State Estimation in Electric Power Systems Leveraging Graph Neural Networks

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

A digital self-care intervention for Ugandan patients with heart failure and their clinicians: User-centred design and usability study

Background: The prevalence of heart failure (HF) is increasing in Uganda. Ugandan patients with HF report receiving limited information about their illness, disease management, or empowerment to engage in self-care behaviors. Interventions targeted at improving HF self-care have been shown to improve patient quality of life and to reduce hospitalizations in high-income countries. However, such interventions remain underutilized in resource-limited settings like Uganda. Objective: To develop a digital health intervention that enables improved self-care amongst HF patients in Uganda. Methods: We implemented a user-centred design process to develop a self-care intervention entitled Medly Uganda. The ideation phase comprised a systematic scoping review and preliminary data collection amongst HF patients and clinicians in Uganda. An iterative design process was then used to advance an initial prototype into a fully-functional digital health intervention. The evaluation phase involved usability testing of the developed intervention amongst Ugandan patients with HF and their clinicians. Results: Medly Uganda is a digital health intervention that is fully integrated within a government-operated mobile health platform. The system allows patients to report daily HF symptoms, receive tailored treatment advice, and connect with a clinician when showing signs of decompensation. Medly Uganda harnesses Unstructured Supplementary Service Data technology that is already widely used in Uganda for mobile phone-based financial transactions. Usability testing showed the system to be accepted by patients, caregivers, and clinicians. Conclusions: Medly Uganda is a fully-functional and well-accepted digital health intervention that enables Ugandan HF patients to better care for themselves. Moving forward, we expect the system to help decongest cardiac clinics and improve self-care efficacy amongst HF patients in Uganda.

State Estimation in Electric Power Systems Leveraging Graph Neural Networks

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

Implementasi Association Rule Dengan Algoritma Apriori Pada Data Peminjaman Buku Upt Perpustakaan Universitas Lampung Menggunakan Metodologi CRISP-DM

Abstrak - UPT Perpustakaan Universitas Lampung merupakan UPT yang bergerak di bidang perpustakaan. Memiliki dua layanan berdasarkan interaksinya yaitu layanan teknis dan layanan pengguna. Saat ini UPT Perpustakaan Universitas Lampung memiliki buku yang tercetak sebanyak 142.776. Penelitian ini bertujuan menemukan pola association rule dengan teknik data mining memanfaatkan software RapidMiner 9.1 dalam penerapan algoritma Apriori. Metode penelitian Cross Industry Standar Process for Data Mining (CRISP-DM) dengan tahapan business understanding phase, data understanding phase, data preparation, modelling phase, evaluation phase dan deployment phase. Data yang digunakan dalam penelitian ini adalah data transaksi peminjaman buku dari tahun 2014 hingga 2017 dengan total data peminjaman buku sebanyak 170.115. Hasil pemodelan association rule dengan algoritma apriori menggunakan nilai support 0.3 dan nilai confidence 0.3 diperoleh judul buku “Metodologi pengajaran bahasa” akan meminjam “English for tourism :panduan berprofesi di dunia pariwisata” nilai support 1 dan confidence 1. Rekomendasi untuk pembelian buku disarankan mengikuti pattern lampiran hasil asosiasi.Kata kunci: UPT Perpustakaan Universitas Lampung, Data Peminjaman Buku, Data Mining, Association Rule, CRISP-DM.

Using Social Media to Analyze Public Concerns and Policy Responses to COVID-19 in Hong Kong

The outbreak of COVID-19 has caused huge economic and societal disruptions. To fight against the coronavirus, it is critical for policymakers to take swift and effective actions. In this article, we take Hong Kong as a case study, aiming to leverage social media data to support policymakers’ policy-making activities in different phases. First, in the agenda setting phase, we facilitate policymakers to identify key issues to be addressed during COVID-19. In particular, we design a novel epidemic awareness index to continuously monitor public discussion hotness of COVID-19 based on large-scale data collected from social media platforms. Then we identify the key issues by analyzing the posts and comments of the extensively discussed topics. Second, in the policy evaluation phase, we enable policymakers to conduct real-time evaluation of anti-epidemic policies. Specifically, we develop an accurate Cantonese sentiment classification model to measure the public satisfaction with anti-epidemic policies and propose a keyphrase extraction technique to further extract public opinions. To the best of our knowledge, this is the first work which conducts a large-scale social media analysis of COVID-19 in Hong Kong. The analytical results reveal some interesting findings: (1) there is a very low correlation between the number of confirmed cases and the public discussion hotness of COVID-19. The major public concern in the early stage is the shortage of anti-epidemic items. (2) The top-3 anti-epidemic measures with the greatest public satisfaction are daily press conference on COVID-19 updates, border closure, and social distancing rules.

Pembrolizumab plus dinaciclib in patients with hematologic malignancies: the phase 1b KEYNOTE-155 study

Preclinical data demonstrated that combining an anti-programmed cell death 1 (PD-1) inhibitor with a CDK9 inhibitor provided enhanced antitumor activity with no significant toxicities, suggesting this combination may be a potential therapeutic option. The multicohort, phase 1 KEYNOTE-155 study evaluated the safety and antitumor activity of the PD-1 inhibitor pembrolizumab plus the CDK9 inhibitor dinaciclib in patients with relapsed or refractory (rr) chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL) and multiple myeloma (MM). Patients enrolled were ≥18 years of age with a confirmed diagnosis of CLL, DLBCL, or MM. The study included 2 phases: a dose-evaluation phase to determine dose-limiting toxicities and a signal-detection phase. Patients received pembrolizumab 200 mg every 3 weeks plus dinaciclib 7 mg/m2 on day 1 and 10 mg/m2 on day 8 of cycle 1 and 14 mg/m2 on days 1 and 8 of cycles 2 and later. Primary endpoint was safety, and a key secondary endpoint was objective response rate (ORR), Seventy-two patients were enrolled and received ≥1 dose of study treatment (CLL, n = 17; DLBCL, n = 38; MM, n = 17). Pembrolizumab plus dinaciclib was generally well tolerated and produced no unexpected toxicities. The ORRs were 29.4% (5/17, rrCLL), 21.1% (8/38, rrDLBCL), and 0% (0/17, rrMM), respectively. At data cutoff, all 72 patients had discontinued treatment, 38 (52.8%) because of progressive disease. These findings demonstrate activity with combination pembrolizumab plus dinaciclib and suggest that a careful and comprehensive approach to explore anti-PD-1 and CDK9 inhibitor combinations is warranted. Clinical trial registration: ClinicalTrials.gov, NCT02684617

Pembangunan Permainan Mudah Alih Matematik 'Sifir Run' untuk Pembelajaran Topik Darab bagi Murid Sekolah Rendah

Bidang permainan mudah alih pada era ini telah melonjakkan evolusi pendidikan dan juga membangunkan kemahiran kognitif, spatial dan kemahiran motor (skill) serta meningkatkan kemahiran ICT. Pembangunan ICT dan teknologi telah memberi banyak peluang dan ruang untuk diterokai dalam bidang permainan mudah alih. Sifir Run merupakan sebuah aplikasi permainan mudah alih yang bertemakan pembelajaran sifir. Pembangunan aplikasi ini bertujuan untuk meningkatkan kemahiran menghafal sifir dalam kalangan murid sekolah rendah. Objektif projek ini adalah untuk mengenalpasti permasalahan murid dalam operasi darab, mereka bentuk dan membangunkan permainan mudah alih yang bertemakan sifir dan mengkaji kebolehgunaan aplikasi permainan mudah alih tersebut. Pembangunan aplikasi ini dijalankan dengan menggunakan model ADDIE. Terdapat seramai 20 orang responden dipilih oleh pengkaji bagI menjalani fasa penilaian untuk menguji kebolehgunaan aplikasi permainan mudah alih Sifir Run. Development of 'Sifir Run' Mathematical Mobile Game for Learning Multiplication Topics in Primary School Students Abstract: The field of mobile games in this era has accelerated the evolution of education and also developed cognitive, spatial and motor skills (skills) as well as improve ICT skills. The development of ICT and technology has provided many opportunities and spaces to be explored in the field of mobile gaming. Sifir Run is a mobile game application themed on learning ciphers. The development of this application aims to improve the skills of memorizing ciphers among primary school students. The objective of this project is to identify students' problems in multiplication operations, design and develop cipher -themed mobile games and study the usability of such mobile game applications. The development of this application was carried out using the ADDIE model. A total of 20 respondents were selected by the researcher to undergo the evaluation phase to test the usability of the Sifir Run mobile game application. Keywords: Educational Games, Mobile Computer Games, Multiplication.

RS-DARTS: A Convolutional Neural Architecture Search for Remote Sensing Image Scene Classification

Due to the superiority of convolutional neural networks, many deep learning methods have been used in image classification. The enormous difference between natural images and remote sensing images makes it difficult to directly utilize or modify existing CNN models for remote sensing scene classification tasks. In this article, a new paradigm is proposed that can automatically design a suitable CNN architecture for scene classification. A more efficient search framework, RS-DARTS, is adopted to find the optimal network architecture. This framework has two phases. In the search phase, some new strategies are presented, making the calculation process smoother, and better distinguishing the optimal and other operations. In addition, we added noise to suppress skip connections in order to close the gap between trained and validation processing and ensure classification accuracy. Moreover, a small part of the neural network is sampled to reduce the redundancy in exploring the network space and speed up the search processing. In the evaluation phase, the optimal cell architecture is stacked to construct the final network. Extensive experiments demonstrated the validity of the search strategy and the impressive classification performance of RS-DARTS on four public benchmark datasets. The proposed method showed more effectiveness than the manually designed CNN model and other methods of neural architecture search. Especially, in terms of search cost, RS-DARTS consumed less time than other NAS methods.

Airway Management and Anesthesia for Tracheal Resection in a 68-Year-Old: 3 Airways for the Price of 1

Subglottic tracheal stenosis can occur after prolonged intubation or tracheostomy. This stenosis can become severe and causes symptoms refractory to endoscopic interventions that require tracheal resection. This surgery presents unique anesthetic issues due to the airway anatomy, physiology, and shared airway management with the surgical team. We present the case of a 68-year-old patient who underwent cervical tracheal resection and reconstruction due to persistent symptoms despite balloon dilation and medical management with oxygen and heliox. Our anesthesia management involved several techniques that allowed the safe completion of this procedure. Firstly, we started the airway management with a combined size 4 Ambu® AuraStraight™ (Denmark) supraglottic airway device and flexible bronchoscopy to allow localization of the stenosis and dilation before endotracheal tube (ETT) placement. The conventional approach for this endoscopic evaluation phase is to use rigid bronchoscopy. Secondly, we used prior CT images to help guide our ETT tube size selection. Thirdly, we used total intravenous anesthesia during most of the procedure because of the intermittent apnea necessary to complete the tracheal resection. Lastly, extubation had to be done very carefully to minimize excessive patient neck movement and avoid any reintubation. Both could lead to a catastrophe with the newly reconstructed trachea.

MYSIMBA: Koswer Pembelajaran Multimedia Interaktif Bagi Pembelajaran Simpulan Bahasa Pelajar Darjah 6

Penyelidikan ini yang lebih tertumpu kepada asas salah satu bentuk peribahasa yang terdapat dalam silibus mata pelajaran bahasa Melayu iaitu simpulan bahasa. Metadologi Kajian dalam pembangunan koswer ini adalah menggunakan model ADDIE. Terdapat lima fasa terlibat dalam pembangunan koswer ini. Kaedah analisis yang digunakan fasa ini melibatkan analisis kandungan perlu difokuskan dari segi kesesuaian kandungan dan sasaran pengguna. Hal ini adalah kerana supaya koswer yang ingin dibangunkan mencukupi dan selaras dengan keperluan pengguna dari segi perisian dan perkakasan. Perisian yang digunakan adalah seperti Microsoft Words dan Microsoft Powerpoint dan perisian lain yang berkaitan seperti server dan sebagainya. Keberhasilan produk akan diuji melalui Technology Acceptance Model (TAM) yang digunakan dalam fasa penilaian yang akan menilai dari segi kebolehgunaan. Tegasnya, koswer ini juga dibangunkan untuk memudahkan pengajaran bagi pengajar atau guru-guru yang mengajar murid sekolah dan mengimplementasikan penggunaan multimedia dalam proses PdP. Ini bertujuan untuk menarik minat murid sekolah dalam proses mengenali simpulan bahasa dan seterusnya memudahkan proses PdP di dalam kelas. Impaknya, diharapkan koswer ini dapat memahirkan murid-murid sekolah terhadap penggunaan simpulan bahasa dalam sesi PdP dan kehidupan seharian. MYSIMBA: Interactive Multimedia Learning Courseware for Grade 6 Students' Idioms Learning Abstract: This research is more focused on basic one form of proverbs found in the Malay language syllabus of idioms. Research Methodology in the development of this courseware is using the ADDIE model. There are several phases involved in the development of this course. The method of analysis used in this phase involves content analysis should be focused in terms of content suitability and target users. This is because so that the courseware to be developed is adequate and in line with the needs of users in terms of software and hardware. The software used is like Microsoft Words and Microsoft Powerpoint and other related software such as servers and so on. Product success will be tested through the Technology Acceptance Model (TAM) used in the evaluation phase which will evaluate in terms of usability. Strictly speaking, this courseware was also developed to facilitate teaching for teachers or teachers who teach school children and implement the use of multimedia in the PdP process. This aims to attract school students in the process of recognizing idioms and further facilitate the PdP process in the classroom. As a result, it is hoped that this course will be able to educate school children on the use of idioms in PdP sessions and daily life. Keywords: ADDIE, Courseware, Instructional Technology.