ONLINE EDUCATION DURING THE COVID-19 PANDEMIC - OPPORTUNITIES AND CHALLENGES: A CASE STUDY IN THE SOUTH OF VIETNAM

The Covid-19 pandemic has led all Vietnamese students to study at home since 2021. Therefore, a number of universities in Vietnam have shifted to online learning platforms to keep their academic activities going. The challenge is whether learners can absorb all knowledge through online learning? Between the two learning platforms, traditional and modern, what will be the impact on learners? and what needs to change in teachers? Therefore, in order to have an overview and partly clarify the questions of education 4.0, the authors carried out this research among 200 Vietnamese students to highlight the significance of online education. The results indicated that the majority of the respondents (91%) are willing to online classes to manage their studies during a time of the global pandemic. The respondents claimed that the convenience and flexibility of online classrooms make it the right option while students’ self-discipline and broadband connectivity matters in rural areas make it a challenge for most students when taking advantage of online classrooms.  Article visualizations

都市部における持続可能な消費を妨げる社会技術的障壁：アジアの発展途上国におけるモビリティの事例

要約のみTohoku University松八重一代課

Thermoelectric properties of Higher Manganese Silicides

This work aims to cover a variety of aspects relating to the Higher Manganese Silicide (HMS) system, e.g. composites, substitutions, synthesis methods, and structural evolutions. The composites made of HMS-based compounds and nano-inclusions have been prepared via two different procedures, i.e. (i) solid state reaction, manually mixing, and hot pressing, or (ii) soft ball milling and reactive spark plasma sintering. The later approach has proved its effectiveness in preparing the multi-walled carbon nanotube (MWCNT)/HMS-based material composites mainly containing the HMS phases with a homogeneous distribution of MWCNTs. It was demonstrated that a fine distribution of the nano-inclusions played a crucial role in reducing thermal conductivity through enhancing phonon scattering in HMS-based materials, resulting in an improvement by about 20% for the maximum efficiency for the MWCNT/HMS-based material composite with 1.0 wt.-% MWCNTs. The substitution of molybdenum, tungsten, or silver at the Mn sites, and of germanium or aluminium at the Si sites has been studied for the HMS-based materials. The best thermoelectric efficiency among different Ge contents was achieved for the phase mixture of the non-stoichiometric composition MnSi1.75Ge0.02, which was then chosen to be the base material for further substitutions. No crucial modification of the electrical properties of the base material was observed, but large decreases of lattice thermal conductivity were achieved because of enhanced phonon scattering, with the highest reduction up to 25% for molybdenum substitution. The maximum figure of merit, ZT, value was approximately 0.40 for the material with 2 at.-% molybdenum substitution at the Mn sites. The maximum ZT values ranging from 0.31 to 0.42 have been achieved for various compositions prepared by mechanical alloying, mechanical milling and heat treating in conventional furnace, as well as by solid state reaction, which could possibly be improved by completely eliminating the side products. Subsequently, a simple and effective process was used to synthesize undoped HMS, involving ball milling in n-hexane under soft conditions to obtain homogeneous mixtures of constituting elements, and subsequent spark plasma sintering for a direct solid state reaction. The obtained fine particles after the milling process in n-hexane helped to improve the reaction rate later on, resulting in pure HMS materials. As a consequence, the maximum thermoelectric figure of merit obtained was 0.55 at 850 K, a high value for undoped HMS. Moreover, single crystals of HMS have been prepared using chemical vapor transport with very low yield, but their poor qualities resulted in low resolution in single crystal XRD. HMS-based materials including the ones with different Si/Mn atomic ratios and various dopants, e.g. Ge, Al, Cr, and Mo, have been prepared for the investigation of structural evolution upon heating up from room temperature to high temperature. The average structural formula at room temperature and its temperature dependence were strongly impacted by the phase compositions of the starting materials as well as the nature of dopants. Physical property measurements on the MnSi1.75 compound revealed that a correlation between the thermoelectric properties and the average structural formula of bulk HMS-based materials could be expected

Validering av selvrapportert legemiddelbruk hos deltakere med koronarsykdom i Tromsøundersøkelsen

Bakgrunn: Hjerte- og karsykdommer er en fellesbetegnelse for sykdommer som involverer hjerte og blodårer. Det er en ledende årsak til død over hele verden. Flere epidemiologiske studier utføres for å undersøke årsakene til den økte sykdomsbelastningen. Ved å studere befolkningens mønster av legemiddelbruk og videre korrigere for feilbruk, kan vi redusere risikoen for nye hendelser og progresjon av etablerte sykdommer. For å danne pålitelige konklusjoner av slike studier, må validering av informasjonskildene utføres. I studier som undersøker legemiddelbruk, blir ofte selvrapporterte data validert mot mer objektive gullstandarder. Formål: Å validere selvrapportert bruk av kardiovaskulære legemidler hos deltakere med koronarsykdom i den syvende Tromsøundersøkelsen. Metode: Dette er en tversnittstudie basert på data fra Tromsø 7, utført i 2015-16. Studiepopulasjonen består av deltakere med selvrapportert koronarsykdom (N = 1483). I undersøkelsen ble forhåndsdefinerte svaralternativer først sammenlignet med legemidler oppgitt i fritekst. Data fra undersøkelsen ble koblet opp mot opplysninger om uthentede resepter fra Reseptregisteret. Hovedanalysen ble utført for et 6 måneders tidsvindu for uthenting av legemidler før deltakelse i Tromsøundersøkelsen, med en sensitivitetsanalyse for 3 og 12 måneder. Relabiliteten mellom informasjonskildene ble beregnet ved hjelp av Cohens kappa (\kappa), mens validitet ble målt ved hjelp av sensitivitet og spesifisitet. Indeksene ppos og pneg ble beregnet for å kontrollere for kappa-paradokser. En binær logistisk regresjon ble utført for å studere assosiasjonen mellom ulike kovariater og uoverensstemmelse mellom de to informasjonskildene for legemiddelbruk. Resultater: Overensstemmelsen mellom forhåndsdefinerte svaralternativer og legemidler listet opp i fritekst var moderat for alle legemiddelklassene. Overensstemmelsen mellom selvrapportert data og opplysninger fra Reseptregisteret varierte fra moderat til nesten perfekt overensstemmelse. En lavere utdanning, å bo uten ektefelle eller samboer, høyere alder og lavere BMI var signifikant assosiert med høyere uoverensstemmelser mellom selvrapportert data og Reseptregisteret. Konklusjon: Validiteten av selvrapportert data varierte mellom de ulike kardiovaskulære legemidlene, men var generelt god når sammenholdt med Reseptregisteret.

Top-down sustainability transitions in action: How do incumbent actors drive electric mobility diffusion in China, Japan, and California?

In explaining how socio-technical transitions occur, prevailing theories focus on bottom-up processes driven by new entrants, diverse actors and open-ended exploration in small, protected niches. Incumbent firms are frequently portrayed as hampering change, while managerial strategies using traditional public policy instruments remain understudied. Addressing this bias, we examine strategies used by networks of incumbent state and industry actors in China, Japan and California to accelerate the production and diffusion of battery-electric or hydrogen-powered vehicles. We build a comprehensive framework that systematically marries mechanisms of industrial transformation described in developmental-state literature with theories of socio-technical change from transitions scholarship. We then use a vast dataset of secondary documents and interviews to examine the principal strategies employed in each country, identifying variations over two phases of technological diffusion. Findings reveal that the incumbent actor networks in each country have collectively employed multiple but similar strategies. Yet closer inspection of specific policy instruments, such as regulations and performance-based incentives, along with ambitions to phase out vehicles with internal combustion engines, reveals differences across cases. We explain these by considering different motivations for each country’s transition and influencing socio-political conditions. Our study contributes to the enrichment of future transitions research in at least two ways. Theoretically, by integrating literature on transitions and developmental states, we deepen understanding of how incumbent state and market actors can attempt to drive socio-technical change. Empirically, our analysis provides important evidence for understanding the strategies driving top-down transitions outside northern Europe, and the conditions affecting instrument choice

M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector

Deep-learning-based object detection methods show promise for improving screening mammography, but high rates of false positives can hinder their effectiveness in clinical practice. To reduce false positives, we identify three challenges: (1) unlike natural images, a malignant mammogram typically contains only one malignant finding; (2) mammography exams contain two views of each breast, and both views ought to be considered to make a correct assessment; (3) most mammograms are negative and do not contain any findings. In this work, we tackle the three aforementioned challenges by: (1) leveraging Sparse R-CNN and showing that sparse detectors are more appropriate than dense detectors for mammography; (2) including a multi-view cross-attention module to synthesize information from different views; (3) incorporating multi-instance learning (MIL) to train with unannotated images and perform breast-level classification. The resulting model, M&M, is a Multi-view and Multi-instance learning system that can both localize malignant findings and provide breast-level predictions. We validate M&M's detection and classification performance using five mammography datasets. In addition, we demonstrate the effectiveness of each proposed component through comprehensive ablation studies.Comment: MICCAI 2023 with supplementary material

Problems and shortcuts in deep learning for screening mammography

This work reveals undiscovered challenges in the performance and generalizability of deep learning models. We (1) identify spurious shortcuts and evaluation issues that can inflate performance and (2) propose training and analysis methods to address them. We trained an AI model to classify cancer on a retrospective dataset of 120,112 US exams (3,467 cancers) acquired from 2008 to 2017 and 16,693 UK exams (5,655 cancers) acquired from 2011 to 2015. We evaluated on a screening mammography test set of 11,593 US exams (102 cancers; 7,594 women; age 57.1 \pm 11.0) and 1,880 UK exams (590 cancers; 1,745 women; age 63.3 \pm 7.2). A model trained on images of only view markers (no breast) achieved a 0.691 AUC. The original model trained on both datasets achieved a 0.945 AUC on the combined US+UK dataset but paradoxically only 0.838 and 0.892 on the US and UK datasets, respectively. Sampling cancers equally from both datasets during training mitigated this shortcut. A similar AUC paradox (0.903) occurred when evaluating diagnostic exams vs screening exams (0.862 vs 0.861, respectively). Removing diagnostic exams during training alleviated this bias. Finally, the model did not exhibit the AUC paradox over scanner models but still exhibited a bias toward Selenia Dimension (SD) over Hologic Selenia (HS) exams. Analysis showed that this AUC paradox occurred when a dataset attribute had values with a higher cancer prevalence (dataset bias) and the model consequently assigned a higher probability to these attribute values (model bias). Stratification and balancing cancer prevalence can mitigate shortcuts during evaluation. Dataset and model bias can introduce shortcuts and the AUC paradox, potentially pervasive issues within the healthcare AI space. Our methods can verify and mitigate shortcuts while providing a clear understanding of performance

Genetic diversity analysis of black pepper (Piper spp.) with RAPD markers

Black pepper is a well-known export commodity in Vietnam, but pepper production has been decreasing in recent years. The lack of knowledge about the origin and genetic characteristics of pepper varieties may create variety degradation and loss of product quality. Therefore, it is necessary to study the genetic diversity of existing local and imported pepper varieties and effectively propagate and create new varieties with high yields and quality. In this study, RAPD markers were used with 100 RAPD UBC primers to study genetic diversity. Twelve RAPD primers were selected to amplify 39 pepper cultivars, and 40 polymorphic DNA bands were created with sizes ranging from 200 to 1400 bp. Five of the 12 primers amplified all 39 cultivars. The genetic diversity of lines/cultivars in the pepper population is relatively high. The phylogenetic tree of the 39 cultivars has two branches showing similarity ranging from 41.8 to 51%. The first branch includes five pepper individuals, and the second consists of 34 individuals. There is a high diversity among the pepper cultivars in the same population

GC-MS analysis and cytotoxic activity of the n-hexane fraction from Curcuma sahuynhensis Škornick. & N.S.Lý leaves collected in Vietnam

Curcuma sahuynhensis Škornick. & N.S.Lý is an endemic plant in Vietnam that has been used by the Sa Huynh people as a spice and medicine to cure illnesses linked to digestive disorders. Very little information is available so far about the chemical composition and biological effects of C. sahuynhensis. To find new pharmaceutical ingredients, the in vitro cytotoxic effect and the chemical profile of C. sahuynhensis leaf extract were investigated. In this study, the percolation method and liquid-liquid dispersion technique were used to extract dry sample powder. The chemical composition was detected by gas chromatography-mass spectrometry (GC-MS). The Sulforhodamine B and MTT methods were used to determine the cytotoxic activity. The chemical composition analysis showed that the leaf extract contained 14 components. The major components in the n-hexane extract were 6,10,14-trimethylpentadecan-2-one, phytol, 1-ethylbutyl hydroperoxide, isoborneol, 1-methylpentyl hydroperoxide, and neophytadiene. On human cancer cell lines, namely MFC-7, SK-LU-1, Hela, MKN-7, and HL-60, the leaf extract showed dose-dependent cytotoxic activity, with IC50 values ranging from 221.70±10.24 to 369.42±10.60 ?g/mL. The present study provides significant information on the chemical components and cytotoxic effects of the n-hexane extract from C. sahuynhensis leaves. The findings will continue to be crucial in future research on the evaluation of secondary metabolite compound analysis for cancer therapeutic effects