Miracle-dz/UniFAR-FTUs huggingface dataset:
The Construction of Expatriate Identity: A Critical Discourse Analysis on “Indonesia Expat” Magazine

This thesis is a study on the construction of expatriate’s identity in Indonesia Expat magazine. The purpose of this study is to find how the expatriate’s identity is constructed in Indonesia Expat. In this study, the theory used was van Dijk’s (2015) critical discourse analysis. The data were parts of texts taken from Meet the Expat and Info for Expat columns in Indonesia Expat magazine. The writers took a descriptive qualitative approach in this study. According to the findings, there were five identities constructed about the expatriates: skillful, successful, wealthy, healthy, and caring. These identities were constructed by utilizing background, details, graphics, and word choice to generate particular interpretations.Keywords: Critical discourse analysis, expatriate, identity, elite
Team Cognition Approach in Agile Software Development

Crowdsourcing cognitive science (CCS) provides a cutting-edge development strategy for scientific advancement and innovation to leverage the positive features of the platform. Utilizing the positive qualities of the platform and mitigating risk is the prime decisive task. The crowdsourcing paradigm shortens software development time by increasing the parallelism of design, coding and testing through flexible implementation. Although software development tasks using traditional paradigms were more complex and interdependent, they can be made easy using the crowdsourcing. CCS offers a new way for scientific advancement in this field. The objective of this study is to build more adaptable quality software. As per WHO/World Bank, 15% people in the world have challenges due to disabilities. In the Mission 2030 for Sustainable Development, disability cannot be seen as a hurdle for the lack of digital access and for the achievement of their human rights. It is also important to reinforce the exogenous effect in order to sustain the internal development of human capital and technology for agile process convergence. Crowd-sourced design reduces development cost, time, and effort by involving intelligence, creativity, and critical thinking of people at different levels of society. In this study, internal consistency and reliability of team are also assessed with the help of Cronbach’s alpha using IBM-SPSS. The given approach has been validated for its effectiveness and verified with the help of Smart India Hackathon (SIH-2019) datasets.
Safety of a feed additive consisting of a dried aqueous ethanol extract from the leaves of Melissa officinalis L. for all animal species (Nor‐Feed SAS)

Abstract The additive under assessment is a dried aqueous ethanol extract of Melissa officinalis L. leaves, intended to be used as a sensory additive (flavouring compound) in feed for all animal species. The aqueous ethanol extract is specified to contain ≥ 10% of hydroxycinnamic acid derivatives including ≥ 3% of rosmarinic acid. In a previous assessment, considering the contradictory data from the Ames tests and uncertainty about the qualitative and quantitative presence of flavonoids and other compounds in the extract from M. officinalis L. leaves, the Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) could not conclude on the safety of the additive at the proposed use levels of up to 100 mg/kg complete feed for the target species, the consumer and the user. The new data submitted identified luteolin 3′‐glucuronide as the only flavonoid present in the additive, improved the characterisation of the hydroxycinnamates present and demonstrated that the additive is not genotoxic. The FEEDAP Panel concludes that the additive under assessment is safe up to the maximum proposed use level of 100 mg/kg complete feed for all animal species. The use in water for drinking is safe provided that the total daily intake of the additive does not exceed the daily amount that is considered safe when consumed via feed. No safety concern would arise for the consumer from the use of the additive up to the highest safe level in animal nutrition. The exposure of users to dusts from the additive is not of concern. No data are provided on irritant properties for eyes or skin, thus no conclusion can be drawn on these aspects. Due to the nature of the additive, it may be assumed to be potentially both a skin and respiratory sensitiser.
Computational analysis reveals similarities and differences between SCLC subtypes

Small cell lung cancer (SCLC) is a neuroendocrine malignancy with dismal survival rates. Previous studies have revealed inter and intra tumoral heterogeneity of SCLC driven by neuroendocrine differentiation and multiple gene expression signatures have been proposed to classify the distinct SCLC molecular subtypes However, few questions remain unanswered: a) how many SCLC subtypes exist? b) how similar or different are these subtypes? , c) which gene list(s) can be used to identify those specific subtypes? Here, we show that irrespective of the three gene sets (33 genes, 50 genes, 105 genes) proposed in different studies to classify SCLC into different subtypes, the markers of phenotypic heterogeneity in SCLC form a “teams” like pattern of co-expressed modules. Moreover, the 105 geneset could classify SCLC cell lines into five clusters, three of which can be distinctly mapped to the SCLC-A, SCLC-N and SCLC-Y subtypes. Intriguingly, we noticed a high degree of similarity in the transcriptional landscape of two non-neuroendocrine subtypes: SCLC-Y and SCLC-I*, as well as in their enrichment of EMT. Thus, our analysis elucidates the landscape of phenotypic heterogeneity enabling diverse SCLC subtypes.
SCAN-NF: A CNN-based System for the Classification of Electronic Invoices through Short-text Product Description

: This research presents a Convolutional Neural Network (CNN) based system, named SCAN-NF, to classify Consumer Electronic Invoices (NFC-e) based on product description. Due to how individual issuers submit Consumer Electronic Invoices, processing these invoices is often a challenging task. Information reported is often incomplete or presents mistakes. Before any meaningful processing over these invoices, it is necessary to assess the product represented in each document. SCAN-NF is developed to identify correct products codes in electronic invoices based on short-text product descriptions. Real data from Brazilian NFC-e and NF-e documents related to B2B and retail transactions are used in experiments. Comparing base single model and proposed ensemble model approaches, the evaluation results using recall, precision, and accuracy show the satisfaction of the developed system.
Impact of Protection on Domestic Processing of Primary Commodities for Export Markets

Processing of primary commodities to export more ‘value added’ products has become a widely adopted development strategy to accelerate employment and export revenues in many developing countries following the experience of East Asian NICs. However, policies which are in place to protect domestic producers limit the effectiveness of such an approach by introducing distortions in factor and input markets. In this paper, we examine how protection for a group of primary product producers affects the performance of domestic value addition for export markets. We consider the case of the tea import regulation policy of Sri Lanka and its effects on the performance of exporting tea in a value added form. The analysis shows how protection of a producer group can prevent development of the value added tea export industry of Sri Lanka. The findings should be of interest to policy makers as well as development analysts.
Towards Financial Sustainability: Beneficiaries’ Perception and Performance of Community Water Supply Services in Ghana

Revenue mobilization is critical for community-managed water systems to overcome financial constraints and to achieve financial sustainability. Using data from beneficiaries of a community managed water supply system in Ghana, we employed descriptive statistics, chi-square, perception index and document review of the system’s financial reports to assess beneficiaries’ views and perception on revenue mobilization for operations and maintenance, system expansion as well as sustainability of the project. The results showed that revenue mobilization for sustainability of operations and maintenance is significantly influenced by water connection type and religion while marital status, age and income of beneficiaries determine sustainability of the project’s expansion. The findings further revealed a significant relationship between sustainability of replacement of the project’s accessories and water connection type as well as gender, marital status, age and income of beneficiaries. The average perception index of 3.2 showed that beneficiaries perceived revenue mobilization as very good for replacing the water system’s accessories. Revenue mobilization is able to support the water system’s expansion to help meet the increasing water demands. In addition, with an average perception index of 3.6, the beneficiaries’ perception was that revenue was enough to fund operations and maintenance. Furthermore, the document review of the system’s financial reports confirmed beneficiaries’ perception of sufficiency of revenue for operations and maintenance. Finally, we found weaknesses in revenue mobilization with over 40% of bills in arrears, mostly from private users. To build resilience to the financial challenge with enhanced innovations, the study recommends the institution of effective debt recovery strategies such as the provision of pre-paid metering for private users, similar to the public standpipe pay-as-you-fetch system as well as the introduction of smart tap technology for public standpipes in community-managed water supply systems.
Comparison of In-Person and Telesimulation for Critical Care Training during the COVID-19 Pandemic

Background The coronavirus disease (COVID-19) pandemic has disrupted medical education for trainees of all levels. Although telesimulation was initially used to train in resource-limited environments, it may be a reasonable alternative for replicating authentic patient experiences for medical students during the COVID-19 pandemic. It is unclear whether a more passive approach through telesimulation training is as effective as traditional in-person simulation training. Objective Our aim was to evaluate the effectiveness of in-person versus remote simulation training on learners’ comfort with managing critical care scenarios. Methods This was a prospective observational cohort study assessing the impact of an in-person versus remote simulation course on volunteer fourth-year medical students from February to April 2021 at the University of California San Diego School of Medicine. Precourse and postcourse surveys were performed anonymously using an online secure resource. Results In the in-person learners, there was statistically significant improvement in learner comfort across all technical, behavioral, and cognitive domains. In remote learners, there was a trend toward improvement in self-reported comfort across technical and cognitive domains in the telesimulation course. However, the only statistically significant improvement in postcourse surveys of telesimulation learners, compared with baseline, was in running codes. Regardless of the training modality, the students had a positive experience with the critical care simulation course, ranking it, on average, 9.6 out of 10 (9.9 in in-person simulation vs. 9.3 in telesimulation; P = 0.06). Conclusion We demonstrated that implementation of a telesimulation-based simulation course focusing on critical care cases is feasible and well received by trainees. Although a telesimulation-based simulation course may not be as effective for remote learners as active in-person participants, our study provided evidence that there was still a trend toward improving provider readiness across technical and cognitive domains when approaching critical care cases.
Continuous Parameter Control in Genetic Algorithms using Policy Gradient Reinforcement Learning

: Genetic Algorithms are biological-inspired optimization techniques that are able to solve complex problems by evolving candidate solutions in the search space. Their evolutionary features rely on parameterized stochastic operators that are sensitive to changes and, ultimately, determine the performance of the algorithms. In recent years, Reinforcement Learning has been proposed for online parameter control in contrast to traditional ﬁne-tuning, which inevitably leads to suboptimal conﬁgurations found through extensive trial-and-error. In this regard, the current literature has focused on value-based Reinforcement Learning controllers for Genetic Algorithms without exploring the advantages of policy gradient methods in such environments. In this study, we propose a novel approach to leverage the continuous nature of the latter with agents that learn a behavior policy and enhance the performance of Genetic Algorithms by tuning their operators dynamically at runtime. In particular, we look at Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO). The resulting hybrid algorithms are tested on benchmark combinatorial problems and performance metrics are discussed in great detail considering the existing work based on Q-Learning and SARSA.
Research Progress and Application of Single-Atom Catalysts: A Review

Due to excellent performance properties such as strong activity and high selectivity, single-atom catalysts have been widely used in various catalytic reactions. Exploring the application of single-atom catalysts and elucidating their reaction mechanism has become a hot area of research. This article first introduces the structure and characteristics of single-atom catalysts, and then reviews recent preparation methods, characterization techniques, and applications of single-atom catalysts, including their application potential in electrochemistry and photocatalytic reactions. Finally, application prospects and future development directions of single-atom catalysts are outlined.
Intergenerational Decision Making: The Role of Family Relationships in Medical Decision Making

A symposium held at the 42nd annual Society for Medical Decision Making conference on October 26, 2020, focused on intergenerational decision making. The symposium covered existing research and clinical experiences using formal presentations and moderated discussion and was attended by 43 people. Presentations focused on the roles of pediatric patients in decision making, caregiver decision making for a child with complex medical needs, caregiver involvement in advanced care planning, and the inclusion of spillover effects in economic evaluations. The moderated discussion, summarized in this article, highlighted existing resources and gaps in intergenerational decision making in four areas: decision aids, economic evaluation, participant perspectives, and measures. Intergenerational decision making is an understudied and poorly understood aspect of medical decision making that requires particular attention as our society ages and technological advances provide new innovations for life-sustaining measures across all stages of the lifespan.
Artist Recommendation based on Association Rule Mining and Community Detection

: Recent advances in the web have greatly increased the accessibility of music streaming platforms and the amount of consumable audio content. This has made automated recommendation systems a necessity for listeners and streaming platforms alike. Therefore, a wide variety of predictive models have been designed to identify related artists and music collections. In this paper, we proposed a graph-based approach that utilizes association rules extracted from Spotify playlists. We constructed several artist networks and identiﬁed related artist clusters using Louvain and Label Propagation community detection algorithms. We analyzed internal and external cluster agreements based on different validation criteria. As a result, we achieved up to 99.38% internal and 90.53% external agreements between our models and Spotify’s related artist lists. These results show that integrating association rule mining concepts with graph databases can be a novel and effective way to design an artist recommendation system.
Iterative methods for optimization problems and image restoration

In this paper, we introduce a new accelerated iterative method for finding a common fixed point of a countable family of nonexpansive mappings in the Hilbert spaces framework. Using our main result, we obtain a new accelerated image restoration iterative method for solving a minimization problem in the form of the sum of two proper lower semi-continuous and convex functions. As applications, we apply our algorithm to solving image restoration problems.
Patient-derived zebrafish xenograft models reveal ferroptosis as a fatal and druggable weakness in metastatic uveal melanoma

Uveal melanoma (UM) is the most common intraocular melanoma, derived from transformed melanocytes of the uvea. Although treatment of primary UM is usually successful, there is a high risk (up to 50%) of liver metastasis with negligible long-term survival. There are currently no reproducible patient-derived animal models that faithfully recapitulate the latter stages of metastatic dissemination of UM, hindering the discovery of curative treatments. To overcome this problem and to accelerate the development of new metastatic UM treatments, we developed a patient-derived zebrafish xenograft (zf-PDX) model, using spheroid cultures generated from metastatic and primary UM tissues. Engrafted UM cells derived from these spheroid cultures give rise to metastatic lesions and recapitulate the molecular features of UMs and their potential drug sensitivity. Importantly, harnessing this versatile model, we reveal a high sensitivity of circulating UM cells to ferroptosis induction in vivo by Erastin and RSL3. Our findings are further corroborated by supportive analysis of patient data implicating ferroptosis as a new, and druggable, target for the treatment of metastatic UM patients, specifically in those with BAP1 loss in the tumor.
Harnessing the Power of Community Science to Address Data Gaps in Arctic Observing: Invasive Species in Alaska as Case Examples

The Arctic is undergoing large-scale changes that are likely to accelerate in future decades such as introductions and expansions of invasive species. The Arctic is in a unique position to prevent new introductions and spread of existing invasive species by adopting policies and actions aimed at early detection. Responding to threats from invasive species to minimize impacts to ecosystems, communities, food security, and northern economies will necessitate extensive observations and monitoring, but resource managers often face decisions without having adequate data and resources at hand. Local observing programs such as citizen science and community-based monitoring programs present attractive methods for increasing observing capacity that span contributory and co-created approaches while raising awareness of an issue among stakeholders. While the co-created model has been widely applied and encouraged in the Arctic context, contributory citizen science programs offer an additional tool for addressing observing needs in the Arctic. We showcase three contributory citizen science programs related to freshwater, terrestrial, and marine environments that have supported the objectives of the Alaska Invasive Species Partnership. We discuss criteria for achieving ARIAS priority actions at the participant scale related to participants’ motivation and participants’ understanding of the value of their contributions, at the programmatic scale, for example promoting accessible, reciprocal, and transparent knowledge exchange, and at the policy and science scale where management action is data driven. The approach is aimed at successful integration of citizen science into Arctic policy making. Finally, we discuss challenges related to broader global data collection and future directions for contributory citizen science within Arctic observing networks.
Comparative Biocompatibilities of Various Sizes of AgCIT and AgPVP with their Protein Coronas Nanoparticles

Biocompatibilities of nanoparticles are crucial for biomedical applications. Diverse silver nanoparticles (5 nm, 10 nm, 20 nm, 40 nm and 80 nm) caped with citrate and polyvinylpyrrolidone (PVP) were synthesized and primed their protein coronas. Nanoparticles were characterized with UV-visible spectroscope, Dynamic light scattering (DLS) and Transmission Electron Microscope (TEM). Comparative biocompatibilities were verified and recorded using MTS techniques. Human hepatoma carcinoma HepG2) cell line was used for measuring cytotoxic effect by MTS assays. Deleterious and comparative behaviors of citrate and PVP supported nanoparticles with varied dimensions were investigated and concluded; that citrate caped nanoparticles are comparatively less toxic and independent of size than PVP supported nanoparticles, having increased cytotoxicity with increasing size. The cytotoxic effect of citrate caped and its protein coronas nanoparticles was insignificant, while the boosted concentration of PVP supported nanoparticles enhanced the toxic effect, which endorsed enlarged size and amount of PVP supported nanoparticles. As medicinal precursors, the overwhelming use of PVP nanoparticles should be avoided, and a unique protocol must be designed if its use is crucial and unavoidable.
SNR Analysis for Quantitative Comparison of Line Detection Methods

The need for line detection in images is growing rapidly due to its importance in many image processing applications. The selection of an appropriate line detection method is essential for accurate detection of line pixels, but few studies provide an analytical basis for selecting a specific line detection method. In this study, to solve the problem, a method to analytically determine the signal-to-noise ratio (SNR) of line detection methods is proposed. Three line detection methods were selected for comparison: edge-detection (ED)-based, second derivative (SD)-based, and the sum of gradient angle differences (SGAD)-based line detection methods. Then, this study quantifies the SNR of the three line detectors through error propagation and signal noise coupling. In addition, the derived SNRs are graphically visualized to explicitly compare the performance of line detectors. Then, the quantified SNRs were validated by showing that they are highly correlated with the completeness and correctness observed in the experiment with a set of natural images. The experimental results show that the proposed SNR analysis can be used to select or design a suitable line detector.
Specialized Neural Network Pruning for Boolean Abstractions

: The inherent intricate topology of a neural network (NN) decreases our understanding of its function and purpose. Neural network abstraction and analysis techniques are designed to increase the comprehensibility of these computing structures. To achieve a more concise and interpretable representation of a NN as a Boolean graph (BG), we introduce the Neural Constantness Heuristic (NCH), Neural Constant Propagation (NCP), shared logic, the Neural Real-Valued Constantness Heuristic (NRVCH), and negligible neural nodes. These techniques reduce a neural layer’s input space and the number of nodes for a problem in NP (reducing its complexity). Additionally, we contrast two parsing methods that translate NNs to BGs: reverse traversal ( N ) and forward traversal ( F ). For most use cases, the combination of NRVCH, NCP, and N is the best choice.