on unseen data. Such approaches require significant amounts of annotated training data which is costly and time consuming to produce. Currently available Arabic dialect datasets do not exceed a few hundred thousand sentences, thus we need to extract features other than word and character n-grams. In our work we present experimental results from automatically identifying dialects from the four main Arabic dialect regions (Egypt, North Africa, Gulf and Levant) in addition to Standard Arabic. We extend previous work by incorporating additional grammatical and stylistic features and define a subtractive bivalency profiling approach to address issues of bivalent words across the examined Arabic dialects. The results show that our new methods classification accuracy can reach more than 76% and score well (66%) when tested on completely unseen data

Arabic dialect identification in the context of bivalency and code-switching

In this paper we use a novel approach towards Arabic dialect identification using language bivalency and written code-switching. Bivalency between languages or dialects is where a word or element is treated by language users as having a fundamentally similar semantic content in more than one language or dialect. Arabic dialect identification in writing is a difficult task even for humans due to the fact that words are used interchangeably between dialects. The task of automatically identifying dialect is harder and classifiers trained using only n-grams will perform poorly when tested on unseen data. Such approaches require significant amounts of annotated training data which is costly and time consuming to produce. Currently available Arabic dialect datasets do not exceed a few hundred thousand sentences, thus we need to extract features other than word and character n-grams. In our work we present experimental results from automatically identifying dialects from the four main Arabic dialect regions (Egypt, North Africa, Gulf and Levant) in addition to Standard Arabic. We extend previous work by incorporating additional grammatical and stylistic features and define a subtractive bivalency profiling approach to address issues of bivalent words across the examined Arabic dialects. The results show that our new methods classification accuracy can reach more than 76% and score well (66%) when tested on completely unseen data

Translating Rare Dementias. Making a Difference through Simulated Agency Experience.

In the post-pandemic era, we are even more aware of the importance of preparing our students for translation in health contexts, not just to enhance their learning experience but also to help them realise their work’s value to translation users in real life. Funded by external and internal grants, in the 21-22 academic year we ran a medical translation project on dementia for our MA/MSc translation students via a simulated agency. Dementia affects millions of people worldwide and 5-15% live with a rare form of dementia. To help people understand these rare dementias, the UCL-led Rare Dementia Support (RDS) service shares research-based, disease-specific information on their website, which is accessed from all over the world. This paper describes how UCL’s Centre for Translation (CenTraS) collaborated with the RDS to open up their website to speakers of other languages by setting up a simulated translation agency for CenTras students. The extra-curricular scheme aimed to give translation students practical, hands-on experience of working within a team to produce high quality translations for an external ‘client’ (RDS). Thirty-three students volunteered to take part, producing translations into French, German, Italian, Russian, Spanish, and Chinese (traditional and simplified). Students acted as translators and/or reviewers under the guidance of four CenTraS staff ‘project managers’. Thanks to grants from The National Brain Appeal and UCL, they were paid for their work in vouchers. Students’ names will also be added to the website to acknowledge their efforts, allowing them to provide evidence of work experience to future employers. In this paper, we share the lessons we learned and challenges we faced. We discuss the feedback we received from students, and we talk about the amendments we have made to the scheme this year

Roundtable: Situated Learning Experiences in Medical Translation The Rare Dementia Project

Translating Rare Dementias A question-and-answer session on CenTraS’ collaboration with UCL’s Rare Dementia Support (RDS) service. The translation collaboration between UCL’s Centre for Translation Studies (CenTraS) and the UCL-led Rare Dementia Support service (www.raredementiasupport.org) was launched in November 2021. Run as a simulated translation agency, the project gives MA and MSc students at CenTraS a unique opportunity to practise and implement the skills they learn in their translation classes in a "real-life" scenario. Their translations will be published on the Rare Dementia Support service website this July and will help disseminate key information on rare dementias worldwide. In this session, we will explain how we initially structured and launched this extracurricular project. We discuss how we have secured funding to pay our students and how we assign translation and quality assurance tasks. We will talk through the issues we have encountered and the impact that the project has had internally and externally. We will also share the lessons we have learned as project managers and hear about student experience from a student translator who prepared Russian translations for the Rare Dementia Support service website. As the project approaches the end of its second year, we discuss the future of such initiatives within CenTraS and their value as mentored professional experience for our students

State-of-the-Art Review on Shipboard Microgrids: Architecture, Control, Management, Protection, and Future Perspectives

Shipboard microgrids (SBMGs) are becoming increasingly popular in the power industry due to their potential for reducing fossil-fuel usage and increasing power production. However, operating SBMGs poses significant challenges due to operational and environmental constraints. To address these challenges, intelligent control, management, and protection strategies are necessary to ensure safe operation under complex and uncertain conditions. This paper provides a comprehensive review of SBMGs, including their classifications, control, management, and protection, as well as the most recent research statistics in these areas. The state-of-the-art SBMG types, propulsion systems, and power system architectures are discussed, along with a comparison of recent research contributions and issues related to control, uncertainties, management, and protection in SBMGs. In addition, a bibliometric analysis is performed to examine recent trends in SBMG research. This paper concludes with a discussion of research gaps and recommendations for further investigation in the field of SBMGs, highlighting the need for more research on the optimization of SBMGs in terms of efficiency, reliability, and cost-effectiveness, as well as the development of advanced control and protection strategies to ensure safe and stable operation

Feasibility of diffusion-weighted magnetic resonance imaging in evaluation of early therapeutic response after CT-guided microwave ablation of inoperable lung neoplasms

Objective!#!To determine the early treatment response after microwave ablation (MWA) of inoperable lung neoplasms using the apparent diffusion coefficient (ADC) value calculated 24 h after the ablation.!##!Materials and methods!#!This retrospective study included 47 patients with 68 lung lesions, who underwent percutaneous MWA from January 2008 to December 2017. Evaluation of the lesions was done using MRI including DWI sequence with ADC value calculation pre-ablation and 24 h post-ablation. DWI-MR was performed with b values (50, 400, 800 mm!##!Results!#!Forty-seven patients (mean age: 63.8 ± 14.2 years, 25 women) with 68 lesions having a mean tumor size of 1.5 ± 0.9 cm (range: 0.7-5 cm) were evaluated. Sixty-one lesions (89.7%) showed a complete treatment response, and the remaining 7 lesions (10.3%) showed a local progression (residual activity). There was a statistically significant difference regarding the ADC value measured 24 h after the ablation between the responding (1.7 ± 0.3 × 10!##!Conclusion!#!ADC value assessment following ablation may allow the early prediction of treatment efficacy after MWA of inoperable lung neoplasms.!##!Key points!#!• ADC value calculated 24 h post-treatment may allow the early prediction of MWA efficacy as a treatment of pulmonary tumors and can be used in the early immediate post-ablation imaging follow-up. • The pre-treatment ADC value of lung neoplasms is not different between the responding and non-responding tumors

Efficient fault detection, localization, and isolation in MT-HVDC systems based on distance protection and LoRaWAN communication

Multiterminal High-Voltage Direct Current (MT-HVDC) systems offer numerous benefits compared to conventional alternating current (AC) power systems, including higher power density and improved efficiency. However, the need for adequate protection schemes for HVDC systems remains a significant obstacle to their widespread adoption. Much attention has been given to developing HVDC protection methods to address this. Moreover, the protection of MT-HVDC systems presents a challenge due to bidirectional power flow, dynamic system characteristics, and fault current characteristics that cannot be addressed using conventional methods. This paper represents a centralized protection unit based on a distance protection scheme that involves a two-stage relay process. The first stage involves fault detection by measuring voltage and current to obtain the system impedance, which is then compared to the reach point. The second stage involves identifying the fault location by selecting the correct faulty zone. This technique provides both main and backup protection. A central control unit supports the approach presented in this paper to communicate relays and update their settings. The LoRaWAN communication protocol is employed, as it provides more excellent coverage than other standardized communication technologies and can cover long distances. The proposed method is studied under different scenarios, including system contingency, simultaneous faults, fault resistances, locations, and types. The results of this technique provide the effectiveness of the proposed method. The fault can be cleared within 1.32–1.8 ms