177 research outputs found
sj-docx-1-npx-10.1177_1934578X231168481 - Supplemental material for Volatile Components and Biological Activities of <i>n</i>-Hexane Extract From Rhizomes of <i>Homalomena cochinchinensis</i>
No full textSupplemental material, sj-docx-1-npx-10.1177_1934578X231168481 for Volatile Components and Biological Activities of n-Hexane Extract From Rhizomes of Homalomena cochinchinensis by Linh Thuy Khanh Nguyen, Phu Quynh Dinh Nguyen, Nghia Ai Thi Doan, Chau Bao Hoai Nguyen, Tuan Quoc Doan, Linh Thuy Thi Tran, Hoai Thi Nguyen and Duc Viet Ho in Natural Product Communications</p
Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning
No full textThe world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant
Amynthas polychaetiferus
No full textAmynthas polychaetiferus (Thai, 1984) Pheretima polychaetifera Thai, 1984: 1317, fig. 2 A; Nakamura 1999: 53; Thai 2000a: 310; Huynh 2005a: 96; Nguyen 2013: 54. Pheretima sp. — Thai et al., 2007: 313. Pheretima sp.1 — Nguyen T.T. & Tran, 2008: 61; Nguyen et al. 2010: 114; Nguyen & Huynh 2011: 1018. Amynthas polychaetiferus— Blakemore et al. 2007: 29. Pheretima paraalexandri Nguyen, 2011a: 153, Figs 2, 4, syn. nov. Type locality. Viet Nam (Long An: Tan An City). Type material. ZMUM (W.210), Russia. Examined material. 6 C (CTU-EW.008.06) Quoi An, Vung Liem Distr., Vinh Long Prov., 19/10/2008, coll. Nguyen Thi Nhi; 17 C and 4 A (CTU-EW.008.04) Xuan Thinh, Trang Bom Distr., Dong Nai Prov., 16/10/2013, coll. Le Van Nhan; 2 C (SORC-V.083.01) near ricefield, Duc Binh, Tanh Linh Distr., Binh Thuan Prov., 4/4/1995, coll. Huynh Thi Kim Hoi. Records from Vietnam. Dong Nai (Trang Bom); Binh Thuan (Duc Binh); Hau Giang (Phung Hiep); Long An (Thanh Hoa; Tan Thanh, Tan An City); Ben Tre (Binh Đai; Thanh Phu; Cho Lach); Vinh Long (Binh Minh; Mang Thit; Vung Liem; Long Ho); Tien Giang (Chau Thanh; Cai Lay; Cai Be; My Tho city); Can Tho (Cai Rang; Binh Thuy; Co Do; Phong Dien; Thoi Lai); Soc Trang (Ke Sach) (Thai 1984; Huynh 2005; Thai et al. 2007; Nguyen T.T. & Tran 2008; Nguyen et al. 2010; Nguyen & Huynh 2011; Nguyen & Nguyen 2010; Nguyen 2011a). Distribution. Only known from Vietnam. Remarks. Material was re-examined and absence of male copulatory pouches and hence placement of the species in Amynthas is confirmed here.Published as part of Nguyen, Tung T., Nguyen, Anh D., Tran, Binh T. T. & Blakemore, Robert J., 2016, A comprehensive checklist of earthworm species and subspecies from Vietnam (Annelida: Clitellata: Oligochaeta: Almidae, Eudrilidae, Glossoscolecidae, Lumbricidae, Megascolecidae, Moniligastridae, Ocnerodrilidae, Octochaetidae), pp. 1-92 in Zootaxa 4140 (1) on page 44, DOI: 10.11646/zootaxa.4140.1.1, http://zenodo.org/record/25650
Design and fabrication of a Fresnel zone plate with an enhanced depth of focus
No full textA Fresnel zone plate (EFZP) with an extended depth of focus can maintain focused monochromatic light at different distances compared to a general Fresnel zone plate (FZP). The focal distances are determined by dividing the zone plate into multiple areas based on the desired order. The EFZP has potential applications in various research fields such as microscopy, direct laser lithography, and optical coherence tomography. However, manufacturing an EFZP is challenging due to the high precision requirements and difficulties associated with the calculation and simulation processes. In this research, a complete process is presented to design, simulate, and fabricate an EFZP using a Fourier optics design, simulations, and a direct laser lithographic machine. The resulting EFZP has an increased depth of focus of about nine times compared to a general Fresnel zone plate with similar parameters, while maintaining the focal spot diameter. The performance of this EFZP is evaluated through optical verification and mathematical simulation methods. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
Metaphire californica Kinberg 1867
No full textMetaphire californica (Kinberg, 1867) Pheretima californica Kinberg, 1867: 102; Beddard 1895a: 369; Michaelsen, 1900: 258, 275; Chen 1936: 270; Gates, 1972: 174; Do 1994: 62; Le 1995a: 59; Nakamura 1999: 29; Thai 2000a: 308; Thai et al. 2004: 759; Huynh et al. 2005: 180; Nguyen et al. 2012: 146; Nguyen 2013: 48; Nguyen 2014: 109. Pheretima (Amynthas) californica— Thai 1983: 124. Metaphire californica— Sims & Easton 1972: 238; Easton, 1981: 57; Easton, 1982: 731; Blakemore 2002: 195, Figs 2.16–2.17; Blakemore 2007a: 18; Blakemore 2008b. Perichaeta ringeana Michaelsen, 1890a: 10. Perichaeta guarini Rosa, 1894: 13. Pheretima modesta Michaelsen, 1927: 88. See Blakemore (2002, 2007a) for more synonyms. Type locality. Sausolita Bay, California, USA. Type material. Stockholm Museum, Sweden. Examined material. 17 C and 1 A (SORC-V.128.03), A 1 area, Thanh Ninh, Thanh Hoa, 03/10/1997, coll. Thai Tran Bai; 9 C (CTU-EW.005.05), Thanh Phu, Vinh Cuu, Dong Nai, 13/9/2012, coll. Duong Chi Trong. Records from Vietnam. Lao Cai (Pho Lu; Pho Rang: Sa Pa); Yen B ai (Luc Yen); Tuyen Quang (Chiem Hoa); Phu Tho (Phu Tho; Thanh Son; Doan Hung; Xuan Son NP); Vinh Phuc (Me Linh); Thai Nguyen; Bac Can (Cho Don); Cao Bang (Thach An; Trung Khanh; Ha Lang); Lang Son (Binh Gia; Bac Son; Trang Dinh; Dong Dang); Bac Giang (Tan Yen); Dien Bien (Tuan Giao); Son La (Moc Chau; Son La); Hoa Binh (Hoa Binh; Mai Chau; Tan Lac); Hanoi; Ha Nam; Thai Binh; Dong Nai (Vinh Cuu, Long Khanh, Nhon Trach, Long Thanh); Kien Giang (Phu Quoc Isl.; Kien Luong); Tra Vinh (Duyen Hai); Soc Trang (Tran De); Bac Lieu (Bac Lieu Town); An Giang (Tinh Bien) (Le 1995a; Thai et al. 2004; Huynh et al. 2005; Nguyen 2014). Distribution. Cosmopolitan species, found in various parts of the world, e.g. USA (Californica; New York); Mexico; South America; Madeira; Egypt; Myanmar; China; Taiwan; Japan; Australia (Blakemore 2002). Vietnamese name. Giun californi. Remarks. The species possibly originates from the Oriental region (Blakemore 2002). Chen (1936) noted that the species may be a senior synonym of Metaphire hesperidum (Beddard, 189 2) following Gates's (1935) examination.Published as part of Nguyen, Tung T., Nguyen, Anh D., Tran, Binh T. T. & Blakemore, Robert J., 2016, A comprehensive checklist of earthworm species and subspecies from Vietnam (Annelida: Clitellata: Oligochaeta: Almidae, Eudrilidae, Glossoscolecidae, Lumbricidae, Megascolecidae, Moniligastridae, Ocnerodrilidae, Octochaetidae), pp. 1-92 in Zootaxa 4140 (1) on pages 52-53, DOI: 10.11646/zootaxa.4140.1.1, http://zenodo.org/record/25650
Hydrodynamic modelling of microplastic transport - A case study on microplastics from traffic emissions in the Göta River
No full textFrom graphene science to graphene technology: a bibliometric investigation
No full textTo get an advantage in the scientific and technological competitions between nations, it is necessary to fully understand the value chain of four stages: from pure science to applied science to technology, and finally to commercialization along with product development. However, given heavy investments from both public and private industries being poured into scientific research nowadays, it remains difficult to have comprehensive insights into these innovative conversions, in particular which conditions are required to be able to move from one stage to another. To examine how a theoretical scientific idea can become a commercial product, we chose graphene as the case study due to its attractive structures and properties, which resulted in both academic and industrial potential. Our first contribution is an evaluation methodology on whether graphene science was developing on track with graphene technology, and therefore successfully delivered its application promises. In both aggregate and temporal analyses, we found bibliographic evidence between 2004 and 2017 suggesting the ’Golden Eras’ periods in graphene science and technology in the past despite their exponential growth in publications over time. By using a simulation-based method to calculate the temporal interest level in a particular field, we confirmed these observations that the interest levels in graphene science and technology had already peaked in 2010 and 2012 respectively.
Next, we focused on graphene science and proposed a hypothesis of innovation where new research streams (child fields) are likely to incubate and emerge from more established research streams (parent fields). In the dataset, we applied a co-clustering method to the linguistic information of graphene articles to determine four graphene scientific topics – theory and experimental tests, synthesis and functionalization, sensors, and supercapacitors and electrocatalysts. From the publication proportions and levels of interest, we found their order of emergences to follow an expected sequence from pure science(s) to applied science(s). To validate this stream-based model of innovation, we tested nanotubes and batteries to be the potential parent streams for the four topics. Our findings showed strong incubation signatures of all four topics in nanotubes, and a much weaker one of supercapacitors and electrocatalysts in batteries. Moreover, we confirmed the impacts of the 2004 graphene breakthrough in nanotubes and batteries. Here, the framework on the parent-child relationship between two fields as well as their interactions is our second contribution to the study of science to technology.
From these four topics, we further assembled the theoretical (T) and applied (A) branches in graphene science by evaluating which identified topics are T/A oriented. Following this, we aimed to contribute to the quantification of the interplays between pure research and applied research in both temporal and geographical aspects. Our citation-based method incorporating both direct and indirect cited journal papers indicated a universal and asymmetric dependency between T and A: while T mostly depended on T over time, A inherited mainly from T in the early stage before increasingly depending on itself during its mature stage. Our findings not only captured the knowledge evolution in graphene science but also validated the interactions between pure research and applied research on the innovation model. Lastly, we expected to have an equal contribution to the study of graphene technology besides graphene science by focusing on the regional competition in graphene technology as well as the strategic differences in patent portfolio management. In this study, we identified seven graphene technology areas and compared whether the technology evolution sequence is in parallel with the science evolution sequence. Moreover, by classifying entities into different groups based on their assignee categories (universities, corporations, and others) as well as their accumulated number of patents (three quartile groups), we found significant differences in patenting behaviors between universities and corporations and also between large entities and small entities. While large entities prefer expanding their patent portfolios diversely over multiple technology areas, small entities cannot afford this strategy and have to specialize in a smaller number around their core technologies instead. On the other hand, we proposed and validated a hypothesis on the differences in patenting activities between universities and corporations not only from aggregate analysis but also from studying three representative case studies.
Overall, our contributions in the study of graphene science and graphene technology offer a better understanding of evaluation of progress in science and technology of a given field, in which the ‘golden eras’ might have been in the past despite continuing funding and publication outcomes. Moreover, we confirmed the interactions between the pure science stage and applied science stage in the model of innovation and validated the hypothesis on the emergence of a new field from other mature fields. Finally, our analysis of bibliographic records of patents clearly shows distinguished characteristics in technology portfolio management between universities and corporations, among leading regions, and among entities of different sizes.Doctor of Philosoph
Artificial intelligence (AI) for good? Enabling organizational change towards sustainability
No full textArtificial intelligence (AI) is increasingly being recognized as a critical tool when it comes to addressing the most pressing challenges facing modern industries, including the pursuit of sustainability. The use of AI is aiding businesses in navigating corporate sustainability challenges, but existing research lacks a comprehensive exploration of how corporations leverage AI to boost their sustainability. By exploiting an inductive concept-development approach and incorporating data from 24 companies, this study provides valuable insights into the role that AI plays in shaping organizational sustainability strategies, identifying operational enablement and technical capacity as key drivers of AI adoption for corporate sustainability. These drivers are incorporated into the technology, organization, and environment (TOE) framework alongside the strategic steps and capabilities necessary for organizations to effectively adopt and implement AI in the development of their sustainability strategies. Ultimately, this study proposes an integrative model for sustainability-oriented AI adoption that emphasizes the importance of aligning AI initiatives with organizations' sustainability objectives in order to maintain a competitive advantage and drive progress. Correspondingly, it underscores the need for robust data management, system integration, and continual performance monitoring to reduce resistance to AI adoption allowing for the potential of AI to be fully harnessed in pursuit of sustainability. Furthermore, this study offers practical guidance by exploring the direct and indirect use cases of AI in corporate sustainability. The study concludes by highlighting potential avenues for future research in this evolving field
Novel contract-based runtime explainability framework for end-to-end ensemble machine learning serving
No full textPublisher Copyright: © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.The growing complexity of end-to-end Machine Learning (ML) serving across the edge-cloud continuum has raised the necessity for runtime explainability to support service optimizations, transparency, and trustworthiness. That involves many challenges in managing ML service quality and engineering runtime explainability based on ML service contracts. Currently, consumers use ML services almost as a black box with insufficient explainability for not only inference decisions but also other contractual aspects, such as data/service quality and costs. The generic explainability for ML models is inadequate to explain the runtime ML usage for individual consumers. Moreover, ML-specific metrics have not been addressed in existing service contracts. In this work, we introduce a novel contract-based runtime explainability framework for end-to-end ensemble ML serving. The framework provides a comprehensive engineering toolset, including explainability constraints in ML contracts, report schemas, and interactions between ML consumers and the components of the ML serving for evaluating service quality with contract-based explanations. We develop new monitoring probes to measure ML-specific metrics on data quality, inference confidence, inference accuracy, and capture runtime ML usage. Finally, we present essential quality analyses via an observation agent. That interprets ML inferences and evaluates contributions of ML inference microservices, assisting ML serving optimization. The agent also integrates ML algorithms for detecting relations among metrics, supporting constraint developments. We demonstrate our work with two real-world applications for malware and object detection.Peer reviewe
Novel Contract-based Runtime Explainability Framework for End-to-End Ensemble Machine Learning Serving
No full textThe growing complexity of end-to-end Machine Learning (ML) serving across the edge-cloud continuum has raised the necessity for runtime explainability to support service optimizations, transparency, and trustworthiness. That involves many challenges in managing ML service quality and engineering runtime explainability based on ML service contracts. Currently, consumers use ML services almost as a black box with insufficient explainability for not only inference decisions but also other contractual aspects, such as data/service quality and costs. The generic explainability for ML models is inadequate to explain the runtime ML usage for individual consumers. Moreover, ML-specific metrics have not been addressed in existing service contracts. In this work, we introduce a novel contract-based runtime explainability framework for end-to-end ensemble ML serving. The framework provides a comprehensive engineering toolset, including explainability constraints in ML contracts, report schemas, and interactions between ML consumers and the components of the ML serving for evaluating service quality with contract-based explanations. We develop new monitoring probes to measure ML-specific metrics on data quality, inference confidence, inference accuracy, and capture runtime ML usage. Finally, we present essential quality analyses via an observation agent. That interprets ML inferences and evaluates contributions of ML inference microservices, assisting ML serving optimization. The agent also integrates ML algorithms for detecting relations among metrics, supporting constraint developments. We demonstrate our work with two real-world applications for malware and object detection.Peer reviewe
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