Miracle-dz/UniFAR-FTUs huggingface dataset:
Wrapping a MOOC: Student Perceptions of an Experiment in Blended Learning

Although massive open online courses (MOOCs) are seen to be, and are in fact designed to be, stand-alone online courses, their introduction to the higher education landscape has expanded the space of possibilities for blended course designs (those that combine online and face-to-face learning experiences). Instead of replacing courses at higher education institutions, could MOOCs enhance those courses? This paper reports one such exploration, in which a Stanford University Machine Learning MOOC was integrated into a graduate course in machine learning at Vanderbilt University during the Fall 2012 semester. The blended course design, which leveraged a MOOC course and platform for lecturing, grading, and discussion, enabled the Vanderbilt instructor to lead an overload course in a topic much desired by students. The study shows that while students regarded some elements of the course positively, they had concerns about the coupling of online and in-class components of this particular blended course design. Analysis of student and instructor reflections on the course suggests dimensions for characterizing blended course designs that incorporate MOOCs, either in whole or in part. Given the reported challenges in this case study of integrating a MOOC in its entirety in an on-campus course, the paper advocates for more complex forms of blended learning in which course materials are drawn from multiple MOOCs, as well as from other online sources.
Minimization of acoustic noise in variable speed induction motors using a modified PWM drive

This paper describes an effective way of reducing acoustic noise in a motor supplied from an adjustable speed PWM drive. The PWM patterns are selected to eliminate the most acoustically sensitive harmonics in the motor's vibration spectra. This permits the PWM drive to use more economical yet slower switching devices while maintaining quiet operation. This method is applicable to both voltage source and current source inverters since the current waveform is used as the basis for eliminating the objectionable harmonics. >
An Input-Oriented Power Sharing Control Scheme With Fast-Dynamic Response for ISOP DAB DC–DC Converter

With some advantages, such as electric isolation, high efficiency, and fast dynamic response, the input-series output-parallel (ISOP) dual-active-bridge (DAB) dc–dc converter has been regarded as one of the most promising candidates for connecting the medium voltage terminal and the low voltage terminal. For the ISOP DAB dc–dc converter, the existing control strategies are mainly focusing on the equivalent power sharing control, but the fast-dynamic response is not included and the decoupling between the regulation of input voltage and the adjustment of output voltage is not eliminated significantly. In this article, the average model of this ISOP DAB dc–dc converter is presented first, which can be employed to analyze the power distributions of this modular topology clearly. Then, an input-oriented power sharing control scheme with fast-dynamic response is proposed for ensuring both the power sharing ability and the fast-dynamic performance of the ISOP DAB dc–dc converter in this article. Compared with the existing methods, this proposed scheme can also significantly reduce the coupling between the power sharing control and the output voltage regulation. In addition, an inductance-estimating method is proposed for ensuring the power sharing performance of the ISOP DAB dc–dc converter. Finally, the experimental results are provided to verify the effectiveness of the proposed input-oriented power sharing control with fast-dynamic response for the ISOP DAB dc–dc converter system.
Effect of Family Practice Contract Services on the Perceived Quality of Primary Care among Patients with Multimorbidity: A Cross-Sectional Study in Guangdong, China

Family practice contract services, an important primary-care reform policy for improving primary healthcare quality in China, incorporate patients with multiple chronic conditions into the priority coverage groups and focus on their management. This study aims to explore the family practice contract services’ effectiveness in improving the quality of primary care experienced by this population. A cross-sectional study using a three-stage sampling was conducted from January to March 2019 in Guangdong, China. A multivariable linear regression, including interaction terms, was applied to examine the associations between the contract services and primary care quality among people with different chronic conditions. The process quality of primary care was measured in six dimensions using the validated assessment survey of primary care (ASPC) scale. People with contract services scored higher in terms of quality of primary care than those without contract services. Contract services moderated the association between chronic condition status and primary care quality. Significantly positive interactions were observed in the patient-centred care dimension and negative interactions were reflected in the accessibility dimension. Our findings suggest that family practice contract services play a crucial role in improving patient-perceived primary care quality and provide emerging evidence that patients with multimorbidity tend to benefit more from the services, especially in patient-centred care.
Functional measures as potential indicators of down‐the‐drain chemical stress in freshwater ecological risk assessment

Conventional ecological risk assessment (ERA) predominately evaluates the impact of individual chemical stressors on a limited range of taxa, which are assumed to act as proxies to predict impacts on freshwater ecosystem function. However, it is recognized that this approach has limited ecological relevance. We reviewed the published literature to identify measures that are potential functional indicators of down‐the‐drain chemical stress, as an approach to building more ecological relevance into ERA. We found wide variation in the use of the term “ecosystem function,” and concluded it is important to distinguish between measures of processes and measures of the capacity for processes (i.e., species' functional traits). Here, we present a classification of potential functional indicators and suggest that including indicators more directly connected with processes will improve the detection of impacts on ecosystem functioning. The rate of leaf litter breakdown, oxygen production, carbon dioxide consumption, and biomass production have great potential to be used as functional indicators. However, the limited supporting evidence means that further study is needed before these measures can be fully implemented and interpreted within an ERA and regulatory context. Sensitivity to chemical stress is likely to vary among functional indicators depending on the stressor and ecosystem context. Therefore, we recommend that ERA incorporates a variety of indicators relevant to each aspect of the function of interest, such as a direct measure of a process (e.g., rate of leaf litter breakdown) and a capacity for a process (e.g., functional composition of macroinvertebrates), alongside structural indicators (e.g., taxonomic diversity of macroinvertebrates). Overall, we believe that the consideration of functional indicators can add value to ERA by providing greater ecological relevance, particularly in relation to indirect effects, functional compensation (Box 1), interactions of multiple stressors, and the importance of ecosystem context. Environ Assess Manag 2022;18:1135–1147. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach

Background Pharmacovigilance and safety reporting, which involve processes for monitoring the use of medicines in clinical trials, play a critical role in the identification of previously unrecognized adverse events or changes in the patterns of adverse events. Objective This study aims to demonstrate the feasibility of automating the coding of adverse events described in the narrative section of the serious adverse event report forms to enable statistical analysis of the aforementioned patterns. Methods We used the Uniﬁed Medical Language System (UMLS) as the coding scheme, which integrates 217 source vocabularies, thus enabling coding against other relevant terminologies such as the International Classification of Diseases–10th Revision, Medical Dictionary for Regulatory Activities, and Systematized Nomenclature of Medicine). We used MetaMap, a highly configurable dictionary lookup software, to identify the mentions of the UMLS concepts. We trained a binary classifier using Bidirectional Encoder Representations from Transformers (BERT), a transformer-based language model that captures contextual relationships, to differentiate between mentions of the UMLS concepts that represented adverse events and those that did not. Results The model achieved a high F1 score of 0.8080, despite the class imbalance. This is 10.15 percent points lower than human-like performance but also 17.45 percent points higher than that of the baseline approach. Conclusions These results confirmed that automated coding of adverse events described in the narrative section of serious adverse event reports is feasible. Once coded, adverse events can be statistically analyzed so that any correlations with the trialed medicines can be estimated in a timely fashion.
Dimensionality Reduction Methods for Alzheimer's Disease Classification

Alzheimer’s Disease (AD) is the most common type of dementia in the world. There is no cure for the disease. The early detection of the disease is the goal for better AD treatment. Computer-aided diagnosis serves as a supportive tool in AD diagnosis, which classifies the stages of AD from the three-dimensional (3D) brain images. In image processing, a 3D image will result in millions features. Therefore, apart from extracting significant features for AD classification, the feature extraction step also involves reducing the dimensions of the data. This paper aims to investigate the suitability of the current dimensionality reduction methods on AD classification. In addition, this paper also examines the impact of various intrinsic dimension estimation techniques on the dimensionality reduction techniques. The contribution of this paper is to conduct the comparative study with same dataset, which allows a comparison of the strengths of existing methods on AD classification. A total of 200 subjects with T1-weighted images were obtained at different time points from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The performance measurement of this paper is classification accuracy. The best approach among the methods discussed in this paper is the combination of discrete wavelet transform and principal component analysis, and it achieved 87% accuracy in average for the dataset collected at different time points. It reveals that the current techniques have the strength in extracting significant features for AD classification. However, the classification performance is influenced by the intrinsic dimension on dimensionality reduction. Therefore, the rationale and suggestions for improvement of the methods are discussed.
Het Nederlandse wetsvoorstel kilometerprijs: hoe was het gedacht?

Like in many other countries, during the last few decades much thought has been given in the Netherlands to an alternative charging system for motor vehicles. Considering serious and growing congestion on the road, it was felt that not the possession of a motor vehicle should be taxed, but the use that is made of it. However, up till now no proposal ever made it into law. The latest episode in this story is the 2009 proposal of the former Dutch government for a kilometre charge based on gps-technology. Tariffs per kilometre would depend on the type of motor vehicle and characteristics like CO2-emission, particles emission, type of fuel and (maximum) weight. In cases of structural congestion an extra rush hour surcharge could apply. The proposal will not be taken over by the new Dutch government. Nevertheless, for those interested the proposal will be described here more in detail.
The Trust in Voting Systems (TVS) Measure

It is essential to democracy that voters trust voting systems enough to participate in elections and use these systems. Unfortunately, voter trust has been found to be low in many situations, which could detrimentally impact human-computer interactions in voting. Therefore, it is important to understand the degree to which voters trust any specific voting method. Voting researchers have developed and used measures of overall trust in technology; yet researchers have long argued that trust in systems is domain-specific, implying that system-specific measures should be used instead. To address this latter point, this paper describes the development of a psychometrically reliable and validated instrument called the Trust in Voting Systems (TVS) measure. The TVS not only allows researchers to understand group mean differences in trust across voting systems; it also allows researchers to understand individual differences in trust within systems—all of which collectively serves to inform and improve voting systems.
How Can We Provide Additively Manufactured Parts with a Fingerprint? A Review of Tagging Strategies in Additive Manufacturing

Additive manufacturing (AM) is rapidly evolving from “rapid prototyping” to “industrial production”. AM enables the fabrication of bespoke components with complicated geometries in the high-performance areas of aerospace, defence and biomedicine. Providing AM parts with a tagging feature that allows them to be identified like a fingerprint can be crucial for logistics, certification and anti-counterfeiting purposes. Whereas the implementation of an overarching strategy for the complete traceability of AM components downstream from designer to end user is, by nature, a cross-disciplinary task that involves legal, digital and technological issues, materials engineers are on the front line of research to understand what kind of tag is preferred for each kind of object and how existing materials and 3D printing hardware should be synergistically modified to create such tag. This review provides a critical analysis of the main requirements and properties of tagging features for authentication and identification of AM parts, of the strategies that have been put in place so far, and of the future challenges that are emerging to make these systems efficient and suitable for digitalisation. It is envisaged that this literature survey will help scientists and developers answer the challenging question: “How can we embed a tagging feature in an AM part? ”.
Modern Authentication Schemes in Smartphones and IoT Devices: An Empirical Survey

User authentication remains a challenging issue, despite the existence of a large number of proposed solutions, such as traditional text-based, graphical-based, biometrics-based, Web-based, and hardware-based schemes. For example, some of these schemes are not suitable for deployment in an Internet of Things (IoT) setting, partly due to the hardware and/or software constraints of IoT devices. The increasing popularity and pervasiveness of IoT equipment in a broad range of settings reinforces the importance of ensuring the security and privacy of IoT devices. Therefore, in this article, we conduct a comprehensive literature review and an empirical study to gain an in-depth understanding of the different authentication schemes as well as their vulnerabilities and deficits against various types of cyberattacks when applied in IoT-based systems. Based on the identified limitations, we recommend several mitigation strategies and discuss the practical implications of our findings.
Supplier selection and purchase problem for multi-echelon defective supply chain system with stochastic demand

In the real production process, some members in the supply chain system sometimes cannot effectively complete their production task because of defects involving the production or purchasing of components. A supply chain system that has defects in at least one echelon is called a multi-echelon defective supply chain (MDSC) system. Most supply chain systems are MDSC systems. Determining parts or components supply quota from different suppliers with limited suppliers, factories and distribution centers capacities in the supply chain system are becoming an important issue for businesses. In this study, we propose a new heuristic (H2) which is an extension of H1 heuristic that was previously presented. The MDSC system was formed with the mixed integer linear programming by LINDO software for calculation of the lower bound. The heuristics and MDSC system were modeled by using ProModel software. The heuristics were applied to a case from the Turkish furniture industry. The heuristics were compared with each other by considering different coefficients of variation, service levels, and deviation from lower bound. Simulation experiments showed that the proposed H2 heuristic outperformed the H1 heuristic.
Vitamin D Analogs Regulate the Vitamin D System and Cell Viability in Ovarian Cancer Cells

Background: Ovarian cancer (OC) is one of the most lethal cancers in women. The active form of vitamin D3, 1,25-dihydroxyvitamin D3 (1,25D3, calcitriol) has anticancer activity in several cancers, including ovarian cancer, but the required pharmacological doses may cause hypercalcemia. We hypothesized that newly developed, low calcemic, vitamin D analogs (an1,25Ds) may be used as anticancer agents instead of calcitriol in ovarian cancer cells. Methods: We used two patient-derived high-grade serous ovarian cancer (HGSOC) cell lines with low (13781) and high (14433) mRNA expression levels of the gene encoding 1,25-dihydroxyvitamin D3 24-hydroxylase CYP24A1, one of the main target genes of calcitriol. We tested the effect of calcitriol and four structurally related series of an1,25Ds (PRI-1906, PRI-1907, PRI-5201, PRI-5202) on cell number, viability, the expression of CYP24A1, and the vitamin D receptor (VDR). Results: CYP24A1 mRNA expression increased in a concentration-dependent manner after treatment with all compounds. In both cell lines, after 4 h, PRI-5202 was the most potent analog (in 13781 cells: EC50 = 2.98 ± 1.10 nmol/L, in 14433 cells: EC50 = 0.92 ± 0.20 nmol/L), while PRI-1907 was the least active one (in 13781 cells: EC50 = n/d, in 14433 cells: EC50 = n/d). This difference among the analogs disappeared after 5 days of treatment. The 13781 cells were more sensitive to the an1,25Ds compared with 14433 cells. The an1,25Ds increased nuclear VDR levels and reduced cell viability, but only in the 13781 cell line. Conclusions: The an1,25Ds had different potencies in the HGSOC cell lines and their efficacy in increasing CYP24A1 expression was cell line- and chemical structure-dependent. Therefore, choosing sensitive cancer cell lines and further optimization of the analogs’ structure might lead to new treatment options against ovarian cancer.
Impact of the COVID-19 Pandemic on HIV Services in Korea: Results from a Cross-Sectional Online Survey

Background Globally, the coronavirus disease 2019 (COVID-19) pandemic has compromised human immunodeficiency virus (HIV) services. The study aimed to assess the impact of COVID-19 on the access and delivery of HIV care in Korea. Materials and Methods People living with HIV (PLHIV), people at risk of HIV (PAR) and prescribers of HIV care were recruited through a patient advocacy group, online communities for men who have sex with men (MSM) and a HIV care center for a web-based survey between October 22 and November 26, 2020. The survey compared the frequency of hospital/clinic visits, HIV-related testing, access to antiretroviral therapy (ART) or preventive medications, and experience with telehealth services by PLHIV and PAR between the pre-pandemic and pandemic eras. Results One hundred and twelve PLHIV (mean age: 38.5 ± 10.2 years), 174 PAR (mean age: 33.5 ± 8.0 years) and 9 prescribers participated the survey; ≥97% of the PLHIV and PAR were male. A greater proportion of PAR than PLHIV reported a decrease in the frequency of hospital/clinical visits (59.2% vs. 17.0%) and HIV-related testing (50.6% vs. 6.3%) since COVID-19. Among PAR, not engaging or engaging less in high-risk behaviors was the most frequently cited reason (51.1%) for decreased frequency of HIV-related tests. A substantial proportion of PLHIV (12.5%) and PAR (50.0%) experienced interrupted use of ART and HIV preventive medications, respectively. A substantial proportion of PLHIV (35.7%) and PAR (62.5%) were concerned about the long-term accessibility of HIV care, however, >90% had not used any types of telehealth services during the pandemic. Conclusion Overall, COVID-19 has negatively impacted the access and delivery of HIV services in Korea, especially HIV-related testing for PAR. Our findings highlight the need to develop strategies to mitigate the interrupted HIV care.
Localized Surface Plasmon Resonance Decorated with Carbon Quantum Dots and Triangular Ag Nanoparticles for Chlorophyll Detection

This paper demonstrates carbon quantum dots (CQDs) with triangular silver nanoparticles (AgNPs) as the sensing materials of localized surface plasmon resonance (LSPR) sensors for chlorophyll detection. The CQDs and AgNPs were prepared by a one-step hydrothermal process and a direct chemical reduction process, respectively. FTIR analysis shows that a CQD consists of NH2, OH, and COOH functional groups. The appearance of C=O and NH2 at 399.5 eV and 529.6 eV in XPS analysis indicates that functional groups are available for adsorption sites for chlorophyll interaction. A AgNP–CQD composite was coated on the glass slide surface using (3-aminopropyl) triethoxysilane (APTES) as a coupling agent and acted as the active sensing layer for chlorophyll detection. In LSPR sensing, the linear response detection for AgNP–CQD demonstrates R2 = 0.9581 and a sensitivity of 0.80 nm ppm−1, with a detection limit of 4.71 ppm ranging from 0.2 to 10.0 ppm. Meanwhile, a AgNP shows a linear response of R2 = 0.1541 and a sensitivity of 0.25 nm ppm−1, with the detection limit of 52.76 ppm upon exposure to chlorophyll. Based on these results, the AgNP–CQD composite shows a better linearity response and a higher sensitivity than bare AgNPs when exposed to chlorophyll, highlighting the potential of AgNP–CQD as a sensing material in this study.
MICROSCREENING FOR STORMWATER TREATMENT Mikrosilning för rening av bräddvatten

A competitive system for stormwater treatment should provide efficient particle removal, small footprint, rapid start-up, robustness, cost effectiveness and flexibility. Chemical pre-treatment followed by microscreening could fulfil these requirements. Thus the potential in such a system was investigated within the framework of a project involving Kemira Kemi AB, Hydrotech AB and the Department of Water and Environmental Engineering at Lund Institute of Technology. Results from treatment of raw wastewater indicated that suspended solids as well as total phosphorous could be reduced with more than 90 %. The article summarises the first phase of the project, whereas the next phase will focus in long-term effects and the response to the dynamics of real stormwater situations.
Underground Measurements of Nuclear Reaction Cross-Sections Relevant to AGB Stars

Nuclear reaction cross sections are essential ingredients to predict the evolution of AGB stars and understand their impact on the chemical evolution of our Galaxy. Unfortunately, the cross sections of the reactions involved are often very small and challenging to measure in laboratories on Earth. In this context, major steps forward were made with the advent of underground nuclear astrophysics, pioneered by the Laboratory for Underground Nuclear Astrophysics (LUNA). The present paper reviews the contribution of LUNA to our understanding of the evolution of AGB stars and related nucleosynthesis.