High yield synthesis of graphene quantum dots from biomass waste as a highly selective probe for Fe3+ sensing

Graphene quantum dots (GQDs), a novel type of zero-dimensional fluorescent materials, have gained considerable attention owing to their unique optical properties, size and quantum confinement. However, their high cost and low yield remain open challenges for practical applications. In this work, a low cost, green and renewable biomass resource is utilised for the high yield synthesis of GQDs via microwave treatment. The synthesis approach involves oxidative cutting of short range ordered carbon derived from pyrolysis of biomass waste. The GQDs are successfully synthesised with a high yield of over 84%, the highest value reported to date for biomass derived GQDs. As prepared GQDs are highly hydrophilic and exhibit unique excitation independent photoluminescence emission, attributed to their single-emission fluorescence centre. As prepared GQDs are further modified by simple hydrothermal treatment and exhibit pronounced optical properties with a high quantum yield of 0.23. These modified GQDs are used for the highly selective and sensitive sensing of ferric ions (Fe3+). A sensitive sensor is prepared for the selective detection of Fe3+ ions with a detection limit of as low as 2.5 × 10–6 M. The utilisation of renewable resource along with facile microwave treatment paves the way to sustainable, high yield and cost-effective synthesis of GQDs for practical applications

SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL

Artificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models. These models have revolutionized various fields, benefiting society in numerous ways, from self-driving cars and intelligent chatbots to automated facial authentication systems. However, in recent years, machine learning models have been the target of various attack methods. One common and dangerous attack method is adversarial attack, where modified input images can cause misclassification or erroneous predictions by the models. To confront that challenge, we present a novel approach called adversarial retraining that uses adversarial examples to train machine learning and deep learning models. This technique aims to enhance the robustness and performance of these models by subjecting them to adversarial scenarios during the training process. In this paper, we survey detection methods and propose a method to detect adversarial examples using YOLOv7, a commonly used intensive research model. By training adversarial retraining and conducting experiments, we show that the proposed method is an effective solution for helping deep learning models detect certain cases of adversarial examples

Numerical and Experimental Studies on Dynamic Load Testing of Open-ended Pipe Piles and its Applications

13301甲第3962号博士(工学)金沢大学博士論文要旨Abstrac

Numerical and Experimental Studies on Dynamic Load Testing of Open-ended Pipe Piles and its Applications

13301甲第3962号博士(工学)金沢大学博士論文本文ful

Factors affecting the adoption of climate-smart aquaculture (CSAq) in the North Central Coast of Vietnam

Climate-smart aquaculture (CSAq) is considered an appropriate and effective adaptation approach for the coastal aquaculture sector under the climate change phenomenon. This study, applying probit model, aims to assess the influence of several factors on the farmers’ decision to apply CSAq practices in extensive coastal shrimp farming. Data were collected from interviews with 200 households who have both already applied and have yet to apply CSAq practices in five coastal districts of Thanh Hoa Province. The results showed six key factors that influenced the decision of the farmers to apply CSAq practices: availability of household labor; access to information on CSAq practices; market price of products applying CSAq practices; economic efficiency; ability to ensure food security; and improved pond environment when applying CSAq practices. These factors explained 69.41% of their decision to apply CSAq, among which economic efficiency had the greatest impact (30.2%). Market prices and access to information about CSAq are also important factors with respective levels of influence at 16.0% and 14.9%. The result implies that strengthening access to CSAq information and improving technical understanding of CSAq practices are important solutions to upscale CSAq in the North Central Coast of Vietnam

Dataset of Vietnamese students’ intention in respect of study abroad before and during COVID-19 pandemic

The Covid-19 Pandemic had completely disrupted the worldwide educational system. Many schools chose the online delivery mode for students in case learning losses incurred during social distance decree. However, as to these students who are currently in the study abroad planning stages, reached an intention crossroads, whether standing for certain unchanging decisions in study abroad destinations or changing swiftly due to the unexpected policies in quarantine. This case opened to interpretation, which was based on our e-survey since 3 May to 13 May 2020 with 397 responses covering a range of Vietnamese students. In this dataset, we focused on (i) Students’ Demographics; (ii) The previous intention of students to study abroad before and during the Covid-19 ravaged and (iii) Their intention afterwards

Purification and characterization of novel fibrinolytic proteases as potential antithrombotic agents from earthworm Perionyx excavatus

Six protease fractions, namely FI, FII, FIII-1, FIII-2, FIII-3 and FIV, were isolated from Perionyx excavatus earthworm biomass by acetone precipitation, followed by serial chromatography using anion exchange, hydrophobic interaction and size exclusion chromatography. All fractions exhibited strong hydrolytic activity towards casein. The activity of six fractions towards fibrin, determined by fibrin plate assay, ranged from 44 to 831 plasmin unit.mg-1 and ranked as FIII-3 > FIII-2 > FI > FIII-1 > FIV > FII. Casein degradation was optimal at pH 7 and 11, and at 45-60°C. All fractions were considerably stable at high temperature and wide pH range. They were completely inhibited by phenylmethylsulfonyl fluoride (PMSF). The molecular weights (MW) and isoelectric points (pI) determined by 2D-electrophoresis were 27.5-34.5 kDa, and 4.3-5.2, respectively. Tandem mass spectrometry (MS) analysis was used to deduce the amino acid sequences of some peptides from FIII-1 and FIII-2. The sequences shared 16.9% and 13.2% similarity, respectively, with the fibrinolytic enzymes from two related earthworm species, Lumbricus rubellus and Eisenia fetida. The P. excavatus proteases were classified as serine proteases. They could perform rapid hydrolysis on both coagulated fibrous fibrin and soluble fibrinogen monomers without the presence of activators such as tPA or urokinase