ethicalabs/Research-Intent-Collab huggingface dataset:
SIMULTANEOUS INTERPRETING USED IN “THE SAMSUNG  GALAXY S24 AUTOMATICALLY TRANSLATES CALLS VIA AI”  VIDEO ON RAPPLER’S YOUTUBE CHANNEL

The existence of this scientific article aims to provide a brief description of the position of translation in the phenomenon of globalization, which is inseparable from technological advances and human mobility that demand a connection between language and culture. Therefore, to discuss the world of interpreting further, the researcher chose the title "Simultaneous Interpreting Used in "The Samsung Galaxy S24 Automatically Translates Calls Via AI" Video on Rappler's YouTube Channel". This research focuses on finding the modes of interpreting used with a descriptive qualitative method, and the data obtained comes from conversations between a person and AI (Artificial Intelligence) in one of Rappler's YouTube Channel videos, namely "The Samsung Galaxy S24 Automatically Translates Calls Via AI".  After the research was conducted, it was found that one interpreting mode was used based on the theory of Pochhacker, namely simultaneous interpreting, because the Live Translate feature released by the Samsung Galaxy S24 Series brand translates in real time and there are 24 conversations that can be delivered in one time or one breath but as if a pause was made therefore the AI ​​Interpreter could use that time to translate.
India Antimicrobial Stewardship and Resistance (INTEREST): A Needs Assessment Survey

Background: The emergence and spread of antimicrobial resistance is a major problem in India with significant knowledge on whether this is a systems–based, prescriber and patient characteristic based or diagnostic technologies–based issue. Methods: An electronic survey was sent to select distribution list of intensive care units (ICU) and hospital inpatient (medicine ward) providers from India. Survey questions included antimicrobial clinical practice data, access to electronic medical records, microbiological diagnostic techniques, and access to microbiology data. The survey focused on antimicrobial prescription trends and their association with diagnostic techniques. Results: There were 90 responses from 18 states in 65 ZIP codes. They had median of 187.5 beds (IQR, 40–470). Representative responders had a median age of 40 years (IQR, 31–53). Among the responders, 73 (81%) were men. Of the 90 responses, 48 providers (52%) practiced solely in ICUs (medical and surgical) and 40 providers (45%) practiced solely on the medical ward or floor, with the rest practicing in other units. In total, 31 centers (34%) reported full access to electronic medical records, and 53 centers (59%) had access to wi-fi or Internet. Interestingly, 27 centers (30%) needed to use personal provider phone data for Internet access. Only 26 centers (29%) had electronic microbiological data. Also, 63 respondents (70%) agreed to de-escalation behavior after receiving microbiological data. In addition, 55 respondents (61%) agreed that patients have easy access to outpatient antibiotics without an appropriate prescription, over the counter. Furthermore, 58 responders (64%) said that antibiotic resistance was a major problem at their center, and 61 responders (68%) were familiar with antimicrobial stewardship programs. Among the centers, 69 (77%) had no access to formal infectious disease programs at their center. Only 27 centers (30%) had a formal Clostridium difficile–associated infection reporting and control program. Only 28 centers (31%) had a formal occupational health program. Conclusions: In a large-scale, semistructured, online survey, most issues related to easy availability of antibiotics and lack of “electronization” of medical and microbiological records. It was reassuring that most providers expressed knowledge of the existing antimicrobial stewardship program.Funding: NoneDisclosures: None
TEM virus images: Benchmark dataset and deep learning classification

To achieve the full potential of deep learning (DL) models, such as understanding the interplay between model (size), training strategy, and amount of training data, researchers and developers need access to new dedicated image datasets; i.e., annotated collections of images representing real-world problems with all their variations, complexity, limitations, and noise. Here, we present, describe and make freely available an annotated transmission electron microscopy (TEM) image dataset. It constitutes an interesting challenge for many practical applications in virology and epidemiology; e.g., virus detection, segmentation, classification, and novelty detection. We also present benchmarking results for virus detection and recognition using some of the top-performing (large and small) networks as well as a handcrafted very small network. We compare and evaluate transfer learning and training from scratch hypothesizing that with a limited dataset, transfer learning is crucial for good performance of a large network whereas our handcrafted small network performs relatively well when training from scratch. This is one step towards understanding how much training data is needed for a given task.The benchmark dataset contains 1245 images of 22 virus classes. We propose a representative data split into training, validation, and test sets for this dataset. Moreover, we compare different established DL networks and present a baseline DL solution for classifying a subset of the 14 most-represented virus classes in the dataset.Our best model, DenseNet201 pre-trained on ImageNet and fine-tuned on the training set, achieved a 0.921 F1-score and 93.1% accuracy on the proposed representative test set.Public and real biomedical datasets are an important contribution and a necessity to increase the understanding of shortcomings, requirements, and potential improvements for deep learning solutions on biomedical problems or deploying solutions in clinical settings. We compared transfer learning to learning from scratch on this dataset and hypothesize that for limited-sized datasets transfer learning is crucial for achieving good performance for large models. Last but not least, we demonstrate the importance of application knowledge in creating datasets for training DL models and analyzing their results.
Relaxation time of chemical reactions from network thermodynamics

The relationship for the relaxation time(s) of a chemical reaction in terms of concentrations and rate constants  has been derived from the network thermodynamic approach developed by Oster, Perelson, and Katchalsky.Generally, it is necessary to draw the bond graph and the “network analogue” of the reaction scheme, followed by loop or nodal analysis of the network and finally solving of the resulting differential equations. In the case of single-step reactions, however, it is possible to obtain an expression for the relaxation time. This approach  is simpler and elegant and has certain advantages over the usual kinetic method. The method has been illustrated  by taking different reaction schemes as examples.
LSQCA: Resource-Efficient Load/Store Architecture for Limited-Scale Fault-Tolerant Quantum Computing

Current fault-tolerant quantum computer (FTQC) architectures utilize several encoding techniques to enable reliable logical operations with restricted qubit connectivity. However, such logical operations demand additional memory overhead to ensure fault tolerance. Since the main obstacle to practical quantum computing is the limited qubit count, our primary mission is to design floorplans that can reduce memory overhead without compromising computational capability. Despite extensive efforts to explore FTQC architectures, even the current state-of-the-art floorplan strategy devotes 50% of memory space to this overhead, not to data storage, to ensure unit-time random access to all logical qubits.   In this paper, we propose an FTQC architecture based on a novel floorplan strategy, Load/Store Quantum Computer Architecture (LSQCA), which can achieve almost 100% memory density. The idea behind our architecture is to separate all memory regions into small computational space called Computational Registers (CR) and space-efficient memory space called Scan-Access Memory (SAM). We define an instruction set for these abstract structures and provide concrete designs named point-SAM and line-SAM architectures. With this design, we can improve the memory density by allowing variable-latency memory access while concealing the latency with other bottlenecks. We also propose optimization techniques to exploit properties of quantum programs observed in our static analysis, such as access locality in memory reference timestamps. Our numerical results indicate that LSQCA successfully leverages this idea. In a resource-restricted situation, a specific benchmark shows that we can achieve about 90% memory density with 5% increase in the execution time compared to a conventional floorplan, which achieves at most 50% memory density for unit-time random access. Our design ensures broad quantum applicability.
CYBERL@B AS AN OPEN EDUCATIONAL RESOURCE (OER) FOR LEARNING ENGLISH THROUGH PRACTICE AND COLLABORATION

The teaching of English as a Foreign Language (EFL) is a key field in public education in developing countries.  In the case of Costa Rica, the free-access EFL platform and Open Education Resource (OER), Cyberl@b (https://cyberlab.ucr.ac.cr), was designed in order to meet public educational needs and policies, and it is targeted towards primary and secondary EFL teaching.  In this article, Cyberl@b will be described and analyzed in the light of Laurillard’s concepts of Learning Through Practice (LTP) and Learning Through Collaboration (LTC).  Based on these theoretical concepts, basic criteria were extracted in order to evaluate the platform as an educational resource that offers multimedia, synchronous and asynchronous interaction, peer and mentor feedback, as well as affordances for practice and collaboration in the following macro and micro skills: listening, speaking, reading, writing, grammar, pronunciation, and vocabulary. The study concludes that Cyberl@b allows for rich opportunities for practice and collaboration according to LTP and LTC principles for primary and secondary English learners in Costa Rica and around the world.
The PANORAMA pan-European Survey: impact of severe and non-severe hypoglycaemia on quality of life and other patient reported outcomes in patients with type 2 diabetes

Q1
Realising the right to water and sanitation in Nigeria

This dissertation investigates how the right to water and sanitation can be realised in Nigeria. The aim is to provide an alternative approach which is human rights and environment friendly. Nigeria’s laws, policies and practices were analysed to determine their consistency with the relevant international legal obligations. The factors contributing to water and sanitation problems in Nigeria and the extent of Nigerians’ awareness of the right to water and sanitation were examined using the data obtained from empirical study. Legal interpretation, human rights-based and ecosystem approaches were studied using scholarly writing and were used in demonstrating how the right can be realised in Nigeria.
Bridging Collective and Individual Approaches to Occupational Safety and Health: What Promises Does Workplace Health Promotion Hold?

This paper examines the potential of workplace health promotion to create new approaches to occupational safety and health. In relation to health, workplace health promotion is often regarded as an individualist-based competitor to occupational safety and health. However, the concept of workplace health promotion may be constructed and interpreted along different lines, drawing on different conceptions of health and health promotion. The paper presents a study of how a concept of workplace health promotion that integrates occupational safety and health and lifestyle was developed in the Danish context. This concept represents a hybrid of medical and humanistic understandings of health promotion. It escapes the inherent individualism of health promotion, and displays elements of empowerment, highlighting employee participation. At the same time, it introduces a subjective perspective to occupational safety and health. The concluding discussion considers the possibilities of further developing and institutionalising approaches that integrate collective and subjective approaches to health promotion in the workplace.
Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation

Graph-based diffusion models have shown promising results in terms of generating high-quality solutions to NP-complete (NPC) combinatorial optimization (CO) problems. However, those models are often inefficient in inference, due to the iterative evaluation nature of the denoising diffusion process. This paper proposes to use progressive distillation to speed up the inference by taking fewer steps (e.g., forecasting two steps ahead within a single step) during the denoising process. Our experimental results show that the progressively distilled model can perform inference 16 times faster with only 0.019% degradation in performance on the TSP-50 dataset.
Critical Factors Influencing Decision to Adopt Human Resource Information System (HRIS) in Hospitals

The aim of this research is to explore factors influencing the management decisions to adopt human resource information system (HRIS) in the hospital industry of Bangladesh-an emerging developing country. To understand this issue, this paper integrates two prominent adoption theories-Human-Organization-Technology fit (HOT-fit) model and Technology-Organization-Environment (TOE) framework. Thirteen factors under four dimensions were investigated to explore their influence on HRIS adoption decisions in hospitals. Employing non-probability sampling method, a total of 550 copies of structured questionnaires were distributed among HR executives of 92 private hospitals in Bangladesh. Among the respondents, usable questionnaires were 383 that suggesting a valid response rate of 69.63%. We classify the sample into 3 core groups based on the HRIS initial implementation, namely adopters, prospectors, and laggards. The obtained results specify 5 most critical factors i.e. IT infrastructure, top management support, IT capabilities of staff, perceived cost, and competitive pressure. Moreover, the most significant dimension is technological dimension followed by organisational, human, and environmental among the proposed 4 dimensions. Lastly, the study found existence of significant differences in all factors across different adopting groups. The study results also expose constructive proposals to researchers, hospitals, and the government to enhance the likelihood of adopting HRIS. The present study has important implications in understanding HRIS implementation in developing countries.
Epigenetic regulation of higher order chromatin conformations and networks

Higher order chromatin conformations result from the packaging of the genome into the physical confines of the cell nucleus. Structural hallmarks of the nucleus influence the spatio-temporal behavior of genome underlying the regulation of genomic functions. Moreover, accumulated data show that the physical proximities between interphase chromatin fibers significantly contribute to the regulation of genomic transcription, replication and repair. The dynamic patterns of spatial crosstalk between genomic regions are, moreover, controlled by environmental cues to fine-tune gene transcription. The studies in this thesis focus on the nature of higher order chromatin conformations and networks and their developmental regulation in mouse and human model systems. The thesis also includes the description of a novel technique that enables the visualization of higher order chromatin proximities in single cells at a resolution far beyond that of the microscope. Specifically, we identified developmentally regulated genome-wide chromosomal interactomes impinging on the H19 imprinting control region (ICR) in embryonic stem (ES) cells and derived embryoid bodies (EBs). The chromosomal interactomes appear poorly conserved between mouse and human. We further constructed chromosomal interaction networks with crosswise interacting pattern and present the modular topology of the human networks. The molecular glue connecting chromosomes to each other was identified as poly(ADP-ribose). TGFβ signaling was shown to rapidly rewire the chromosomal interaction networks by targeting a CTCF-PARP1 feed-back loop to decrease poly(ADP-ribose) levels in the nucleus. We further captured a developmentally conserved imprinted interaction network, which is dependent on CTCF binding sites on the maternal H19 ICR allele. This network was shown to function as a vehicle to transfer epigenetic states from H19/Igf2 domain to other imprinted domains it interacts with. We propose the principle of non-allelic transvection of epigenetic states as a notable ...
Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging

Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.905-0.960 in detecting malignant nasopharyngeal lesions and distinguishing NKTCL from nasopharyngeal carcinoma in independent validation datasets. In comparison to human radiologists, the diagnostic systems show higher accuracies than resident radiologists and comparable ones to senior radiologists. The prognostic system shows promising performance in predicting survival outcomes of NKTCL and outperforms several clinical models. For patients with early-stage NKTCL, only the high-risk group benefits from early radiotherapy (hazard ratio = 0.414 vs. late radiotherapy; 95% confidence interval, 0.190-0.900, p = 0.022), while progression-free survival does not differ in the low-risk group. In conclusion, AI-based systems show potential in assisting accurate diagnosis and prognosis prediction and may contribute to therapeutic optimization for NKTCL.
The Relationship between Corporate Social Responsibility (CSR) Reporting and Financing (Bank Loans and Debt Securities)

A critical concern for companies is securing the necessary financial resources to conduct business operations and achieve appropriate performance. Various factors can influence a company's access to financial resources, including those that can reduce information asymmetry between the company and its creditors. Given the importance of Corporate Social Responsibility (CSR) and its significant impact on public trust in companies, the purpose of this research was to investigate the relationship between CSR reporting and financing from both the money (bank loans) and capital (debt securities) markets. The sample included 134 companies listed on the Tehran Stock Exchange (TSE) from 2014 to 2020. The results showed a positive and significant relationship between CSR reporting and the amount of financing obtained from both the capital market (through debt securities issuance) and the money market (bank loans). While past research had examined the relationship between social responsibility and debt ratio or the cost of debt, the connection between CSR and debt securities issuance had not been previously considered.
Developing a natural language processing system using transformer-based models for adverse drug event detection in electronic health records

ABSTRACT                                    Objective                   To develop a transformer-based natural language processing (NLP) system for detecting adverse drug events (ADEs) from clinical notes in electronic health records (EHRs).                                                     Materials and Methods                   We fine-tuned BERT Short-Formers and Clinical-Longformer using the processed dataset of the 2018 National NLP Clinical Challenges (n2c2) shared task Track 2. We investigated two data processing methods, window-based and split-based approaches, to find an optimal processing method. We evaluated the generalization capabilities on a dataset extracted from Vanderbilt University Medical Center (VUMC) EHRs.                                                     Results                   On the n2c2 dataset, the best average macro F-scores of 0.832 and 0.868 were achieved using a 15-word window with PubMedBERT and a 10-chunk split with Clinical-Longformer. On the VUMC dataset, the best average macro F-scores of 0.720 and 0.786 were achieved using a 4-chunk split with PubMedBERT and Clinical-Longformer.                                                     Discussion                   Our study provided a comparative analysis of data processing methods. The fine-tuned transformer models showed good performance for ADE-related tasks. Especially, Clinical-Longformer model with split-based approach had a great potential for practical implementation of ADE detection. While the token limit was crucial, the chunk size also significantly influenced model performance, even when the text length was within the token limit.                                                     Conclusion                   We provided guidance on model development, including data processing methods for ADE detection from clinical notes using transformer-based models. Our results on two datasets indicated that data processing methods and models should be carefully selected based on the type of clinical notes and the allocation trade-offs of human and computational power in annotation and model fine-tuning.
<i>Self-Organized Criticality: Emergent Complex Behavior in Physical and Biological Systems</i>

Preface 1. Introduction 2. Characterization of the SOC state 3. Systems exhibiting SOC 4. Computer models 5. The search for a formalism 6. Is it SOC or is it not? Appendices.
Patient Safety Reporting Systems: Sustained Quality Improvement Using a Multidisciplinary Team and “Good Catch” Awards

Since 1999, hospitals have made substantial commitments to health care quality and patient safety through individual initiatives of executive leadership involvement in quality, investments in safety culture, education and training for medical students and residents in quality and safety, the creation of patient safety committees, and implementation of patient safety reporting systems. At the Weinberg Surgical Suite at The Johns Hopkins Hospital (Baltimore), a 16-operating-room inpatient/outpatient cancer center, a patient safety reporting process was developed to maximize the usefulness of the reports and the long-term sustainability of quality improvements arising from them.A six-phase framework was created incorporating UHC's Patient Safety Net (PSN): Identify, report, analyze, mitigate, reward, and follow up. Unique features of this process included a multidisciplinary team to review reports, mitigate hazards, educate and empower providers, recognize the identifying/reporting individuals or groups with "Good Catch" awards, and follow up to determine if quality improvements were sustained over time.Good Catch awards have been given in recognition of 29 patient safety hazards identified since 2008; in each of these cases, an initiative was developed to mitigate the original hazard. Twenty-five (86%) of the associated quality improvements have been sustained. Two Good Catch award-winning projects--vials of heparin with an unusually high concentration of the drug that posed a potential overdose hazard and a rapid infusion device that resisted practitioner control--are described in detail.A multidisciplinary team's analysis and mitigation of hazards identified in a patient safety reporting process entailed positive recognition with a Good Catch award, education of practitioners, and long-term follow-up.